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This is to certify that the
thesis entitled

APPLICATION OF A SCIENCE-BASED, MULTI-SCALED
APPROACH TO WATERSHED PROTECTION AND
REHABILITATION IN THE RIFLE RIVER WATERSHED,
MICHIGAN

presented by

ANDREA BARBARA ANIA

has been accepted towards fulfillment
of the requirements for the

MS. degree in Fisheries and Wildlife

 

 

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APPLICATION OF A SCIENCE-BASED, MULTl-SCALED APPROACH TO
WATERSHED PROTECTION AND REHABILITATION IN THE RIFLE RIVER
WATERSHED, MICHIGAN

By

Andrea Barbara Ania

A THESIS
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
MASTER OF SCIENCE
Department of Fisheries and Wildlife

2007

 

ABSTRACT
APPLICATION OF A SCIENCE-BASED, MULTl-SCALED APPROACH TO
WATERSHED PROTECTION AND REHABILITATION IN THE RIFLE RIVER
WATERSHED, MICHIGAN
By

Andrea Barbara Ania

Currently, there are many different watershed analysis and planning
procedures being used across the nation to address watershed health and halt
the decline of aquatic species. The primary goal of this project was to apply a
science-based, landscape-level approach to watershed protection and
rehabilitation utilizing an existing watershed analysis and planning procedure. I
used Ecosystem Analysis at the Watershed Scale: The Federal Guide for
Watershed Analysis (EAWS) to describe hydrologic and land use trends,
determine the current stream temperature regime, predict the impacts of global
warming on stream temperature, and qualitatively assess stream Channel
morphology in the Rifle River watershed, Michigan. The results of EAWS
analysis were used to recommend areas within the Rifle River watershed to
protect and enhance fish habitat. Results suggest base-flow has increased over
time and there has been an increase in developed land, grassland, and shrub
land. Temperatures upstream of river kilometer 33 are satisfactory for salmonids
under current and predicted global warming conditions; temperature does not
appear to be a limiting factor downstream where warmer temperatures occur.
Problems (e.g., erosion, silt) in the mainstream and tributaries were identified

along with their potential causes (e.g., Channelization, culverts).

 

ACKNOWLEDGEMENTS

I would like to thank my advisor, Dr. William W. Taylor, for the opportunity
and freedom to pursue my research interests. Additionally, I appreciate the
support and counsel he provided. I would equally like to thank my co-advisor Dr.
Tammy J. Newcomb for her technical guidance and counsel throughout this
project. Her willingness to share her knowledge of riverine processes and
interactions greatly enhanced my understanding in this field. I would also like to
acknowledge the contributions of Dr. Kelly Millenbah of my advisory committee
for providing me with GIS assistance and feedback. I would also like to thank Dr.
Richard Merritt of my advisory committee for sharing his fervor and knowledge of
aquatic insects. Dr. Dana lnfante provided guidance on hydrologic analysis,
which I greatly appreciated.

I wish to thank my co-workers at the US. Fish and Wildlife Service Alpena
Fisheries Resource Office, Michigan, particularly Heather Rawlings and Susan
Wells, for sharing their knowledge and providing support during this process. I
would also like to thank Gerry Jackson of the US. Fish and Wildlife Service in
Minneapolis, Minnesota, for allowing me with the opportunity to undertake this
project and employ me during the summer months. Additionally, I appreciate the
support and assistance of other US. Fish and Wildlife Service personnel, such
as Mike Oetker and Craig Czarnecki.

This project received funding from the Department of Fisheries and

Wildlife at Michigan State University, US. Fish and Wildlife Service (Region 3),

 

and the Kalamazoo Valley Chapter of Trout Unlimited. The Michigan Department
of Natural Resources Lake Huron District and Jim Hergott of the Saginaw Bay
Resource and Conservation District provided data. Field collected could not
have been accomplished without the help of Pete Datema. I am grateful for the
support Of friends and family whose encouragement and understanding helped
me through the process. Finally, I am indebted to my husband Eric for moving

back to the Midwest, adjusting to our new life, and enduring all of its Challenges.

 

TABLE OF CONTENTS

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

LIST OF TABLES vi
LIST OF FIGURES viii
INTRODUCTION 1
Restoration and Rehabilitation ~ 4
Physical Processes 5
National Fisheries Habitat Initiative 10
Study objectives 13
Protocol Selection 14
Michigan Department of Environmental Quality .................... 16
Michigan Department of Natural Resources ......................... 17
Ecosystem Analysis at the Watershed Scale ........................ 18
APPLICATION OF ECOSYTEM ANALYSIS .................................... 20
Study Site 23
Methods 28

GIS Land Cover Analysis 28

Hydrologic Analysis 33

Water Temperature Model 35

Qualitative Habitat Assessment 43

Results 46

GIS Land Cover Analysis 46

Hydrologic Analysis 50

Water Temperature Model 51

Qualitative Habitat ." uent 54

Discussion 57

GIS Land Cover Analysis 57

Hydrologic Analysis 60

Water Temperature Model 63

Qualitative Habitat Assessment 65

Conclusion 67
Evaluation of the Ecosystem Analysis ................................... 67
APPENDICES 133
LITERATURE CITED 138

 

 

LIST OF TABLES

Table 1. Comparison of watershed analysis and planning
frameworks. Michigan Department Of Environmental Quality
(MDEQ), the Michigan Department of Natural Resources (MDNR),
and Ecosystem Analysis at the Watershed Scale (EAWS). ................ 75

Table 2. Rifle River Watershed fish community. Information based
on MDNR fish surveys for the lower Rifle River (Omer), and
Michigan’s Wildlife Action Plan (Eagle et al. 2005) .............................. 76

Table 3. Federal and state listed threatened, endangered, proposed,
and candidate Species in the Rifle River Watershed ID 4080101 30
1-18 (Michigan Natural Features Inventory 2006) ............................... 77

Table 4. Side-by-side comparison of data layers used in land cover
analysis. Michigan Natural Feature’s Inventory (MNFl) Circa 1800
data and US. Geological Survey’s National Land Cover Dataset
(NLCD) that were used in this study 78

 

Table 5. Land cover Classes present in Circa 1800, 1992, and 2001
datasets for the Rifle River watershed and simplified land cover
Classification schemes used in land cover analysis ............................. 79

Table 6. Summary of hydrologic parameters used in Indicators of
Hydrologic Alteration (IHA) statistics (modified from The Nature
Conservancy 2006) 81

 

Table 7. Summary of hydrologic parameters used in Environmental
Flow Components (EFCs) statistics (modified from The Nature
Conservancy 2006) 82

 

Table 8. Data inputs required to build Stream Network TEMPerature
model

 

Table 9. Results from multiple linear regression, which was used to
estimate missing water temperature values for the mainstream and
tributaries of the Rifle River watershed 84

 

Table 10. Lethal, Optimal, and favorable water temperatures for
brown trout, brook trout, and steelhead based on the literature. Cold
water guild values were used to interpret the biological implications
of SNTEMP model

 

vi

 

Table 11. Factors considered in sub-watershed delineation of the
mainstream 86

 

Table 12. Tributaries the State Of Michigan considers vital to the
protection of the Rifle River and were evaluated during the qualitative
habitat assessment 87

 

Table 13. Mesohabitat data collected at each site during the
qualitative habitat assessment of the Rifle River watershed ............... 88

Table 14. Feature characteristic categories used for qualitative
habitat assessment of the Rifle River watershed ................................. 89

Table 15. Selected habitat variables for sub-watersheds (n=9) and
tributaries (n=3) of the Rifle River 90

 

Table 16. Results of qualitative habitat assessment for sub-
watersheds of the Rifle River 91

 

vii

 

LIST OF FIGURES

Figure 1. Conceptual model of watershed processes, interactions,
and rehabilitation

 

Figure 2. Location and map of the Rifle River watershed in
northeastern-lower Michigan

 

Figure 3. Composite stream network Of Rifle River watershed from
the headwaters to the mouth 94

 

Figure 4. Mean monthly discharge at Melita Road (M—70) for
calendar years 1997, 2000, and the 11 year average (1995-2005) in
the Rifle River Watershed, Michigan 95

 

Figure 5. Residual plot for Rifle River at Melita Road from multiple
linear regression 96

 

Figure 6. Scatterplot of predicted value from multiple linear
regression vs. known values for Rifle River at Melita Road ................. 97

Figure 7. Mean daily water temperature collected by the Michigan
DNR from February 1997 to January 1998 at the upper (Ranch
Bridge, rkm 58.7), middle (State Road, rkm 44.8) and lower (Melita
Road, rkm 0.0) sections of the Rifle River watershed. The preferred
temperature range (15-19°C) is based on the mean temperature
measured at sites where Wehrly et al. (1999) found peak densities of
brook, brown, and rainbow trout. Maximum is the temperature
(206°C) that should not be exceeded for optimal growth and survival
of trout (Dexter and O’Neal 2004) 98

 

Figure 8. Mean daily water temperature collected by the Michigan
DNR from late June 1997 to early June 1998 at for the West Branch
Rifle River, Klacking Creek, Houghton Creek, Gamble Creek, and
the lower section of the mainstream (Melita Road). The preferred
temperature range (15-19°C) is based on the mean temperature
measured at sites where Wehrly et al. (1999) found peak densities of
brook, brown, and rainbow trout. Maximum is the temperature
(206°C) that should not be exceeded for optimal growth and survival
of trout (Dexter and O’Neal 2004) 99

 

Figure 9. ln-stream data collection sites within the Rifle River
watershed 1

 

viii

 

Figure 10. Circa 1800, 1992, and 2001 land use results from GIS
watershed scale analysis for the Rifle River watershed, Michigan. 101

Figure 11. Results from GIS land use analysis within the 90m buffer
for a) 1992 and b) 2001 for the Rifle River watershed, Michigan ...... 102

Figure 12. Results from GIS land use analysis within the 60m buffer
for a) 1992 and b) 2001 for the Rifle River watershed, Michigan ...... 103

Figure 13. Results from GIS land use analysis within the 30m buffer
for a) 1992 and b) 2001 for the Rifle River watershed, Michigan ...... 104

Figure 14. Results from GIS land use analysis for circa 1800 at the
a) watershed level and b) 90m buffer scale for the Rifle River
watershed, Michigan 105

 

Figure 15. Results from GIS analysis of 2001 watershed scale land
use and within 90m, 60m, and 30m buffers for the Rifle River
watershed, Michigan 106

 

Figure 16. GIS Sub-watershed delineation of the Rifle River
watershed, Michigan. Upper, middle, and lower sub-watersheds
comprised 31%, 30%, and 39% of the total watershed area
respectively 107

 

Figure 17. Results from GIS analysis of 2001 land use analysis
within sub-watersheds of the Rifle River watershed .......................... 108

Figure 18. Parametric analysis of monthly flows for August, Rifle
River at Melita Road 1

 

Figure 19. Parametric analysis of monthly flows for November, Rifle
River at Melita Road 110

 

Figure 20. Parametric analysis of large flood frequency, Rifle River
at Melita Road 111

 

Figure 21. Annual 1-day minimum discharge for the Rifle River at
Melita Road 11

 

Figure 22. Annual 3-day minimum discharge for the Rifle River at
Melita Road 11

 

Figure 23. Annual 7-day minimum discharge for the Rifle River at
Melita Road 114

 

Figure 24. Annual 30—day minimum discharge for the Rifle River at
Melita Road 11

 

Figure 25. Annual 90-day minimum discharge for the Rifle River at
Melita Road 11

 

Figure 26. Date of minimum flow for the Rifle River at Melita Road. 117

Figure 27. Number of hydrologic reversals for the Rifle River at
Melita Road 118

Figure 28. Frequency of extreme low flows for the Rifle River at
Melita Road 119

Figure 29. Mean annual discharge for the Rifle River at Melita Road
120

 

Figure 30. SNTEMP daily calibration predictions and measured
temperature (°C) values from 1997 at Melita Road (0.1rkm; figure a).
SNTEMP daily validation predictions and measured temperatures
(°C) values from 2000 at Melita Road (0.1rkm) located in the Rifle
River watershed, Michigan (figure b) 121

 

Figure 31. SNTEMP model’s predictive performance for calibration
(1997) and validation (2000) datasets based on the percentage of
time that predictions exceeded measured temperatures: <1°C =
optimal, <2°C = acceptable, 2-4°C = marginal, and >4°C =
unacceptable (figure 3). Plot of model error (predicted-measured)
against measured water temperatures for 1997 calibration dataset to
assess simulation quality (figure b) 122

 

Figure 32. Mean weekly temperatures for the Rifle River watershed
as predicted by the SNTEMP model from the headwaters to Melita
Road, Sterling, Michigan. Model simulation conditions represent
winter and spring temperatures. The shaded area represents
optimal growth temperatures for salmonids (Werhly et al. 1999) 123

Figure 33. Mean weekly temperatures for the Rifle River watershed
as predicted by the SNTEMP model from the headwaters to Melita
Road, Sterling, Michigan. Model simulation conditions represent
summer and fall temperatures. The shaded area represents optimal
growth temperatures for salmonids (Werhly et al. 1999) ................. 124

Figure 34. Temperature duration curve for the upper (rkm 39),
middle (rkm 13), and lower (rkm 1) sections of the Rifle River
watershed, Michigan 125

 

 

Figure 35. Mean weekly temperatures for the Rifle River watershed
as predicted by the SNTEMP model from the headwaters to Melita
Road, Sterling, Michigan. Model simulation conditions represent a
global increase of 27°C in mean annual air temperature. The
shaded area represents optimal growth temperatures for salmonids
(Werhly etal. 1999) 126

 

Figure 36. Mean weekly temperatures for the Rifle River watershed
as predicted by the SNTEMP model from the headwaters to Melita
Road, Sterling, Michigan. Model simulation conditions represent a
global increase in air temperature of 27°C. The shaded area
represents optimal growth temperatures for salmonids (Werhly et al.
1999) 1

 

Figure 37. Mesohabitat characteristics based on in-stream habitat
assessment of the Rifle River for 3) segments of the mainstream
Rifle River and b) tributaries to the Rifle River ................................... 128

Figure 38. Site quality (percent streambank erosion) based on in-
stream habitat assessment of the Rifle River for a) segments of the
mainstream Rifle River and b) tributaries to the Rifle River ............... 129

Figure 39. Stream canopy results based on in-stream habitat
assessment of the Rifle River for a) segments of the mainstream
Rifle River and b) tributaries to the Rifle River ................................... 130

Figure 40. Substrate results based on in-stream habitat assessment
of the Rifle River for 3) segments of the mainstream Rifle River and
b) tributaries to the Rifle River 1

 

Figure 41. Rehabilitation sites (signified by dots) completed from
1998-2005 throughout the Rifle River watershed .............................. 132

Images in this thesis are presented in color.

xi

 

INTRODUCTION

Healthy watersheds play a beneficial role in supporting human, aquatic,
and terrestrial life. Some of these benefits include the availability of clean water
for human consumption, recreational Opportunities, quality of life, and maintaining
viable fish and wildlife populations. Fresh water comprises less than 1% of the
global water supply (Fetter 2001); therefore, it is imperative that theSe finite fresh
water resources remain suitable for human consumption and sustaining diverse,
resilient aquatic ecosystems.

Watersheds are the interface where physical, biological, and chemical
processes interact with each other and with human economic, recreational, and
cultural activities. Watersheds, also called catchments or basins, are geographic
areas where surface and groundwater flow drains into a common outlet such as
river, stream or other surface channel (Armantrout 1998). Watersheds can vary
in size from individual to multiple river basins and include all the land that drains
into the river system. Due to the terrestrial linkages of the aquatic components of
watersheds, land-use practices (e.g., urbanization, roads, agriculture)
significantly contribute to the physical and biological degradation of aquatic
ecosystems.

A healthy watershed is one that supports the native biotic community and
exists in what is referred to as a dynamic equilibrium. In the state of dynamic
equilibrium, the system is capable of recovering from perturbations and can
maintain native aquatic community structure and diversity (Heede 1986).

Physical and Chemical watershed processes create the available habitat for fish

J;

 

and aquatic organisms, which ultimately determines the diversity of species a
watershed can support. Characteristics such as water temperature, food
availability, in-stream cover, spawning substrate, and chemical properties such
as dissolved oxygen and nutrients collectively determine the type and diversity of
habitat accessible to fish. Human land use choices such as removal of
streamside vegetation can alter these characteristics, resulting in a Shift in the
abundance and type of aquatic organisms a watershed can support, which is
also referred to as the loss of aquatic habitat.

Loss of aquatic habitat due to land use/land cover (LULC) Change is
becoming a global issue (Lambin et al. 2001). As the world’s population
continues to expand, natural resources are increasingly exploited to fulfill human
needs at the expense of sustainable and diverse ecosystems (Foley et al. 2005).
The collective land-use effects of urbanization, agriculture, grazing, mining,
logging, damming, and water withdrawals are some of the activities directly and
indirectly impacting our nation’s fish populations through habitat loss,
degradation, and fragmentation (National Fish Habitat Initiative 2006; Williams et
al. 1997).

Both point source pollution (i.e., sewage or industrial waste) and nonpoint
source (NPS) pollution contribute to water quality impairment and the overall
health of a watershed. Nonpoint source pollution occurs when rainwater or.
snowmelt transports pollutants (e.g., sediment, fertilizers, pesticides, oil, grease)
into surface or ground waters. The US. Environmental Protection Agency

(USEPA) estimates NPS pollution comprises 60% of all water pollution (Maine

 

Department of Environmental Protection 1998), making it the nation's primary
source of water quality degradation. Sediment and nutrients are the principal
sources Of NPS pollution in aquatic environments (USEPA 1997).

Agricultural land use is responsible for the majority of NPS pollution in the
United States. Agricultural activities such as cattle grazing and crop production
accelerate erosion due to land-disturbing activities that facilitate the removal and
transport of sediment into lotic (i.e., flowing water) environments (Hairston et al.
2001). Excessive sediment entering a stream can reduce salmonid productivity
by suffocating fish eggs and clogging interstitial spaces that are used by young
fish and invertebrates. After agriculture, urbanization is the second leading
cause of stream impairment. Urban areas negatively impact stream hydrology,
geomorphology, and biotic community richness by making the stream more
flashy, widening the stream channel, and reducing fish and aquatic insect
diversity due to increased runoff from impervious surfaces (e.g., paved roads,
roofs) and nutrient loading from municipal wastewater discharge plants (Paul and
Meyer 2001).

Although agricultural land-use is the major source of NPS pollution in the
United States, environmental degradation has not been Observed until >50% of
upstream land-use is agriculture (Wang et al. 1997). In urban areas, watershed
health (e.g., biotic integrity, habitat quality) begins to deteriorate around 10%
impervious surface and severe degradation is observed in excess of 30%
(McClintock and Cutforth 2003; Wang et al. 1997). Impervious surfaces prevent

rainfall from infiltrating and percolating through the ground, resulting in increase

45%

 

3 A

 

runoff and elevated water temperatures, which can lead to habitat degradation
and loss of aquatic habitat important to fish and wildlife.

Streambank vegetation, also called riparian vegetation, provides many
ecosystem services to fish and wildlife. For example, 30m vegetated buffers
provide benefits such as sediment removal, reduced erosion, and water
temperature stabilization (USDA-USFS 2003; Schultz et al. 1994). Sixty meter
riparian buffers function as effective flood control and 90m buffers provide the
greatest benefit to wildlife (USDA-USFS 2003). Thus, as buffers increase in size
they provide more ecosystem services. Some studies have investigated whether
land use at the watershed scale or the 100m riparian buffer scale is more
reflective of the physical habitat available and thus, aquatic ecosystem health;
however, these studies have produced incongruous results (Richards et al. 1996;
Wang et al. 1997). For example one study found 100m buffers were better
predictors of sediment-related variables while the entire watershed was more
important for maintaining overall stream health (Richards et al. 1996). Since the
influence of riparian buffers varies by width and physical site characteristics (e.g.,
slope, stream order, vegetation type and condition), it is essential to understand
how buffers and whole catchment land use is influencing a watershed (USDA-
USFS 2003).

Restoration and Rehabilitation

Habitat rehabilitation is a necessary tool for improving ecosystem health.

The terms rehabilitation and repair will be used in this paper to describe habitat

manipulation efforts commonly referred to in the literature as stream or aquatic

4 >i‘

habitat “restoration.” Rehabilitation projects begin with a degraded condition
(e.g., streambank erosion) and initiate ecosystem improvement of watershed
processes and function, but preclude the establishment of a historical goal
(Bradshaw 1987). The term restoration will not be used in this paper because
true restoration implies the return of ecosystem processes and function to a
relatively original or indigenous state (Whisenant 1999). Defining an original,
indigenous, or historical state is complicated (e.g., pre-humans, pre-colonial
settlement, pre-logging) and often this information is unavailable, making it
difficult to set and achieve restoration goals. Furthermore, the introduction of
non-native invasive species can impede true restoration because a purely
indigenous ecosystem may no longer be unattainable due to their presence.
Watershed rehabilitation projects are Often focused on improving water quality
and recovering fish habitat.
Physical Processes

Although numerous factors contribute to the biological diversity of river
ecosystems, physical processes create the habitat (or structure) necessary for
aquatic life and determine the range of aquatic organisms a watershed can
support (Gordon et al. 2004). In watershed systems, physical processes include
climate, geology, topography, hydrology, geomorphology, LULC, and
components of water quality (Figure 1). Physical processes interact with each
other and the biological community; thus it is important to understand these

complex interactions to develop effective watershed management plans.

 

 

Physical processes operate on a number of spatiotemporal scales. On a
regional scale, Climate is affected by the topography of a basin and respectively
influences the geology, vegetation, and hydrology of the area. Infiltration and
runoff within the watershed are controlled by the area's geology, topography, and
vegetation (Fetter 2001). On a local level, the presence, abundance, and type of
riparian vegetation (i.e., streambank vegetation) influence in-stream habitat,
biota, channel structure, and organic input.

The hydrologic regime of a watershed is the collective input of water from
precipitation, surface water, and groundwater plus the amount of water leaving
the watershed due to evaporation and transpiration (Armantrout 1998). The
natural flow regime (or hydrology) of a basin is defined by the magnitude,
frequency, duration, predictability, and flashiness (i.e., rapid increase in stream
flow) of a river (Poff et al. 1997). Watershed hydrology “determines the biotic
composition, structure, and function of aquatic, wetland, and riparian
ecosystems;" therefore, understanding the degree that the natural flow regime
has been altered by humans is essential to effective watershed management
planning (Richter et al. 1996, p. 1163). When natural hydrologic processes are
altered, geomorphic processes (e.g., sediment size, channel stability, floodplain
morphology) change correspondingly and this influences the quality of aquatic
insect and fish habitat available (Heede and Rinne 1990; Poff et al. 1997).
Watershed rehabilitation that fails to acknowledge the significance of hydrology,
geomorphology, and land use can produce ineffectual results; for example,

stabilizing eroded streambanks that have resulted from urbanization and

a A

increased runoff fails to address the source of the problem and may transfer the
problems downstream or upstream of the site (Brooks et al. 2003).

Groundwater (water that accumulates underground) discharges into
streams when the water table rises above the streambed and water emerges
through a system of slow seepages or as a Spring. During base-flow conditions
(i.e., drought or low flow), groundwater provides the entire stream flow and is
Characteristic of the geology, topography, soils, and climate of the basin
(Hendrickson and Doonan 1972). Groundwater-dominated rivers provide cooler,
more stable water temperatures and flows, which are important for supporting a
diversity of fauna and maintaining healthy salmonid populations (Sear et al.
1999). Land-use activities that remove riparian vegetation can alter hydrological
processes by disconnecting the floodplain or destroying adjacent wetland habitat.
This can result in diminished groundwater recharge and increased surface runoff,
thus altering groundwater-surface water interactions (e.g., flood attenuation,
lower base-flow, increased water temperature; Winter et al. 1998).

The temperature and chemistry of the water also influences lotic
community structure and composition. Water temperature has a strong influence
on many fish species, ranging from acute lethal consequences to regulating
migration (Bartholow 2000; Gallagher 1999; Bond 1996). Water temperature
plays a vital role in determining fish and aquatic species composition, growth
rates, and Iife-stage(s) a watershed can support (Roni 2005). For example,
coldwater fish like trout, are typically found in waters with mean daily

temperatures below 65°F (206°C; Holton and Johnson 1996). Dissolved

 

oxygen, rates of allochthonous decomposition, and nutrient availability are

functions of water temperature and water chemistry, ultimately shaping the
spatial and temporal distribution of aquatic organisms (Bain and Stevenson
1999).

Understanding the relationship between physical and ecological
processes is essential for managing and predicting ecosystem response
(Richards et al. 1996). Varying physical and ecological processes emerge at
different spatiotemporal scales and reveal processes interacting among scales
(Fausch et al. 2002); thus, well planned rehabilitation and monitoring projects
require small, intermediate, and large-scale habitat information (e.g.,
pool—»reach——> watershed) to be effective. A multi-Scaled approach to aquatic
rehabilitation advocates the assessment of ecological systems at the local,
reach, and watershed scales to achieve rehabilitation goals through
understanding natural processes that create fish and wildlife habitat (Roni 2005).

Small-scale sites can range in Size from a few meters to 100m and data is
typically collected at points (e.g., habitat units) within the watershed (Roni 2005).
Some examples of small-scale data include stream temperature, dissolved
oxygen, or in-stream habitat-features (i.e., pool, riffle, run). Unique habitats or
disturbance events at specific sites (points) along a stream can have profound
effects, influencing properties of the entire system at great distances in either
direction (Fausch et al. 2002). Thus, point data alone has limitations and may
not reflect the complexity of physical, chemical, and biological interactions within

the watershed.

 

 

Intermediate-scale stream habitat features (e.g., reach, segment) typically
range from 100m to several kilometers and exemplify the scale of most
rehabilitation and monitoring efforts (Roni 2005). Qualitative habitat evaluations
and quantitative longitudinal profile surveys are some examples of stream reach
measurements (Armantrout 1998). Collecting stream reach data is a valuable
fisheries tool because it enables biologists to characterize the type Of habitat
available to fish during different life stages (Fausch et al. 2002). By
understanding the quality, quantity, and connectivity of in-stream fish habitat,
rehabilitation and protection efforts can be effective by addressing problems at a
spatial scale relevant to fish and the life stage of concern. Thus, reach scale
data should be gathered to ensure effective fisheries and watershed
management planning occurs.

Regional or large-scale habitat features can be an entire state (Wang et
al. 1997) or the catchment area above a sampling site (Roth et al. 1996), and this
approach requires landscape tools to achieve a broad—based view. GIS is a
useful computer-based tool for mapping, planning, and decision-making at
watershed-level scales (e.g., 1:100,000-1:24,000). Geographic information
systems (GIS) facilitate decision-making activities by allowing users to analyze
multiple data sets to reveal complex patterns and relationships (ESRI 2004). By
allowing multiple coverage overlays, GIS functions as a decision support tool that
can assist managers and biologists in the decision-making process and provides
a useful tool for modeling impacts on aquatic habitats (Fisher and Rahel 2004).

This map-based approach allows relationships between land-use activities, the

 

riparian zone, and in-Stream fisheries to be analyzed. For example, it is possible
to use GIS applications to predict the impact of hydrologic processes (e.g., spring
runoff, reaches with high rates of ground water input) on trout populations (Fisher
and Rahel 2004). By working with watershed-level scales, characteristics such
as geology, hydrology, and LULC databases can be used to asses land-water
relationships (Fisher and Rahel 2004).

National Fisheries Habitat Initiative (NFHI)

There has been a great deal of time and money invested in addressing
water quality and aquatic species concerns. Unfortunately, habitat loss and
degradation continues to occur. For example, over $1.8 billion was spent on
restoration activities between 1992 and 2001 in the Great Lakes Basin alone, yet
basic water quality and restoration problems continue to exist (USGAO 2003). In
the mid-west region of the United States (Michigan, Ohio, Indiana, Illinois,
Wisconsin, Minnesota, Iowa, and Missouri), 75% of freshwater mussels, 67% of
crayfish, and 60% of fish are imperiled locally, imperiled range-wide, or possibly
extinct (Patronski and Oetker 2004).

Due to the continued decline in fish populations, the Sport Fishing and
Boating Partnership Council proposed the cultivation of a national effort focused
on protecting, rehabilitating, and enhancing fisheries and aquatic habitats (NFHI
2004). In 2004, the US. Fish and Wildlife Service (USFWS) led this endeavor by
launching the NFHI. The NFHI has received overwhelming support from state,
federal, tribal, and non-governmental agencies. As a result, this national call to

action has grown into the National Fish Habitat Plan (NFHAP). The NFHAP is an

(HP

1° A

 

important on-going effort to move from opportunistic, site-specific restoration to a
science-based, landscape-level approach to aquatic habitat management
through the use of science and partnerships (NFHAPB 2006).

From the inception of the NFHI to the current NFHAP, the North American
Waterfowl Management Plan (NAWMP) was selected as a template for
developing and implementing a successful science-based, landscape-level fish
habitat program. The NAWMP was selected as a model because it is
acknowledged as one of the worlds' most prominent conservation initiatives
(CWS 2007). The NAWMP was initiated between the United States and Canada
in 1986 and later joined by Mexico in 1994. This continental joint venture
program’s main purpose is to address declining waterfowl populations through
protecting, restoring, and enhancing waterfowl habitat (USFWS 2007a). To-date,
$4.5 billion dollars has been spent on project goals achieving 15.7 million acres
of wetland and upland habitat across the participant countries (USFWS 2007b).
As a result of these efforts, targeted population goals have been surpassed for
three principal duck species (i.e., gadwall, green-winged teal, and northern
shoveler), and two other duck species (i.e., scaup and northern pintail) continue
to receive recovery efforts as outlined in the Plan.

In addition to the NAWMP, there are other successful conservation
initiatives; for example, the Oregon Plan for Salmon and Watersheds, the ’
Chesapeake Bay Program, and The Nature Conservancy’s Conservation
Initiatives. The success of these initiatives has been attributed to a unique mix of

characteristics such as being partnership-driven, science-based, and

 

geographically-focused. In addition, project efficacy is determined through
monitoring, which also allows long-term projects to be adaptively managed and
improves accountability. In fact, the NFHAP was designed by stakeholders to
incorporate these common characteristics that have been proven to mature into
successful conservation initiatives.

The NFHAP is a national effort to halt and recover declining fish
populations by establishing regional partnerships, using the best available
science to guide the decision-making process, and monitoring project
effectiveness. The plan also strives for partnerships to address problems at
spatiotemporal scales most meaningful to fish species and life stage of interest.
The success of this program is expected to be gauged by comparing stated
protection, rehabilitation, and enhancement goals (e.g. social, economic,
biological, and ecological benefits) against final, observed outcomes.
Additionally, monitoring and reporting measurable results will increase
accountability to political, peer, and public stakeholders to ensure fish population
and habitat goals are being achieved. Ultimately, the efficacy of the NFHAP will
be measured by meeting fish population recovery goals.

Currently, there are many different approaches to watershed protection,
rehabilitation, and enhancement efforts. These efforts range from being general
to specific and having different strengths and weaknesses. There has not been a
watershed planning and analysis framework selected or implemented for the.
NFHAP; thus, a component of my research was to apply an existing approach

that may be helpful in accomplishing the goals of the NFHAP.

12 A

Goal

The primary goal of this project is to apply a science-based, landscape-
level approach to watershed protection and rehabilitation utilizing an existing
watershed analysis and planning procedure. The specific objectives were to:

1) Apply the Ecosystem Analysis at the Watershed Scale: The Federal

Guide for Watershed Analysis (RIEC 1995) to Michigan’s Rifle River
watershed to describe hydrologic and land-use trends, and determine
the current stream temperature regime.

2) Recommend areas within the Rifle River watershed to protect and

enhance fish habitat.

First, I will discuss the watershed analysis and planning framework that was
selected and how it facilitates a science-based, landscape-level approach to
watershed protection and rehabilitation. Second, I will apply the watershed
analysis procedure to describe and quantify physical watershed processes.
Third, I will recommend areas to protect, rehabilitate, and enhance fish habitat
based on results of the selected watershed analysis procedure.

The results Of this study should provide insight to watershed management
professionals challenged with selecting, implementing, and predicting ecosystem
response to rehabilitation projects by using the best available science.
Additionally, a science-based approach improves accountability for money and
resources used for rehabilitation. Local governments could also use the results
of this study to advocate for sustainable ecological and economic growth by

protecting sensitive habitats; this should reduce the need for rehabilitation funds,

13 A

and protect aquatic species and their habitats. Lastly, knowledge gained from
this study may assist partners in selecting a watershed planning framework that
considers physical processes operating on multiple scales and halt the decline in
aquatic species populations.

PROTOCOL SELECTION

The design and implementation of science-based watershed rehabilitation
projects should involve the expertise of an array of disciplines such as, biologists,
entomologists, geomorphologists, hydrologists, and engineers. These teams of
experts should possess the scientific competency to ensure the best available
information is collected and used. The term “sound science” (or science-based)
will be used in this document as defined by the Society of Environmental
Toxicology and Chemistry as “organized investigations and observations
conducted by qualified personnel using documented methods and leading to
verifiable results and conclusions” (SETAC 1999). Using the best available
science to guide decisions will enhance accountability, and augment the field of
watershed rehabilitation (Williams et al. 1997).

Although this approach takes time and expertise, the benefits of science-
based, multi-scale rehabilitation are gaining recognition and momentum. Many
watershed groups and partners are interested in switching to this approach to
address the underlying causes of ecosystem impairment and to recover system
biotic and abiotic factors (NFHI 2006). By identifying and treating ecosystem

(watershed) dysfunction, the primary causes of degradation can be addressed

14 A

and the consequences of rehabilitation become more predictable (Williams et al.
1997)

Aquatic rehabilitation must also function as a component of regional and
local land management to be effective (Hobbs 1996). One way to achieve this
goal is for local watershed councils to develop partnerships with state, federal,
tribal and non-governmental organizations. Watershed councils are fOrmed from
community-based concerns for ecosystem health and are typically centered-
around a commitment to improve water quality and fish habitat. By partnering
with governmental agencies and non-governmental organizations, watershed
councils can gain access to technical expertise, coordination skills, and funding
resources essential to achieving rehabilitation goals.

Many other examples of federal, state, and non-governmental successful
watershed management planning guidelines and documents exist today.
Watershed frameworks have been developed to facilitate coordination among
partners, enhance the achievement of water resource management goals, and
maintain environmental quality (USEPA 2002). Since there has not been a
watershed planning and analysis framework selected or implemented for the
NFHAP, a component of this research was to select an existing approach with
the potential to accomplish the goals of the NFHAP.

The following overview of watershed protocols is based on a review of the
literature. This information is intended to provide insight into some of the I
drawbacks and benefits to three different watershed planning approaches 1) the

Michigan Department of Environmental Quality (MDEQ), 2) the Michigan

A

 

Department Of Natural Resources (MDNR), and 3) Ecosystem Analysis at the
Watershed Scale (EAWS; Table 1).
Michigan Department of Environmental Quality

The MDEQ guidelines (Developing a watershed management plan for
water quality: An introductory guide; Brown et al. 2000) are implemented under
the Clean Water Act (CWA; MDEQ 2006). Under CWA’S Section 319, the
federal government provides financial aid to watershed councils that are working
to reduce NPS pollution and protect water resources. In Michigan, the DEQ is
responsible for allocating Section 319 funds and assisting watershed councils in
establishing appropriate water quality criteria goals that are then addressed
through rehabilitation planning and implementation.

The MDEQ guidance document provides descriptive guidelines for
watershed management planning focused on water quality. This planning
process is valuable because it has a grassroots focus involving local citizens and
resource professionals who share a common vision. It also draws on the
expertise of multiple partners and promotes knowledge sharing among group
members. However, this approach narrowly focuses on water quality to restore
and protect designated uses.

Additionally, the MDEQ guidelines recommend identifying pollutants,
sources, and causes through a visual assessment of the watershed. Although
this descriptive approach familiarizes stakeholders with the watershed, the I
procedure falls short of cultivating a science-based understanding of watershed

processes and identifying the true causes of ecosystem impairment.

‘6 A

Michigan Department of Natural Resources

Another approach to watershed management planning is through the use
of MDNR’s comprehensive river assessments. River assessments are
documents prepared by Fisheries Division biologists for selected watersheds
(e.g., Thunder Bay River Assessment, Jordan River Assessment). Biologists use
the best scientific information available to describe the physical envirOnment,
dams and barriers, water quality, special jurisdictions, historical and modern
fisheries management, biological communities, public comments, and
management options for watershed issues. Drafts of the river assessments are
available to the general public for a period of review and comment.

Although citizens and other agencies are allowed to comment and provide
input prior to final publication, the lack of multi-agency expertise and public
involvement does not foster and engage partnerships. As a result, watershed
groups may be less willing to use a river assessment to guide protection,
rehabilitation, and planning efforts. Additionally, completed river assessments
are a compilation of agency findings from comprehensive literature and data
reviews, but do not focus on integrating ecosystem processes.

Finally, river assessment documents do not function as effective long-term
management tools because they present the results of agency analysis rather
than primary data (i.e., summarized rather than original data). Not having access
to the primary data limits watershed groups ability to incorporate additional data,
ask new spatial questions not addressed in the river assessment, or analyze

interrelated ecosystem processes (e.g., land use and water temperature).

A

17

 

Incorporating original agency data into a GIS-based tool would allow user groups
to evaluate rehabilitation and land use scenarios, functioning as decision support
systems to guide multi-level watershed planning, rehabilitation, and monitoring.
Ecosystem Analysis at the Watershed Scale (EAWS)

The third approach to watershed-scale planning is a guidance document
that was developed by a multidisciplinary group of federal, tribal, and State
partners in the western United States (Ecosystem analysis at the watershed
scale: the federal guide for watershed analysis; RIEC 1995). This procedure is
geared toward guiding interdisciplinary teams of resource specialists through a
six step ecosystem scale process. Teams are encouraged to use the best
available science and staff to complete the watershed analysis. The ecosystem
analysis procedure assists teams in characterizing the human, aquatic, riparian,
and terrestrial processes operating within a selected watershed by responding to
core watershed-level questions on erosion processes, hydrology, vegetation,
stream channel, water quality, and species.

This latter framework for watershed planning raises valuable questions
regarding catchment-level processes and interactions. The EAWS process is
designed to generate baseline information about the watershed and provide a
platform for continued integration of new information. The EAWS encourages
federal, tribal, state, local, and public involvement early in the planning process.
It also provides an ideal platform to incorporate GIS analysis to enhance I
decision-making such as project planning, modeling, development,

implementation, and monitoring. Lastly, it advocates a shift from species based

1" A

and site specific management to an ecosystem based approach where project
outcome becomes more predictable due to our understanding of watershed scale
processes.

Although the three guidelines to watershed assessment and planning
share a common goal of improving aquatic resources, they have different target
audiences, uses, and technical (scientific) content. I will apply the_Ecosystem
Analysis at the Watershed Scale: the Federal Guide for Watershed Analysis to
the Rifle River watershed for this study. Applying and evaluating this ecosystem-
based approach to watershed analysis and planning is necessary for several
reasons. First, it will determine if characterizing ecosystem processes operating
within a watershed is necessary for taking a scientific approach to watershed
rehabilitation and protection. Second, understanding the complex interactions
between land cover, which has a biophysical role, and other ecosystem
processes (e.g., water temperature, hydrology) can enhance watershed planning
committees ability to protect and rehabilitate valuable fish and wildlife resources.
Third, watershed assessment analysis conducted at multiple scales will provide
useful information to watershed planners in similar physiographic areas. Lastly, it
appears to be the most suitable protocol for accomplishing the goals of the

National Fish Habitat Action Plan.

19

 

APPLICATION OF ECOSYSTEM ANALYSIS AT THE WATERSHED SCALE

Ecosystem Analysis at the Watershed Scale (EAWS) is designed to guide
interdisciplinary, interagency teams through an ecosystem-based analysis rather
than the traditional site or species-Specific planning process. The six step
process is intended to guide planning teams in: 1) Characterizing a watershed, 2)
identifying key watershed issues, 3) describing current conditions relevant to key
watershed issues, 4) describing reference conditions relevant to key watershed
issues, 5) exploring and interpreting changes between reference and current
conditions, and 6) developing management recommendations for key watershed
issues. By applying the six step process to core topics - erosion processes,
hydrology, vegetation, stream channel, water quality, and species and habitats —
an understanding of basic ecosystem processes, conditions, and interactions can

facilitate sustainable watershed planning (Appendix A).

It is important to note that in a complete watershed analysis, the
interdisciplinary expertise of partners and public involvement would be crucial to
the development of an effective watershed plan. Due to time and personnel
constraints, a partial watershed analysis was conducted for this study, but I
consulted with professionals possessing essential interdisciplinary expertise. For
the purposes of this study, application of EAWS entailed characterization of land
cover (vegetation), hydrology, water temperature, and stream channel
morphology to address specific research questions about the Rifle River

watershed, as outlined below.

Land Use/ Land Cover

20

Land use/ land cover reflects the type and abundance of vegetation on the
landscape that can influence hydrology, water temperature, and stream Channel
morphology. Research questions were focused on determining if land cover
Changes have occurred over time, how natural habitats have been impacted
(e.g., changes in the type and abundance of vegetation, reduction of natural

habitat), and the spatial extent of changes.

First, | evaluated how land cover changed at the watershed scale from
historic (Circa 1800) to recent conditions (1992). Then I examined more recent
land use trends by comparing 1992 and 2001 vegetation data to determine if land
use changes occurred and the spatial extent of those Changes (watershed level,
90m, 60m, and/or 30m buffers). This information was used to address the

following research hypothesis:

H1: Between 1992 and 2001, there is no significant difference in land
use/ land cover at the watershed scale; however, there is a significant difference
in land use at the riparian scale (90m, 60m, and 30m buffers) due to a decrease

in natural habitats in the Rifle River watershed.

The rationale behind this research question is based on my expectation
that land use has shifted from agriculture to forest at the watershed scale and

that development within the riparian zone has increased.

Next, I evaluated if the 90m buffer was historically (circa 1800) an
indicator of watershed scale land use and if it is currently an indicator of
watershed scale land use. Previous research has produced incongruous results

on this topic and further investigation may Clarify the impact humans have on the

21

landscape. Finally, the watershed was divided into sub-watersheds to determine
if land use is significantly different in the upper, middle, and lower sections.

Results will be useful for recommending where to focus rehabilitation efforts.
Hydrology

Historic and existing hydrologic conditions were characterized to
determine if hydrology has Changed over time. Dominant hydrologic trends were
evaluated, along with other watershed Characteristics, to address the following

hypothesis:

H2: Watershed hydrology has changed over time, resulting in reduced storage
capacity and an increase in runoff due to loss of natural habitat within the riparian

zone.

The presence and abundance of vegetation on the landscape, climate,
and geology influence watershed hydrology; therefore, these Characteristics were
examined to assist in interpreting the results of hydrologic analysis. These
results will be useful for investigating natural and human influences on watershed
hydrology and the implications of those changes such as altering water quality

and stream geometry.
Water Temperature

Water temperature falls under the core topic of Water Quality and also
under the core topic of Species and Habitats. Water temperature, a water quality
and biophysical parameter, was also evaluated to determine if thermal conditions

are limiting salmonid distribution in the Rifle River watershed. Salmonids have

22

been the focus of previous rehabilitation efforts, but it is unclear if the Rifle River
exceeds water temperature tolerance limits for salmonids. Water temperature
data collected by the MDNR suggests tributaries to the Rifle River may be
providing thermal refuge for salmonids in the summer (July) and winter
(February) when water temperature extremes occur. Modeling heat transport will
explain the current environment and aid in identifying where temperature
extremes may be limiting salmonid distribution. It can also be used to classify
habitat for early life history steelhead (Oncorhynchus mykiss), resident brown
trout (Salmo trutta), and resident brook trout (Salve/inus fontinalis) based on
temperature requirements established in the literature. Based on global
temperature modeling presented in Bartholow (1989), the Rifle River has the
potential to be impacted by increases in global air temperature, which could shift
the range of salmonids north of the watershed. The temperature model can be
used to predict the impacts of global warming on salmonid distribution and
recommend priority areas for habitat rehabilitation and protection.
Stream Channel Morphology

This topic was qualitatively assessed by inventorying in-stream and
riparian habitat conditions in the mainstream and tributaries. The results of this
assessment will be used to identify potential concerns related to geomorphic
attributes and the quality of habitat available to fish, specifically salmonids. .
Study Site I

The Rifle River watershed (RRW) was selected as a prototype for this

study for numerous reasons including its significant fish and wildlife resources,

23

sociopolitical importance, and ecological integrity. The Rifle River is located in
northeastern-lower Michigan (Figure 2) and drains an area of approximately
99,718 hectares (385 square miles; MDNR 2002). The mainstream is perennial,
stretching 105 river kilometers (rkm) long (65 river miles) from Ogemaw to
ArenaC County where it empties into Lake Huron’s Saginaw Bay (HPRC&D
2005).

Of the 298 rkm of perennial stream in the watershed (185 river miles),
approximately 80 rkm (50 river miles) of the mainstream and 97 rkm (60 river
miles) of tributaries are designated as wild-scenic river under Michigan's 1970
Natural River Act (MDNR 2002). The Natural River Act also provides zoning set-
backs and restrictions to “preserve, protect and enhance the Rifle River
environment in a natural state for the use and enjoyment by all generations"
(MDNR 2002, p 19). Additionally, the mainstream and tributaries (above T19N,
R4E, Section 5, except Richter and Wells Creek) are recognized by the state of
Michigan as designated trout streams (MDNR 2000).

The RRW supports over 40 fish species (Table 2). The upper portion of
the watershed is known for brook trout and brown trout fishing (MDNR 2002). A
sucker run (Catostomidae spp.) occurs each spring and is celebrated locally
during the Omer Sucker Festival located in Omer, Michigan. There are also
numerous federal and state listed threatened, endangered, proposed, and
candidate species of plants, reptiles, insects, and birds found in the watershed
(Table 3). In addition to the quality fishing opportunities, the RRW offers hunting,

wildlife viewing, boating, trapping, and biking opportunities within a few hours

24

it

drive of major metropolitan areas (i.e., Detroit, Bay City, Saginaw, Midland, and
Flint), making it easily accessible to large urban populations.

The historical significance of the RRW dates back to Michigan’s earliest
inhabitants, which were Native Americans (Knutilla et al. 1971 ). There are two
sites near the mainstream that allegedly provided winter protection for Native
Americans during the period AD 1100-1400 (MDNR 2002). During the logging
era (1840-1900), Michigan dominated national lumber production from 1869 to
1900 (Cook 2006). As a result of this vast logging, lumbermen were attracted to
the area and created the settlements of Rose City, Lupton, and Selkirk (Knutilla
et al. 1971). Virgin white (Pinus strobus) and Non/vay pine (Pinus resinosa) were
harvested and floated down the Rifle River to be milled in Saginaw (Knutilla et al.
1971). The long-term impacts of Clear-cut logging are unknown for this
watershed. However, a study in Wisconsin found hydrologic and geomorphic
conditions such as the sediment loads and channel bed elevation were altered in
historically clear-cut areas that were then put into agricultural production
(Fitzpatrick et al. 1999).

Based on 1998 land use estimates, the watershed is approximately 55%
forested, 21% agriculture, 11% wetland, 3% urban, and 10% other (open space,
roads, and idle land; SBRC&D et al. 1999). Forested lands are predominantly
second and third growth pine and hardwoods; agricultural land is primarily for
dairy and cattle production (MDNR 2002). As a result of glaciation, the I
watershed has varied topography and relief. The northern portion rises above

400 meters elevation and has rolling hills (MDNR 2002). At the mouth, the

25

elevation is significantly lower as the river drains into an outwash plain of low
relief (USGS 1972).

The mainstream of the Rifle River is one of the few undammed, free-
flowing rivers in the Lake Huron watershed. The lack of barriers allows fish
movement to occur unimpeded. The basin receives an average of 29 inches of
precipitation a year and the upper portion of the watershed is predominantly
(~73%) groundwater-driven, which provides stable water temperatures and
ensures summer base-flows are sufficient to support resident trout populations
(Knutilla et al. 1971; SBRC&D et al. 1999). Although groundwater is primarily
supplied from glacial deposits of sand, clay, and gravel (Knutilla et al. 1971), 33%
of the input is derived from sub-surface interbasin flow from the adjacent
AuSable River watershed (SBRC&D et al. 1999). Due to artificial drainage (e.g.,
county drains) and underlying Clay soils, the middle and lower portions of the
watershed experience flashier hydrography (i.e., rapid increase in stream flow;
SBRC&D et al. 1999). In addition to the perennial stream system, approximately
435 rkm of intermittent stream exist throughout the watershed.

The Rifle River Watershed Restoration Committee (RRWRC) was officially
formed in the mid-19905 to increase coordination and collaboration of public and
private partners working to reduce NPS pollution within the watershed (SBRC&D
et al. 1999). Prior to the council’s formation, the Mershon Chapter of Trout
Unlimited led habitat rehabilitation efforts aimed at improving the recreational
trout fishery (SBRC&D et al. 1999). Concern over protecting the high water

quality and recreational opportunities drove numerous rehabilitation efforts,

26 A

guidance documents, and state initiatives within the watershed (e.g., road—stream
crossing assessment, streambank erosion inventory, in-stream habitat projects).
Currently, a storm water management study is being conducted to protect the
watershed from sediment and pollutants associated with storm water discharges.

The RRWRC is composed of local citizens, non-governmental
organizations (Saginaw Mershon and Ann Arbor chapters of Trout unlimited,
Saginaw Bay and Huron Pines Resource Conservation & Development
Councils), the Saginaw Chippewa Indian Tribe, and state (MDNR, MDEQ,
Michigan Department of Agriculture) and federal agencies (US. Department of
Agriculture, USFWS). Although the RRWRC is not currently a nonprofit
organization, members have been meeting since the 1990s and partners have
raised nearly $1.5 million for habitat protection and rehabilitation (HPRC&D
2005). Members of the RRWRC are dedicated to the resource and share a
common goal to HELP (Honor, Enjoy, Love, and Protect) the watershed
(HPRC&D 2005). The Nature Conservancy has also identified this watershed as
a conservation area but has had little involvement due to funding restrictions
(Kline personal comm. 2004).

The RRW was selected as the nation’s first watershed development
program in the 19503, functioning as a pilot for conducting watershed-level
stream improvements (Tody 1950). The ultimate goal of this effort was to
enhance trout production by treating fish and game production, stream pollution,
soil erosion, and agriculture as components of stream degradation (MDC 1951 ).

Although innovative for its time, this landscape approach to rehabilitation fell

27 A

Short of its goal because river and ecosystem processes were poorly understood
during that era. The watershed was selected then, as it is now, due its natural
and cultural importance, proximity to large urban populations, resilient community
advocacy, and relatively intact ecological integrity. The decision to use the RRW
today was also based on the existence Of historical hydrological and fisheries
information from the 1950s, when the upper portion of the watershed was used
as a fish and wildlife field laboratory (MDNR 2006).

Starting in the 19503, trout populations of the RRW have been the focus of
many stream improvement projects (Gowing 1968); therefore, a thermal model
was developed to evaluate water temperature trends and identify potential
biological limitations within the watershed. Due to stakeholder interest, there has
been a significant amount of site-specific rehabilitation work conducted within this
watershed, but additional work remains as new areas become degraded and
unplanned development threatens ecosystem health. An understanding of how
watershed processes operate and interact is essential for long-term management
and protection of the RRW. Currently, there is insufficient information regarding
how the watershed functions as a system and whether rehabilitation efforts have
been effective.

METHODS
GIS Land Cover Analysis

To answer research questions, the vegetation category of EAWS was

expanded to reflect commonly used GIS land cover classification schemes.

Plant communities (e.g., forests, wetlands, grasslands) with the potential to

28

 

benefit wildlife are natural habitats and unnatural habitats include land use
activities (e.g., commercial, residential, agriculture) that may be negatively
impacting physical and biological watershed characteristics. Land cover data is
commonly available in the GIS environment and existing data from Circa 1800,
1992, and 2001 were used in this study to determine changes in land use
patterns over time.

The circa 1800 dataset was selected to represent historic conditions
because it is the oldest GIS vegetation layer available for the Michigan and the
best representation of plant communities prior to human disturbance. The most
recent GIS vegetation data available was collected in 2001, thus 2001 data will
be used to represent current land use patterns. The 1992 dataset was selected
because it will allow research questions to be addressed about recent land use
changes in the watershed (from 1992 and 2001).

Circa 1800 vegetation data was obtained from the Michigan Natural
Features Inventory (MNFI) and was used in this study because it represents
Michigan's vegetation prior to logging and development. This GIS layer has a
pixel resolution of 30 square meters (m2) and is an interpretation of land surveyor
notes recorded between 1816 and 1856. A limitation of this dataset is that
surveyor transects were spaced approximately 1.6 kilometers (km) apart and
land cover has been interpolated between transects based on factors such as
soils, current wetlands, slope, elevation, and aspect (Schools 2007). As a result
of the scale differences between the Circa 1800 vegetation dataset and current

land cover datasets, quantifying a direct Change in land cover over time would be

29

 

inappropriate (Schools 2007); thus, changes in land cover were compared as
percentages. Spatial data for 1992 and 2001 was obtained from the US.
Geological Survey's National Land Cover Database (NLCD) and is based on
Landsat satellite imagery, which has a pixel resolution of 30 m2. The 2001
dataset was chosen to reflect current conditions within the watershed. Due to the
limited datasets in existence, the 1992 dataset was used as an intermediate
dataset between Circa 1800 and 2001.

GIS land cover analysis was conducted at the watershed level and within
riparian buffers using ArcGlS 9.0. Due to the 30m2 resolution of the GIS layers,
stream buffer widths of 30m (~100ft), 60m (~200ft), and 90m (~300ft) were used
in analysis and buffers extended on both sides of the river. The 30m (60m lateral
zone) buffer was of interest because it is the approximate width of the USDA
Forest Service riparian buffer model (Welsch 1991), and provides benefits such
as sediment removal, reduced erosion, and water temperature stabilization
(USDA-USFS 2003; Schultz et al. 1994). The 60m (120m lateral zone) buffer
was selected because it is an intermediate between the 30m and 90m buffer
widths and is the optimal minimum width for effective flood control (USDA-USFS
2003). The 90m buffer (180m lateral zone) was used in this study to evaluate
how buffer and whole catchment land use is influencing the RRW. In addition,
the watershed was divided into upper, middle, and lower sections or sub-
watersheds to assess if current (2001) land use is similar throughout the
watershed. Sub-watershed delineation was based on rationale as outlined in the

Qualitative Habitat Assessment methods of this thesis.

30

 

The buffer tool was used to generate riparian buffers of 30, 60, and 90m
extending on both sides of the Rifle River polyline feature. Each of the buffers
was then used as a mask to extract land use from the vegetation raster image
datasets (1800, 1992, and 2001). The percentage of total area per land cover
Class was calculated for the entire watershed and riparian buffer areas within the
watershed. For sub-watershed analysis, land use percentages were based on
the proportion of land use in each Class relative to the total area of the sub-
watershed. For example, the percentage of deciduous forest in the upper portion
of the watershed was calculated by dividing the amount of deciduous forest in the
upper watershed by the total area of the upper watershed.

Fourteen land cover classes were identified in the MNFI circa 1800 layer
and 15 land cover classes were identified in the 1992 and 2001 NLCD for the
Rifle River watershed. For the purpose of analysis, MNFI and NLCD land cover
classes were each reclassified into the following eleven categories: open water,
deciduous forest, coniferous forest, mixed forest, woody wetlands, herbaceous
wetlands, grasslands/ herbaceous/ savannah, scrub/shrub, developed,
agriculture, and miscellaneous unnatural habitats (Table 5).

Summarized results of land cover were calculated and plotted to
determine if land cover classes could be further combined. Miscellaneous
unnatural habitats were not present in circa 1800 and represented only a small
percentage (0.0% to 0.3%) of land cover during 1992 and 2001; therefore, this
category was omitted from all graphs and values were combined with the

developed Class for statistical analysis. Woody and emergent herbaceous

31

 

wetlands were originally calculated separately to determine the dominant type of
wetland habitat present in the watershed. At all spatial and temporal scales
analyzed, woody wetlands were the dominant wetland vegetation and emergent
herbaceous wetland lands represented a small percentage of land use (<5%).
Thus, woody wetlands and emergent herbaceous wetlands were combined into
one Class labeled wetlands for statistical analysis. The shrub/scrub land cover
class was only present in 2001; it is defined as an area dominated by shrubs less
than 5 meters tall and greater than 20% canopy. This class was not similar to
any of the other land cover classes, thus it could not be combined nor omitted.
Open water was not included in statistical tests because percentages were
similar between all years and this land cover Class was not directly related to
hypothesis testing.

Statistically significant relationships were determined by a p-value of
pS0.05 and all calculations were computed in the statistical software SAS®. I
used the chi-square test (X2) of homogeneity to determine if overall land use was
statistically different between vegetation datasets and/or spatial scales being
evaluated. Where chi-square test results were significant, the chi-square test
was repeated to determine if land use classes could be combined and identify
which land use classes were different from each other (Snedecor and Cochran
1989). Land use categories were combined until no further categories could be
combined due to significant Chi-square results (Snedecor and Cochran 1989).

Land use differences were evaluated between circa 1800 and 1992 at the

watershed scale, to establish if land cover changes had occurred over time.

32 t

Land use differences were evaluated between 1992 and 2001 at all four spatial
scales (watershed, 90m. 60m, and 30m) to address my hypothesis that there is
no significant difference in land use between 1992 and 2001 at the watershed
scale, but a significant difference exists in land use at the riparian scale (90m,
60m, and 30m buffers). Land use differences were evaluated for both circa 1800
and 2001 between the watershed scale and the 90m riparian buffer to ascertain if
the 90m buffer is reflective of watershed scale land use. Further analysis was
conducted on the 2001 dataset to identify where watershed scale and riparian
buffer land use is different. Finally, land use similarity was determined for sub-
watersheds to identify areas where rehabilitation efforts should be focused. In

summary, land use similarity was determined:

1. between circa 1800 and 1992 at the watershed scale,
2. between 1992 and 2001 at the watershed scale,
3. between 1992 and 2001 within each riparian buffer (90m, 60m, and
30m),
4. between circa 1800 watershed scale and 90m riparian buffer,
5. between 2001 watershed scale and each riparian buffer (90m, 60m,
and 30m),
6. 2001 at the sub watershed scale.
Hydrologic Analysis
A 69-year (1938 — 2006) record of mean daily discharge data was
evaluated to describe and quantify changes in the hydrologic regime of the RRW

over time. United States Geological Survey (USGS) stream flow data was used

33 a

to characterize current and historical hydrological conditions in the watershed.
Currently, there is one functional USGS gage station operating in the watershed
and it is located on the mainstream of the Rifle at Melita Road (M-70) near
Sterling, Michigan. The gage is positioned 32 km upstream of the mouth and
drains an area of approximately 830 km2 (320 mi2), which is approximately 80%
of the watershed. Mean daily discharge data for water years (October 1 —
September 30) in cubic feet per second (Cfs) was used in hydrologic calculations.

Indicators of Hydrologic Alteration (IHA) software (The Nature
Conservancy 2006; Richter et al. 1996, 1997, 1998) was used to conduct trend
analysis. The 69-year record of data was analyzed as a single time period (i.e.,
trend analysis) because there have been no significant impacts to the system
over time (e.g., dam, impoundment) that would warrant pre- and post-impact
assessment. The lHA software calculates 67 statistical parameters, consisting of
33 lHA and 34 Environmental Flow Component (EFC) parameters, and
generates associated significance values (Tables 6 and 7; The Nature
Conservancy 2006).

Hydrologic datasets typically have non-normal (skewed) distributions;
therefore, non-parametric statistics (i.e., median and percentiles) were selected
for calculation in the lHA software because they provide more robust measures
of central tendency and variability within the dataset and are less affected by
extreme values. Parametric statistics (i.e., mean and standard deviation) were
calculated for average monthly flows and flood frequency because this approach

was recommended in the lHA literature. The use of parametric statistics

34 t

removed some of the variability by using the average rather than median value,
and therefore provided higher r2 values by improving the fit of the trend line to the
data. The lHA software uses least-squares fit regression lines to evaluate trends
over time along with regression values and statistical test results (p-values). l
determined statistically significant relationships by a p-value of S 0.05. Rain
events were assumed to be occurring uniformly within the watershed and
modeling the influence of the location of precipitation events to the stream gage
was not a component of this research.

In addition to lHA analysis, mean annual flow was calculated because it
reflects the average river flow over the period of record, which provides a more
stable measure of stream flow over time. Mean annual discharge was computed
by dividing the sum Of mean daily discharge values by the number of daily
discharge values for the year. Stream flow values were plotted over time and
visually examined for trends.

Water Temperature Model

I developed a mechanistic temperature model for the RRW to evaluate
and quantify heat transport within the system. In particular, I was interested in
modeling water temperatures under low flow conditions because they can limit
fish distribution. During low flow conditions, stream depth decreases as flow
decreases, potentially raising water temperatures (Gordon et al. 2004). | used
the Stream Network TEMPerature model (SNTEMP), which was developed. by
the USFWS as a tool to predict daily mean water temperature in response to

stream manipulation activities (e.g., rehabilitation, irrigation diversions, thermal

35

 

loading). Input data required to build the SNTEMP mechanistic stream
temperature model include stream geometry, time period, meteorology, and
hydrology parameters (Table 8; Bartholow 2000). Data necessary to construct
the temperature model was obtained from existing sources or gathered in the
field, depending on availability.

The stream network was defined by assigning Melita Road (M-70) near
Sterling, Michigan, as the starting reference point (rkm 0.0) and working
upstream to the headwaters of the Rifle River near Lupton, Michigan (rkm 60.0).
Melita Road was selected as the model reference point because coinciding water
temperature and discharge data were available for this site and both are
necessary for model development. The USFWS Sea Lamprey Control Program
generously provided discharge data that was collected throughout the watershed
during 1997 and 2000; this data was used to identify tributaries that have the
ability to thermally alter the mainstream (contribute 2 10% Of the mainstream
flow) and should be represented in the SNTEMP stream network. This task was
accomplished by plotting distance (rkm) against discharge (cfs) for the
mainstream and tributaries. The following tributaries were identified and
incorporated into the model through this process: Houghton Creek, Prior Creek,
Klacking Creek, and the West Branch of the Rifle River (Figure 3).

Latitude, elevation, and distances (rkm) were measured using Terrain
Navigator USGS topographic map software from Maptech®. Manning’s n is a
measure of channel roughness that typically changes during high and low flow

conditions and as the stream bottom changes. SNTEMP, however, regards

36

Manning’s n as a constant and an acceptable default value of 0.035 was used in
the model.

Stream wetted width and percent shade data was collected in the field.
Stream wetted width was estimated by measuring the width of the waters surface
perpendicular to channel flow at 3 transects in each stream reach. Stream reach
lengths were determined by multiplying wetted width at a representative channel
cross-section by 7 (Bain and Stevenson 1999). Transects were evenly spaced
along the reach and wetted width values were averaged. The amount the stream
segment was shaded by vegetation at noon in July, also known as percent
shade, was visually estimated for the SNTEMP model.

The SNTEMP model allows mean annual air temperature at the weather
station to function as a surrogate for ground temperature. The streambed
thermal gradient is an insensitive parameter and the suggested model default
value based on Bartholow (2000) was used.

All meteorological data was obtained from the National Climate Data
Center (NCDC). Saginaw MBS International Airport in Freeland, Michigan, was
the Closest weather station with comparable climate and topography to the Rifle
River basin; therefore, mean daily air temperature, wind speed, dew point, and
cloud cover data from this station were used to develop the SNTEMP model.

Missing air temperature values were derived by using the average daily
temperature for the day before and after the missing data value. Quality cOntrol
was conducted on all air temperature values (actual and derived) by plotting

temperature over time to visually assess for outliers. No outliers were identified.

37

 

Relative humidity data was not available but could be approximated with a low
error rate (0.6%) by using dew point and air temperature as outlined by
Bartholow (1989) with the following equation:

Rh = [(112 - 0.1 TA + po) / (112 + 0.9 TA)]8
where Rh = relative humidity, TA = air temperature °C, po = dew point
temperature °C. Hourly cloud cover information was calculated to obtain daily
average percentages. Percent sunshine was estimated by subtracting the
percent of daily average cloud cover from 100%. Average daily dust coefficient
and ground reflectivity values were estimated using equations presented in
Theurer et al. (1984).

Average daily discharge data was Obtained from the USGS. Water
temperature data was collected by the MDNR using HOBO water temperature
data loggers that were placed at 16 sites (11 tributaries and 5 mainstream sites)
within the watershed. Average daily lateral inflow temperature was estimated to
be 72°C (45°F) by using mean annual air temperature for the weather station as
a surrogate.

Water temperature data was gathered in the field between 1997 and 2005;
however, it was not continuously recorded during this time period and varied
within and between sites. The HOBO temperature loggers measured water
temperature 20 or more times a day, depending on how the unit was
programmed. The four major contributing tributaries previously identified and
incorporated into the stream network (Houghton, Prior, Klacking, and the West

Branch) and four road-stream crossings on the mainstream (Ranch Bridge, State

38

 

Road, M55, and M-70) were among the sites with existing temperature data. I
determined that 1997 and 2000 calendar years had the most complete water
temperature data sets. Hydrologic data for these two years was then further
examined to determine if they were similar water years or represented different
types of flow conditions.

Daily mean discharge data was visually evaluated for the period of 1995 to
2005 to determine annual discharge trends for 1997 and 2000 (Figure 4). Winter
and spring discharge measurements for 1997 were above the 11 year average
(1995-2005) and 2000 discharge measurements were below the 11 year
average. The 1997 data set was used for model development and calibration.
The 2000 data set was used for model validation and modeling low flow
conditions.

Quality control was conducted on water temperature data by graphically
plotting date against temperature to identify outliers. Only one site on the West
Branch of the Rifle River had an extreme water temperature reading on the
morning of June 10, 1997, and this temperature was removed from the dataset.
This temperature appeared to be erroneous due to a large fluctuation in water
temperature (:I: 10°F) within a two hour and twenty minute period.

Average water temperature values were calculated for each day. Missing
daily water temperature values for stream network sites were estimated by
chronologically ordering all daily air temperature and discharge values from
January 1997 to December 2005 and running a multiple linear regression in

SAS®. This approach allowed me to successfully derive a predictive relationship

39 i

between daily average values for air temperature and discharge to fill missing
water temperature values.

Water temperature data collected on the mainstream and tributaries was
examined for existing trends and potential limitations to salmonids. The only
continuous period of record where water temperature data existed for all
mainstream data collection sites was from February 1997 to January 1998.
Tributary data existed from late June 1997 to early June 1998 with the exception
of Prior Creek where regression values were used. Mean daily water
temperature was plotted over the corresponding period of record. Below zero
water temperature values were Changed to zero because the SNTEMP model
produces error messages when negative values are present.

The model was calibrated by adjusting input parameters until predicted
water temperatures matched measured temperatures closest. To determine if
calibration adjustments improved model performance, predicted and measured
temperatures were plotted over time and visually assessed. The calibration
process was completed when no further model adjustments could be made to
improve model performance and the model’s output provided the “best fit” to
measured water temperatures. Residuals (predicted - measured) were grouped
into four categories to describe the model’s predictive performance in terms of
the percentage of time that predictions exceeded measured temperatures: 1)
optimal (<1°C), acceptable (<2°C), marginal (24°C), and unacceptable (>4°C).

Stream width coefficient A, width exponent B, global air temperature, and

humidity were adjusted as calibration parameters. Originally, stream width

40

coefficient A had been set to stream wetted width measurements and width
exponent B was set to zero, where width = (width coefficient A)*flow (Width ”We“ 3’
These were adjusted by developing a width-flow relationship and performing a
standard linear regression for different portions of the watershed. The calculated
coefficient A of 2.26 and exponent B of 0.25 was used for the upper section,
coefficient A of 3.6 and exponent B of 0.86 was used for the middle section, and
coefficient A of 4.8 and B exponent of 1.14 was used for the lower portion of the
watershed. The global air temperature calibration coefficient was set to 1.1.
Relative humidity values were decreased by 20-40% to reduce over—prediction
during the winter. These adjustments were deemed appropriate because air
temperature and relative humidity values used in the model were collected offsite
(Saginaw International Airport) and under different conditions than those at the
stream.

Originally, all headwater nodes (upstream boundaries of the mainstream
and tributaries) were assigned zero discharge and allowed the model to estimate
water temperature based on mean annual air temperature. However, using
mean annual air temperature resulted in water temperature predictions that were
uniform throughout the year, regardless of season (e.g., Houghton Creek at the
mouth was 766°C all year) and this was not reflective of measured water
temperature data. Thus, the model tended to over estimate mean daily water
temperature in the winter and under estimate temperatures in the summer at all
sites above Melita Road. As a result, all headwater nodes were moved Closer to

where actual temperature data had been collected and this improved model

41

performance. Temperature data had been collected on tributaries near the
mouth; therefore, using measured values in the model resulted in more accurate
modeling of thermal contributions to the mainstream.

After the model was calibrated, a second independent dataset from 2000
was used for validation. Model validity was determined by graphically and
statistically comparing measured data from 2000 to the models predicted
temperatures. Graphically, the model was considered to be validated if predicted
temperatures closely matched measured values. Statistically, the model was
evaluated for goodness-of—fit by calculating the root mean square error (RMSE)
using the following equation:

RMSE = I 2 (Pi — Oi)2/ n ] 0'5

where Pi = prediction at time/space i

Oi = observed value at time/space i

n = number of samples
The RMSE is a measure of the average error and will give an approximation of
the difference between measured values and predicted. To check for systematic
errors, residuals were plotted against observed water temperatures values and
examined for trends. Finally, the process was ended when the error level was
within an acceptable range (<10% of predicted values exceeded measured
values by 4°C).

After calibration, the model was used to predict mean daily temperatures
every 2 km along the stream network. Output results were used to calculate

mean weekly temperatures and plotted seasonally over the length of the river to

42

explain the current water temperature regime and identify where thermal
limitations to salmonids may be occurring in the watershed. To determine the
percentage of time watershed temperatures exceed optimal and lethal
temperatures for salmonids, temperature duration curves were graphed.
Temperature duration curves were constructed for the upper (above M-55),
middle (above Maple Ridge Road), and lower watershed (above Melita Road) to
determine the percent of time these sections of the watershed are suitable for
salmonids. Global warming was modeled by increasing mean annual air
temperature by 2.7°C. Daily model output values were then used to calculate
mean weekly temperatures and plotted for the length of the river.

Lethal, optimal, and favorable water temperatures for brown trout, brook
trout, and steelhead were used to interpret the biological implications of model
output. Based on the literature and a synthesis of Michigan DNR temperature
information, mean daily temperatures of 10-21°C are considered favorable for
salmonid growth, 15-19°C is optimal, > 20°C is lethal for brook trout, and > 24°C
is lethal for brown trout and steelhead (Wehrly et al. 1999; Eaton et al. 1995;
Table 10).

Qualitative Habitat Assessment

A qualitative in-stream habitat assessment was conducted to characterize
intermediate scale habitat features in the mainstream and tributaries. The
mainstream was broken into three sections or sub-watersheds: upper
(headwaters to M-55), middle (M-55 to Maple Ridge), and lower (Maple Ridge to

Melita Road). Sub-watershed delineation was based on changes in geology,

43

stream gradient, salmonid stocking boundaries, and a visual assessment of the
river (Table 11). The mainstream from the headwaters (Rifle River Recreation
Area) to Melita Road was visually assessed by kayaking the river and noting
Changes in field characteristics. Sample reaches were selected to accurately
represent the proportion and variety of topographic features, riparian vegetation
or land use, geomorphic features (i.e., pool, riffle, run), substrate, and
streambank erosion represented within each sub watershed. Data was collected
on 4.5 rkm within each sub watershed for a total of 13.5 rkm.

The tributaries sampled are those that the State of Michigan considers
vital to the protection of the mainstream (Table 12). Three attempts were made
to collect data on Silver Creek, but there was no satellite reception and the Site
was not surveyed. This resulted in each of the 16 tributaries being sampled 1
rkm for a total of 16 rkm. Data was collected by entering the channel near the
mouth or tributary junction and walking upstream. This technique reduced
turbidity and allowed stream bed features to be observed. A total of 29.5 rkm
were sampled within the watershed (Figure 9). Approximately 700m of
Mansfield Creek were surveyed, however, the GPS unit malfunctioned and this
site was not revisited due to limited access.

The following habitat feature and feature Characteristic information was
recorded to describe riparian and in-stream habitat at the mesohabitat scale
(pool, riffle, run): 1) reach name, 2) habitat feature - pool, riffle, run, 3) location —
latitude/longitude recorded at the start of each habitat type 4) dominant substrate

estimate based on three grab samples 5) primary vegetation within 9m (~30ft) of

44

lotic habitat (right and left banks were independently assessed) 6) riparian
density (right and left banks were independently assessed), 7) stream canopy,
and 8) site quality (Tables 13 and 14). For the purposes of analysis, substrate
classes were reclassified into five substrate categories: boulder (>256mm),
cobble (64-256mm), gravel (2-64mm), sand (0.06—2.0mm), and silt (<0.06mm).
This more general classification of substrate data facilitated visual interpretation
of results.

Information on the location and associated habitat characteristics was
recorded using a Trimble® GeoXM TM GPS (Global Positioning System) unit with
2-5m accuracy. After collection, data was downloaded, differentially corrected,
converted into shapefiles, and appended to calculate summary statistics on sub-
watershed, tributaries, and watershed-level mesohabitat data.

Collection of stream wetted width information is described under SNTEMP
methods. Depth was measured at four evenly spaced locations along each of
the three transects within the stream reach. All depth values for the reach were
averaged to estimate average stream depth. The original 10 substrate groups
used in data collection (boulder, cobble, pebbles, granules, very coarse, coarse,
medium sand, fine sand, very fine sand, and silt) were reclassified into 5 groups
(boulder, cobble, gravel, sand, and silt) to present results in a more concise

manner.

45

RESULTS
GIS Land Cover Analysis

Results of the Chi-square test showed overall land use is different (p<0.01)
between Circa 1800 and 1992 at the watershed scale (Figure 10). Further
analysis of the chi-square suggests there is no difference (p=0.17) between
evergreen forest and mixed forest land cover in the two years and these
categories were combined; evergreen forest (41.8% to 6%) and mixed forest
(30.4% to 9.5%) both decreased between 1800 and 1992. In addition, deciduous
forest, grasslands, agriculture, and developed lands were not different (p=0.63)
between the two years and these categories were combined; deciduous forest
(2.4% to 35.2%), grasslands (0.2% to 4.3%), agriculture (0% to 25.4%), and
developed (0% to 1.1%) all increased between Circa 1800 and 1992. Wetlands
were different (p<0.01) from all other land use categories; they decreased (24.1%
to 16.7%) between circa 1800 and 1992. Overall, natural habitats declined from
100% in the 1800’s to 73.5% of land use in 1992.

Results of the chi—square test showed overall land use at the watershed
scale is different (p=0.03) between 1992 and 2001 (Figure 10). Further chi-
square tests established deciduous forest, evergreen forest, mixed forest,
wetlands, and agriculture land use classes were not different (p=0.49) from each
other in the two years and these categories were combined; deciduous forest
(35.2% to 29.6%), mixed forest (9.5% to 5%), and agriculture (25.4% to 15.2%)
all decreased while evergreen forest (6% to 7.8%) wetlands (16.7% to 19.4%)

slightly increased between 1992 and 2001. There was no difference (p=0.42)

46

between grasslands, developed, and shrub land for the two time periods and
these categories were combined; grasslands (4.3% to 10.5%), developed (1.1%
to 9.3%), and shrub (0% to 1.7%) land use practices all increased from 1992 to
2001. The percentage of natural habitat stayed relatively stable between 1992
and 2001 (73.5% to 73.9%), however, Changes occurred in the type of unnatural
land use occurring. There was a reduction in agriculture and an increase in
developed areas from 1992 to 2001.

Results of the Chi-square test showed overall land use within the 90m
buffer is different (p<0.01) between 1992 and 2001 (Figure 11). Further Chi-
square tests were conducted and there was no difference (p=0.98) in evergreen
forests and wetlands in the two years within the 90m buffer and these categories
were combined; evergreen forest (8% to 10.7%) and wetlands (26.4% to 34.9%)
increased within the 90m buffer between 1992 and 2001. There was no
difference (p=0.67) in deciduous forests, mixed forests, and agriculture within the
90m buffer in the two years and these categories were combined; deciduous
forest (26.5% to 18.5%), mixed forest (10.4% to 4.5%), and agriculture (21.7% to
10.9%) all decreased within the 90m buffer between 1992 and 2001. There was
no difference (p=0.59) in grasslands, developed, and shrub land within the 90m
buffer in the two years and these categories were combined; grasslands (2.9% to
8.7%) developed (0.9% to 8%), and shrub (0% to 1.2%) land all increased within
the 90m buffer between 1992 and 2001. There was a slight increase in natural

habitats from 77.3% to 79.9% within the 90m buffer between the two years.

47

There was a significant difference (p=0.01) in 60m buffer land use in 1992
and 2001 based on Chi-square test results (Figure 12). Further chi-square tests
were conducted and there was no difference (p=0.85) evergreen forests and
wetland land use in the two years within the 60m buffer and these categories
were combined; evergreen forests (8.5% to 10.4%), wetlands (28.7% to 38.8%)
increased within the 60m buffer between 1992 and 2001. There was no
difference (p=0.61) in deciduous forests, mixed forests, and agricultural land use
within the 60m buffer in the two years and these categories were combined; from
1992 to 2001 there was a decrease in deciduous forests (25% to 17.4%), mixed
forests (11% to 4.3%), and agriculture (19.9% to 10%) within the 60m riparian
buffer. There was no difference (p=0.64) between grasslands, developed, and
shrub land use within the 30m buffer in the two years and these categories were
combined; grasslands (2.5% to 7.8%), developed (0.8% to 7.3%), and shrub (0%
to 1.1%) all increased within the 60m buffer between 1992 and 2001. Natural
habitats within the 60m buffer showed a small increase from 77.3% to 79.9%.

Overall, land use within the 30m buffer in 1992 and 2001 was different
(p=0.01; Figure 13). Further chi-square test results Showed there was no
difference (p=0.31) between deciduous forests, evergreen forests, and wetlands
in the two years within the 30m buffer and these categories were combined;
there was a decrease in deciduous forests (22.7% to 16.6%) and wetlands
(31.7% to 42.5%) while evergreen forest (9.1% to 9.7%) slightly increased within
the 30m buffer between 1992 and 2001. There was no difference (p=0.62)

between mixed forests and agricultural land use between the two years within the

48

30m buffer and these categories were combined; mixed forests (11.2% to 4%)
and agriculture (18.4% to 9.3%) land use decreased within the 30m buffer
between 1992 and 2001. There was no difference (p=0.69) in grasslands,
developed, and shrub land use in the two years within the 30m buffer; grasslands
(2.1% to 7.1%), developed (0.7% to 6.7%) and shrub (0% to 1.0%) land use all
increased within the 30m buffer between 1992 and 2001. There was an overall
increase from 79.2% to 81.6% in natural habitats within the 30m buffer.

Results of the Chi-square test determined that overall land use was
different (p<0.01) between the watershed scale and within the 90m buffer in Circa
1800 (Figure 14). There was no difference (p=0.30) between deciduous forest,
evergreen forest, mixed forest, and grasslands deciduous forest between the
watershed scale and 90m buffer based on chi-square tests; thus, these Classes
were combined into one; there was a decrease in deciduous forest (2.4 to 2.2%),
evergreen forest (41.8% to 19.7%), mixed forest (30.4% to 28.6%), and
grasslands (0.2% to 0%) between the watershed scale and the 90m buffer. This
combined class was different from wetlands (p<0.01); there was a smaller
percent of wetlands at the watershed scale than within the 90m riparian buffer
(24.1% and 47.1%). Since this dataset was based on pre-settlement information,
natural habitats comprised 100% of land cover.

Overall, land cover in 2001 at the watershed scale was similar to the 90m
(p=0.29) and 60m (p=0.10) buffers (Figure 15). The 30m buffer was different
(p=0.03) from watershed scale land use in 2001. As a result, additional Chi-

square tests were computed and there was no difference (p=90) between

49

deciduous forest, evergreen forest, mixed forest, grasslands, developed,
agriculture and shrub land use in 2001 between the watershed scale and within
the 30m buffer; thus, these categories were combined. There was a decrease in
deciduous forest (29.6% and 16.6%), mixed forest (5% and 4%), grassland
(10.5% and 7.1%), developed (9.3% and 6.7%), agriculture (15.2% and 9.3%),
and shrub (1.7% and 1%) while there was a slight increase in evergreen forest
(7.8% and 9.7%). There was a difference (p<0.01) between wetlands and all
other classes combined. The percentage of wetlands was higher within the 30m
riparian buffer (42.5%) than at the watershed scale (19.4%).

Based on sub-watershed delineation, the upper, middle, and lower
portions of the watershed represented 31%, 30%, and 39% of the total area
respectively (Figure 16 and 17). The chi-square test determined that there was
no difference between land use in the sub-watersheds during 2001 (p=0.99).
There was also no difference (p=0.17) between land use within 30m of the river
in the sub-watersheds.

Hydrologic Analysis

The Rifle River is a fourth-order stream with a mean annual discharge 317
cfs for the period of record (1937 to 2006). The average high flow was 2105 cfs
and the average low flow was 131 cfs for the period of record.

Mean/Standard Deviation

Analysis of mean monthly stream flow (lHA Group 1) indicated an upward

trend for the month of August (p=0.05; Figure 18). Although not statistically

significant, all other months showed an increasing trend (Figure 19), with the

50

exceptions of March, April, and June, which showed decreasing trends. An
analysis of flood frequency (EFCS Group 5) revealed the frequency of large
floods to be decreasing (p=0.05) over the period of record (Figure 20).
Median/Percentiles

Analyses of the magnitude and duration of annual extreme water
conditions (lHA Group 2) revealed upward trends for 1-day (p<0.01), 3-day
(p=0.025), 7-day (p=0.025), 30-day (p=0.01), and 90-day (p=0.05) minimum
flows (Figures 21-25). However, no significant trends were detected for maximum
flows. The timing of annual extreme water conditions (lHA Group 3) indicate a
downward trend (p=0.025) in the timing of minimum flows, which suggests
minimum flows are occurring at an earlier Julian date (Figure 26). The rate and
frequency of water condition changes (lHA Group 5) indicate the number of
hydrologic reversals, which is the number of times per year mean daily discharge
shifts from a rising stage to a falling stage or vice-versa, has decreased (p<0.01)
over time (Figure 27). The EFC results suggest the frequency of extreme low
flows has also decreased (p<0.01) over time (Figure 28). Mean annual flow data
were graphed and visually assessed; however, no obvious trends or Changes in
hydrology were observed (Figure 29).
Water Temperature Model

Adjusted r2 values indicated that 86 to 93% of the variation in water
temperature values was explained by the fitted model for all six sites (p<0.001 for
each; Table 9). Plots of the residuals showed no apparent trend, which indicated

that the linear model was appropriate. A scatterplot of the data (predicted vs.

51

known) showed a linear relationship because the data points fell around a
straight line and there were no evident outliers. This suggests that regression
assumptions were met and the data are linearly related. Residual plot and
scatterplot results from the Rifle River at Melita Road, which had one of the lower
adjusted r 2 values (0.89), are shown in Figures 5 and 6.

Based on actual water temperature data, the months of July and August
were found to be exceeding temperatures optimal for growth and survival of
salmonids (206°C; Dexter and O’Neal 2004) on all mainstream sites and the
West Branch (Figures 7 and 8). Predicted mean daily water temperature values
for Prior Creek appeared irregular and deviated from the overall trend of the
other tributaries. After the model was run, Prior Creek values appeared to be
hindering the models predictive ability on the mainstream and this node was
eliminated, which improved the model’s precision.

Graphical display of predicted and measured water temperatures over
time illustrate that predicted temperatures from the calibrated model follow the
general pattern of real-world measurements for 1997 (Figure 30). The model
tends to over-predict in late winter and early spring, under-predict in the summer,
and is best in late fall and early winter. Based on residuals of the calibrated
model, performance was optimal (<1°C) 60.5% of the time, acceptable (<2°C)
21.4% of the time, marginal (2-4°C) 15.1%, and unacceptable (>4°C) 3% of the
year (Figure 31).

Graphical results of the 2000 dataset used in model validation suggest

that predicted temperatures follow the general trend of measured temperatures

52

over the year (Figure 30). The model tends to under-predict from late spring until
early fall, but appears to follow the trend Closely the rest of the year. Goodness-
of-fit results using the RMSE approximated the average model error to be 159°C
for 1997 and 164°C for 2000. Results from residuals plotted against observed
temperatures for the 1997 calibration dataset show that residuals increase as
temperature increases (Figure 31 ). This suggests that there is a linear bias to
the model (p<0.01). Based on residuals of the validated model, performance
was optimal (<1°C) 53.4% of the time, acceptable (<2°C) 27.1% of the time,
marginal (2-4°C) 15.2%, and unacceptable (>4°C) 3.3% of the year (Figure 31 ).
When divided seasonally, the spatiotemporal variability in the Rifle River’s
thermal regime becomes apparent. During winter, the river is slightly warmer in
the headwaters (rkm 59) and becomes cooler as it moves downstream (rkm 0;
Figure 32). This pattern continues on through early spring and then the regime
gradually shifts to being cooler in the headwaters and warmer downstream by
late spring (Figure 32). In both winter and spring, temperatures remain below
optimal recommendations for salmonids, but by late May the river has warmed
and favorable conditions exist throughout the system. In summer, optimal
temperatures are exceeded downstream of rkm 33 in late June through July, but
the entire mainstream remains within favorable range until early fall (Figure 33).
The river gradually shifts from being warmer downstream to being cooler and
drops below favorable conditions by mid-October (Figure 33). Overall, seasonal
Changes in temperature are more dramatic downstream of rkm 33 than at

upstream sites.

53

Based on temperature duration curves, the upper portion of the watershed
never exceeded optimal temperatures (Figure 34). In the middle and lower
sections, temperatures were exceeded approximately 9% of the time.
Temperatures were within optimal range for salmonid growth 13%, 17%, and
19% of the time for the upper, middle, and lower sections of the watershed,
respectively.

Under the global warming scenario, temperature patterns were similar to
the original simulation where warmer temperatures are upstream (rkm 59) and
cooler temperatures exist downstream (rkm 0; Figure 35). Again, the
temperature pattern gradually turns over through the spring and the river
becomes warmer downstream than upstream (Figure 35). In both winter and
spring, temperatures remain below optimal recommendations for salmonids until
late spring when favorable temperatures occur. In the summer, temperatures are
within optimal and preferred range above rkm 33 except for late June when they
are exceeded downstream (Figure 36). In the early fall, temperatures are
beneficial to salmonids throughout the mainstream. The river gradually turns
over throughout the fall, shifting from cooler to warmer conditions in the
headwaters relative to downstream sites. By mid-October, temperatures drop
below favorable range. Overall, seasonal temperature changes are more
dramatic downstream of rkm 33 than at upstream sites.

Qualitative Habitat Assessment
Stream geometry measurements established that the upper sub-

watershed had a reach average wetted width of 13.41m and an average depth of

54

0.46m (Table 15). The middle portion average wetted width was 19.46m and
0.41 was the average depth. The lower section of the watershed had a mean
wetted width of 27.51 m and depth of 0.62m. Average stream wetted width in the
tributaries ranged between 1.6m and 7.6m. Average stream depth ranged from
0.07m to 0.42m. Wetted width and depth values coincide with an average daily
discharge of 142 cfs at the gage on Melita Road.

Results suggest that the sub-watersheds vary in the type of mesohabitat
available to aquatic organisms. The upper portion of the watershed is dominated
by the frequency of pool habitat with 49% of the habitat features assessed being
pools (Figure 37). In the middle portion of the watershed pool habitat (48%) also
occurred with higher frequency than other habitat features assessed. However,
in the middle section there are fewer riffles and more run habitat than the upper
section of the watershed. Lastly, the lower portion of the watershed had less
than the recommended 40% pool frequency (North Coast Regional Water Quality
Control Board 1999). The lower portion had relatively similar proportions of the
number of pool, riffle, and run habitat with 29%, 31 %, 40% respectively, although
run habitat occurred slightly more frequently.

Inventory results for the tributaries determined that pools were the
dominant mesohabitat (Figure 37). However, Little Klacking, South Eddy, Fritz,
and Townline creeks had less than 55% pool habitat, which is recommended for
streams <15m wide and <2% gradient (Washington Fish and Game Commission
1997; Table 15). There was no riffle habitat present in sample reaches of

Mayhue, Oyster, and Klacking creeks. Mayhue Creek appears to be channelized

55

and runs parallel to Rose City Road for about 1km. Oyster Creek (downstream
of Rose City Road) has an abundance of large woody debris, creating deep pool
habitat and making it difficult to walk. Klacking Creek appeared to be impacted
by the triple culvert on Peters Road, the placement of in-stream rock weirs by
landowners, and areas where land owners had mowed vegetation up to the
rivers edge.

Site quality results, which represents the amount of streambank erosion
observed, identified that the majority of the upper watershed was good quality
(66%; Figure 38; Table 16). In the middle watershed, both good (43%) and poor
(40%) site quality were observed most frequently. Conversely, the lower portion
of the mainstream was dominated by poor (58%) site quality. Results for the
tributaries determined that Klacking, Fritz, and Townline creeks had the greatest
amount of streambank erosion.

Fritz Creek had a claypan bottom and highly eroded banks. Townline Creek also
had a bedrock bottom with eroded banks and Townline Road where it crosses
Townline Creek had collapsed.

Results of the stream canopy assessment determined that all mainstream
sites had either a moderate or sparse canopy (Figure 39; Table 16). The
majority of the upper portion of the watershed inventoried had moderate (78%)
shading. In both the middle and lower portions of the watershed, there were
similar amounts of moderate (48% and 58% respectively) and sparse shading
(52% and 42% respectively). The majority of the tributaries had moderate

stream canopy (25-75% shading).

56

Substrate results established that the sites sampled in the upper watershed
consisted of 1% boulder, 40% cobbles, 35% gravel, and 24% sand (Figure 40;
Table 16). In the middle portion of the watershed there was 6% boulder, 22%
cobbles, 30% gravel, and 42% sand. The lower portion of the watershed had 2%
boulder, 35% cobbles, 25% gravel, and 38% sand. Overall, the three portions of
the mainstream had a small percent of boulder substrate and there was no silt
substrate present at any of the sites. The tributaries were primarily sand and
gravel bottomed. Dedrich Creek had a cobble bottom overlain with silt and there

were several old beaver dams upstream of Gerald Miller Road.

DISCUSSION

GIS Land Cover Analysis

It is important to note that circa 1800 and 1992 datasets were not
collected in the same manner or with the same accuracy; thus, percentage of
land cover and chi-square results may be imprecise (Table 4). Additionally,
differences in methodology used to derive 1992 and 2001 makes a direct change
analysis inappropriate (Homer et al. 2007). In late 2007, a product that will
facilitate direct change analysis will be available. At the current time, uncertainty
surrounds the total error when comparing the 1992 and 2001 NLCD.

As expected, results show that there is a difference in land use at the
watershed scale between Circa 1800 and 1992 vegetation datasets.
Anthropogenic influences have been Changing the landscape over the past 150

years by increasing deciduous forest, developed, and agricultural lands.

57

Meanwhile evergreen forest, mixed forest, and wetland habitats have declined
over the same period of time.

Surprisingly, land cover Changed at the watershed scale between 1992
and 2001. During this relatively short period (~10 years), grassland, developed,
and shrub lands have increased while other land use classes stayed relatively
stable or slightly decreased. It appears that the watershed experienced a surge
in human development during this time period, with developed land use
increasing from 0.8% to 9.2%. This is important because previous research has
documented negative biological impacts when urban land use is in excess of
10% of the catchment area (Wang et al. 1997). If watershed land use continues
on this trajectory over the next 10 years, it is predictable that there will be a
decline in aquatic habitats and shifts in fish community composition unless
sustainable development is fostered (both economic and ecological). In general,
the town of West Branch, which is adjacent to a major highway (l-75), has been
expanding and is an area of concern due to future development potential. AS a
result, partners have been working on the Ogemaw County Stormwater Project
to address and mitigate stormwater runoff problems before the West Branch and
subsequently the mainstream of the Rifle River are further impacted.

Results were also significant in 1992 and 2001 at the 90m, 60m, and 30m
riparian buffer scales. Grassland, developed, and shrub land use was not
different in the two years at all three spatial scales, which paralleled watershed
scale results. Evergreen forest and wetland land uses were similar to each other

in 1992 and 2001; deciduous forest, mixed forest, and agriculture were similar to

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each other at both the 90m and 60m buffer scale. These findings were contrary
to the watershed scale where there was no difference between these five land
use Classes. At the 30m scale, the main distinction was that deciduous land use
was similar to evergreen forest and wetlands, rather than mixed forest and
agricultural land use observed at the other spatial scales. Overall, these results
suggest that the impacts of development can be observed at all spatial scales,
including the 30m buffer. Additionally, different land use patterns emerged at
different spatial scales with the exception of the 90m and 60m buffers. These
results refuted my hypothesis that there is no significant difference in land use at
the watershed scale; however, there is a significant difference in land use at the
riparian scale (90m, 60m, and 30m buffers) due to a decrease in natural habitats.
Results from land use at the watershed scale versus the 90m riparian
buffer indicated that in Circa 1800 there was a difference in land cover at these
two spatial scales. The primary distinction is that almost half (46%) of the
riparian zone is comprised of wetland habitat, which is approximately twice the
amount present at the watershed scale (23.6%). Conversely, watershed scale
land use in 2001 is not different from the 90m or the 60m riparian zone;
therefore, land use within the 90m riparian zone is reflective of watershed scale
land cover. It is not until the 30m buffer that there is a difference in land use,
again between wetlands and all other land use classes. Although the percentage
of wetlands has decreased over time, they comprise nearly twice the amount of
land in the 30m riparian zone (38%) compared to the watershed scale (17.5%).

The circa 1800 dataset is a useful guide for comparing current land use patterns

59

to historic conditions, facilitating our understanding of how and where human
activities are changing the landscape. These results suggests that land use
similarities between the watershed scale, 90m buffer, and 60m buffer may be the
result of human encroachment upon the riparian zone and our ability to
homogenize the landscape. However, it appears that the 30m riparian buffer is
still relatively intact either as a byproduct of the protections afforded under
Michigan Natural River Program’s zoning ordinances or simply because it is the
last portion of the watershed to reflect larger scale changes.

The outcome of the sub-watershed analysis determined that, based on
2001 data, there is not a significant difference in land use in the upper, middle,
and lower portions of the watershed. This was an unexpected, but a valuable
finding as the purpose of this analysis was to identify priority areas for
rehabilitation and protection. Based on land cover analysis, future protection and
rehabilitation efforts can be implemented throughout the watershed rather than
having a narrow geographic focus. Most importantly, efforts should be focused
on preserving the integrity of the 30m riparian zone.
Hydrologic Analysis

Of the 67 measures of hydrologic regime trend analysis, 11 were found to
have changed over time. Mean monthly flows for August have increased over
time from approximately 165 to 190 cfs. August flows typically represent annual
low flows (i.e., base-flows), thus a rising trend suggests that base-flow has
increased over time. Annual minimum flows (1, 3, 7, 30, and 90-day means) also

indicated an upward trend while high flows have remained stable, suggesting that

60

the hydrologic regime has actually become more stable over time. The
frequency of extreme low flows is declining, which also maintains that there has
been an increase in base-flows as the result of more ground water entering the
system.

Results show that the date of minimum flow is occurring earlier in the
season, shifting the timing of annual minimum flow from early September to early
August. Based on discharge data, the winter of 2003 was very dry and the
annual minimum flow took place in March. Even with the 2003 data omitted from
analysis, the trend is still Significant. The number of hydrologic reversals has
decreased over the period of record, falling from approximately 120 to 88
reversals per year. This number represents how frequently mean daily discharge
shifts from a rising stage to a falling stage or vice-versa. Based on these results,
there is less year-to-year variability, which may be the result of changes in the
frequency and duration of precipitation events occurring within the basin.

The frequency of large floods (>3330 cfs) has been decreasing over the
last 69 years with the most recent large flood in 1989 at 3719 cfs. Based on
these results, it is difficult to determine if this trend is important in ecological
terms as it may simply be a 50-year flood event. Large flood events are
important for transporting sediment, flushing fine particles, and shaping the
stream channel. Large flood frequency information is mainly used for the design
of road-stream croSsings such as bridges, culverts, and spillways (Gordon et al.
2004). This information is important for the future design of fish-friendly culverts

that do not interfere with the watershed’s natural hydrologic regime.

61

 

I had hypothesized that dominant watershed hydrologic processes have
Changed over time, resulting in reduced storage capacity and increased runoff
due to loss of natural habitats, particularly in the riparian zone. The rationale
behind this hypothesis was that the type and abundance of vegetation on the
landscape can alter the storage capacity and ultimately the base-flow conditions
of a groundwater driven system like the Rifle River. However, the results of my
hydrologic analysis discussed above do not support this hypothesis.

Overall, the results of this study indicate that the Rifle River has become
hydrologically more stable over time. Large-scale drivers and processes, such
as land use and Climate, may be associated with the observed hydrologic
Changes. Based on the results of the GIS analysis, vegetation within the
watershed has also changed over time; however, the percentage of natural
habitats has stayed relatively stable between 1992 and 2001 at all spatial scales
and the 30m riparian buffer has retained the highest percentage of natural land
cover. In addition to land cover, an increase in precipitation has been
documented over the last century (Allan et al. 2004; USEPA 2000). If the
storage capacity or amount of available ground-water is fairly stable, an increase
in precipitation also has the ability to increase base-flows because the amount of
water recharging the ground-water is greater. Another factor that may be
influencing higher base-flow conditions is the reduction in agricultural land use,
which historically used tiles to drain wetlands for food production. Drain tiles are
designed to move water away from the land and into artificial ditches, resulting in

flashier hydrography. However, as old drain tiles are removed, become clogged,

62

or structurally fail due to aging, rainfall can infiltrate and percolate through the
ground rather than entering the system as surface water and the hydrologic
regime becomes more stable. In conclusion, stable base-flow conditions in this
basin suggest that groundwater recharge has not been altered by land use
changes and/or groundwater withdraws.
Water Temperature Model

The SNTEMP model is a useful tool for predicting stream temperatures.
The model can be used to identify spatial and temporal boundaries to salmonid
distribution, classify potential fish habitat, predict the impacts of Climate change,
and model Changes in stream morphology and riparian shading. Additionally, the
SNTEMP model is a tool that can be used to communicating with stakeholders to
understand the potential biological implications of management and landowner
actions.

Model calibration involves adjusting parameters that are estimated or not
representative of on-site conditions. The model was sensitive to Changes in air
temperature, and humidity. Measuring climate information on-site may improve
model performance. SNTEMP does not perform well near freezing and does not
allow negative water temperature values to be run. Negative values were
recorded in the field and were used in calculating regression values to fill missing
air temperature. It may improve model performance if all negative water
temperature values are Changed to zero prior to calculating regression values.

Additionally, discharge measurements could be measured in the field for the

63

mainstream above Melita Road and tributary contribution as these values were
estimated and could be a source of model error.

The SNTEMP model had poor predictive ability in the summer months and
estimated below actual measured temperatures. The model did not respond well
to large fluctuations in daily temperature. As a result, it tended to over- and
under-estimate these values. The influence of air temperature on water
temperature increases as a river widens due to reduced canopy or other
vegetative cover (Gordon et al. 2004); thus, daily air temperature fluctuations
likely influence water temperatures in the lower portion of the watershed more
strongly than the headwaters. Collecting and incorporating additional stream
geometry data may improve the model’s predictive ability.

The SNTEMP model predicted that the mainstream of the Rifle River did
not exceed lethal limits to salmonids; however, average model error for 1997 and
200 is 169°C and 164°C. Interpretation of model output, particularly in summer,
should consider this limitation. For example, when the average model error
(169°C) is added to mean weekly temperature predictions under normal climate
conditions, the stream below rkm 13 exceeds lethal temperature for Brook trout
(226°C) approximately 2% of the time. In addition, temperature predictions that
are presented as mean weekly temperature values may be moderating extreme
thermal conditions. Overall, more than 80% of predicted values in both 1997 and
2000 were within 2°C of measured values.

The longitudinal distribution of mean weekly temperature predictions for

the Rifle River indicate warmer winter and cooler summer temperatures in the

64

headwaters above rkm 33. This reflected the influence of groundwater in the
upper portion of the watershed. Small tributaries and groundwater can provide
cold-water refuge for salmonids when recommended temperatures are exceeded
on the mainstream (Dexter and O’Neal 2004). Based on MDNR data, the
tributaries are within favorable temperature range for salmonids, except a few
days in summer when the West Branch exceeded recommended values.
Qualitative Habitat Assessment

The process of collecting in-stream habitat data was essential for
understanding the physical and biological Characteristics of the watershed. In
addition, it provided the opportunity to gain in-depth knowledge about the
watershed that could not be gleaned from written documents; however, there
were some limitations to the data collection techniques employed. One limitation
was that riffle and run habitat units tended to be very short in the small tributaries
and l was unable to record all of these units due to the accuracy of the GPS unit
(2-5m). Additionally, there were often two different types of habitat units within
the same cross section of the creek (e.g., pool and run) due to LWD that created
pool habitat within or adjacent to other habitat units. Only the dominant habitat
unit was recorded and l was not able to capture the complexity of this
mesohabitat. The complexity of mesohabitat also made it difficult to Clearly
define the type of habitat to record and other field personnel may record
mesohabitat differently. Finally, it was difficult to quantify and summarize overall
site quality using this mixture of qualitative assessment methods as they

collectively have not been implemented, tested, and established in the literature.

65

There are a variety of techniques established in the literature to inventory
the abundance, type, distribution, and quality of habitat. One example is the
Basinwide Visual Estimation Technique (BVET), which is used by the US. Forest
Service (Dolloff et al. 1993). This approach involves a visual or qualitative
assessment of habitat and includes taking quantitative measurements at pre-
selected intervals to calibrate and correct for estimation bias. However, this
technique requires more time and personnel to accomplish. The approach used
in this study allowed me to assess a much larger portion of the watershed with
minimal assistance.

Two problematic culverts (perched and sediment loading) were identified
and photographed in the process of collecting data (Appendix B). This
information was passed along to agency personnel who are working to remove
barriers to fish migration in the watershed. The double culvert on Prior Creek at
Peters Road is not a high priority culvert for future replacement. The creek
appears of good quality at the road; however, there is a beaver pond not far
upstream. There is a road on Mansfield Creek located just upstream from the
mouth that appears to be on private property. This road has a poorly placed
culvert that has carving out a huge hole and cut away the banks.

Results of the qualitative habitat assessment suggest that the lower
portion of the mainstream has less than the recommended pool frequency and
streambank erosion increases as the mainstream flows from the headwaters to
the mouth. The mainstream has sparse to moderate shading, which appears to

be a function of stream width. None of the sites assessed on the mainstream

66

were densely vegetated or devoid of vegetation. The middle portion of the
watershed has the greatest stream width due to a bedrock outcropping.
Substrate in the mainstream was primarily sand, gravel, and cobble. The
absence of silt substrate in mainstream samples suggests that substrate is not
limiting salmonid presence.

The tributaries assessed in this study were generally very cold, had
abundant large woody debris (LWD), and complex mesohabitat. Large woody
debris appears to have a strong influence on the smaller streams and creates an
abundance of pool habitat by scouring adjacent to the LWD. This resulted in
complex mesohabitat because pools were often adjacent to riffle or run habitat at
the same cross section of river. Results of the qualitative assessment identified
a few tributaries with problems (e.g., abundance of pool habitat, silt substrate)
and potential causes of those problems. Some of the potential causes include
Channelization, bedrock outcroppings, land owner activities, culverts, and beaver
dams.

CONCLUSION

Evaluation of Ecosystem Analysis and Management Recommendations

I conducted a science-based study of the Rifle River watershed using the
EAWS approach to characterize ecosystem processes and identify where
rehabilitation and protection efforts should be focused. To accomplish this
investigation I used documented and repeatable methods that produced
verifiable results and conclusions. Through this analysis I was able to describe

the watershed’s hydrologic Characteristics, land use Changes over time, predict

67

the thermal limits to salmonid distribution, and inventory in-stream habitat
features of the Rifle River watershed.

The study was conducted on multiple spatial scales because spatial and
temporal patterns of ecosystem processes emerge at different scales and these
interactions can be overlooked when only one scale is used. For example,
results of this study suggest base-flow has increased over the period of record
and land use Changes have occurred over time. If only land use had been
examined, I may have speculated that hydrology was being negatively impacted
(e.g., decrease in base-flow conditions) due to an increase in developed land use
and a decrease in natural habitats. Conversely, if only hydrology had been
examined, I may have speculated that significant land use changes had not
occurred; therefore, three spatial scales were used in this study.
Characterization and analysis of land use, hydrology, and the thermal regime
were conducted at the watershed level. The river scale was used to Characterize
land use adjacent within 30m, 60m, and 90m of the river. The qualitative in-
stream habitat assessment was conducted at the reach scale.

Analyzing land use at multiple spatial scales provided useful information
that can be passed along to watershed planners in similar physiographic regions.
The characterization process identified that there has been an increase in
developed land, grassland, and shrub land over the last 10 years (1992-2001) at
the watershed scale and river scale (90m, 60m, and 30m buffers). Currently,
only the 30m buffer has different land use from the watershed scale and it is

characterized by a decrease in mixed forest and agriculture and an increase in

68

developed land, grassland, and shrub land. Because land use appeared to be
different in the upper, middle, and lower sections of the river, another spatial
scale, the sub-watershed, was employed to determine if rehabilitation activities
should be focused in a specific region of the watershed. Surprisingly, there was
no difference in land use within these sub regions or within 30m of the river in
these sub regions. However, results did identify that within 30m of the river the
upper portion of the watershed has the greatest percent of wetlands, the middle
portion has the greatest percent of developed lands, and the lower portion has
the greatest percent of deciduous lands. This information is important Since
rehabilitation activities typically occur within 30m of the river and developed lands
are known to impact stream health. Completion of the Ogemaw County
Stormwater Project in the middle portion of the watershed is important for
minimizing the impacts of development on the West Branch and mainstream.
Additionally, local land planning efforts should focus on ensuring sustainable
development occurs in the watershed, particularly in the West Branch area.
Hydrologic analysis was conducted at the watershed level, using data
collected at a single location. Results from this analysis were very informative
because it suggests base flow has increased and the river has actually become
more stable over time. This is useful information because anecdotally the Rifle
River has a reputation for being a flashy system where water levels rise rapidly
during rain events. Flashy systems are often associated with increased
development on the landscape because impervious surfaces increase runoff and

reduce infiltration; however, some rivers are naturally flashy. Evaluation of both

69

hydrologic and land use data suggests that the Rifle River is not a flashy system.
County road commission personnel who engineer and design road-stream
crossings should be informed of an increase in base-flow to ensure that
appropriate culvert size is selected to ensure fish passage.

Water temperature modeling was conducted at the watershed scale, using
data collected at multiple sites throughout the watershed. The thermal regime of
the Rifle River does not appear to be limiting salmonid distribution. Water
temperatures above 20°C occur less than 5% of the time at Melita Road (rkm 0);
however, the model tended to under-predict in the summer when lethal
temperatures typically occur. The upper portion of the mainstream (above rkm
33) provides cooler summer temperatures and warmer winter temperatures that
are not optimal for growth, but may function as thermal refuge. Under the global
warming scenario, maximum temperatures would stay relatively stable and
minimum temperature would increase. To improve model performance, I would
recommend collecting water temperature data throughout the year. Winter data
was typically lacking and regression values had to be used in model
development. A study on local climate data would also be useful, as the nearest
weather station did not reflect local conditions and impacted model performance.
Othenivise, current temperatures do not appear to be limiting fish distribution;
however, the type of in-stream habitat may be as stream morphology may be.

The in-stream qualitative habitat inventory was conducted at the reach
scale. Although it was a general inventory, it provided useful information

regarding the quality of potential fish habitat in the mainstream and tributaries.

70

The information was also useful for understanding the impacts of riparian
vegetation removal, beaver dams, and large woody debris. Due to the coarse
scale of this approach, it is not recommended as a monitoring tool. Future
habitat monitoring should include depth measurements of pool, riffle and run
features as individuals who conduct surveys may classify habitat units differently.
By collecting depth measurements this would provide sufficient information to
detect changes over time. In addition, Characteristics such as pool depth are
more informative when evaluating the quality and quantity of habitat available to
fish and life stage of interest.

As a result of studying these processes at multiple spatial scales, more
useful information emerged that can be used to guide land use planning and
rehabilitation efforts designed to protect valuable fish and wildlife resources. For
example, changes in hydrology may be increasing bank erosion as the stream
channel adjusts to new base-flow conditions. The streams ability to transport
sediment should be further studied and the results used to set quantitative
objectives. Understanding watershed processes lays the foundation for a
science-based rather than opportunistic approach to fisheries and aquatic habitat
protection and rehabilitation. It is important that the RRWRC set Clear and
quantitative objectives based on SCientifiC findings rather than prioritizing projects
based on local interests or land owner need since this type of opportunistic
project selection may results in short-term fixes that fail to meet long term
fisheries objectives. As a byproduct of gathering and interpreting ecosystem

information, management actions become more predictable because decisions

71

are based on an understanding of watershed interactions. Correspondingly, this
improves accountability through quantifiable results that can be easily
communicated to partners.

The EAWS approach can be used to accomplish the goals of the National
Fish Habitat Action Plan because it is landscape focused, science-based, and is
focused on habitat rather than species. It is not intended to produce a final
watershed management plan, but rather it is intended to function as an adaptive
approach to understanding complex watershed processes. Finally, this process
can be easily incorporated into existing approaches such as the Michigan DNR
river assessments.

The current rehabilitation strategy or goal of the Rifle River Watershed
Restoration Committee (RRWRC) is to preserve the natural condition of the river.
The primary way it has accomplished this goal is by reducing sediment delivery
into the river to protect water quality, fish habitat, and aesthetic attributes of the
system. There is limited information on historic rehabilitation efforts in the Rifle
River watershed due to a lack of documentation by past watershed restoration
committee members. To address this need, I worked with Jim Hergott (Saginaw
Bay RC&D) during the summer of 2005 to inventory the committee’s most recent
rehabilitation efforts (1998-2005) and provide a historical record for future
resource managers. Starting in 1995, a total of 370 eroding streambanks were
identified on the mainstream and West Branch. Of those sites, 134 projects had
been completed and they were all located on the mainstream (Figure 41).

Treatments used to rehabilitate the river are focused on the riparian zone and

72

have consisted of tree revetments (68%), rock rip-rap (22%), and other structures
such as lunkers, log jams, shaping and seeding (10%). In general, projects
appear to be opportunistically chosen based on land owner needs and to control
streambank erosion.

In addition to the above mentioned recommendations, the RRWRC should
continue to focus rehabilitation efforts within the 30m riparian buffer, specifically
in middle and lower portions of the watershed where development and
streambank erosion, respectively, are greatest. Public education aimed at
ensuring Natural Rivers ordinances are not being violated through the removal of
riparian vegetation and discouragement of the placement of in-stream rock weirs
would also benefit fish habitat. Although there are drawbacks to taking an
opportunistic approach to rehabilitation, some project will need to be selected
based on land owner need in order to keep local public support and advocacy.
To ensure the hydrologic regime of the Rifle River remains unaltered, no dams or
barriers should be placed on the system that would alter the natural flow regime
or fish passage as these are both vital for the ecological health of the watershed.
Based on temperature modeling results under normal conditions, salmonid
habitat improvement projects should be focus on the area above rkm 33. Other
factors that may be limiting salmonid distribution, such as the quality and quantity
of in-stream habitat, should be further investigated. Monitoring of future
rehabilitation projects, especially tree revetments, is strongly encouraged to

evaluate treatment efficacy. The extensive use of tree revetments in this system

73

is unique and the effects of this type of bank stabilization are not well
documented in the scientific community.

In conclusion, EAWS appears to work best as an interagency approach,
rather than as a public approach. It is intended to guide teams of interagency
professionals in establishing baseline conditions to address management
questions specific to the watershed of interest. Using this existing knowledge,
future Changes can be detected and baseline data can be built upon to address
management objectives over time. It can also be incorporated into an existing
watershed management approach due to the flexible framework. The results of
the interagency team, whether positive or negative, should also be
communicated to the general public and other relevant stakeholders by an

appointed or selected individual.

74

Table 1. Comparison of watershed analysis and planning frameworks: Michigan

Department of Environmental Quality (MDEQ), Michigan Department of Natural

Resources (MDNR), and Ecosystem Analysis at the Watershed Scale (EAWS).

 

 

MDEQ MDNR EAWS
Approach Clean Water Act, River assessment Ecosystem
Section 319 analysis
Organization Grassroots Agency Multidisciplinary
teams
Focus Site specific; Water Agency findings Watershed
quality processes
Information Yes Uncertain Yes
Transfer
Science-based Opportunistic Yes Yes
Decision Support No Yes Yes
System Platform
Limitations Site specific; Citizen involvement; Time and labor
Opportunistic Summarized results intensive

 

 

75

Table 2. Rifle River Watershed fish community. Information based on MDNR

fish surveys for the lower Rifle River (Omer), and Michigan’s Wildlife Action Plan

(Eagle et al. 2005).

 

 

 

Family Common Name Scientific Name
Catostomidae white sucker Catostomus commersonii
northern hog sucker Hypentelium nigricans
black redhorse Moxostoma duquesnei
golden redhorse Moxostoma erythrurum
shorthead redhorse Moxostoma macro/epidotum
Centrarchidae rock bass Ambloplites rupestn's
green sunfish Lepomis cyanellus
pumpkinseed Lepomis gibbosus
bluegill Lepomis macrochirus
smallmouth bass Micropterus dolomieu
largemouth bass Micropterus salmoides
black crappie Poxomis nigromaculatus
Cottidae mottled sculpin Cottus bairdii
slimy sculpin Cottus cognatus
Cypn'nidae common carp Cyprinus carpio'
spotfin shiner Cyprinella spiloptera
common shiner Luxilus comutus
northern pearl dace Margan'scus nachtriebi
homyhead chub Nocomis biguttatus
blacknose shiner Notropis hetero/epis
sand shiner Notropis stramineus
bluntnose minnow Pimephales notatus
longnose dace Rhinichthys cataractae
western blacknose dace Rhinichthys obtusus
creek chub Semotilus atromaculatus
brassy minnow Hybognathus hankinsoni
finescale dace Phoxinus neogaeus
Esocidae northern pike Esox lucius
Gasterosteidae Brook (five-spined) stickleback Culaea inconstans
lctaluridae black bullhead Ameiurus melas
brown bullhead Ameiurus nebulosus
stonecat Noturus flavus
Percidae rainbow darter Etheostoma caeruleum
johnny darter Etheostoma nigrum
channel darter Percina cope/andi
least darter Etheostoma microperca
yellow perch Perca flavescens
northern log perch Percina caprodes semifasciata
blackside darter Percina maculate
walleye Sander vitreus
Petromyzontidae sea lamprey Petromyzon man'nus*
Salmonidae rainbow trout Oncorhynchus mykiss"
brown trout Salmo trutta”
brook trout Salvelinus fontinalis
cisco Coregonus arfedi
Umbridae central mudminnow Umbra limi
*non-native

76

Table 3. Federal and state listed threatened, endangered, proposed, and
candidate species in the Rifle River Watershed ID 4080101 30 1-18 (Michigan

Natural Features Inventory 2006).

 

 

 

Scientific Name Common Name Federal State
Status Status
Clemmys insculpta Wood Turtle SC
Gavia immer Common Loon T
Haliaeetus Bald Eagle LT,PDL T
Ieucocephalus
Alasmidonta viridis Slippershell Mussel SC
Buteo lineatus Red-shouldered Hawk T
Dalibarda repens False-violet T
Pandion haliaetus Osprey T
Appalachina sayanus Spike-lip Crater SC
Emys blandingii Blanding’s Turtle SC
Northern fen Alkaline Shrub/herb Fen, Upper
Midwest Type
Dendroica kirtlandii Kirtland's Warbler LE E
Opuntia fragilis Fragile Prickly-pear E
Percina copelandi Channel Darter E
Great Blue Heron Great Blue Heron Rookery
Rookery
Merolonche dolli Doll's Merolonche SC
Dentaria maxima Large Toothwort T
State Ms

 

E = Endangered
SC = Special Concern
T = Threatened

Federal Status

LE = Listed Endangered
LT = Listed Threatened
PDL = Proposed Delisted

77

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78

Table 5. Land cover Classes present in circa 1800, 1992, and 2001 datasets for

the Rifle River watershed and simplified land cover classification schemes used

in land cover analysis.

 

Land Cover Classes

Simplified Land Cover Classes

 

Circa 1800 MiG_D_L_*

SPPNP’WPSPN"

A—La—l—k
CON-PO

. Lake/River

Aspen-Birch Forest

Hemlock-White Pine Forest
Jack-Pine Forest

White Pine-Red Pine Forest
Beech-Sugar Maple-Hemlock Forest
Black Ash

Cedar Swamp

Mixed Conifer Swamp

. Mixed Hardwood Swamp

. Muskeg/Bog

. Shrub Swamp/Emergent Marsh
. Oak Barrens

14.

Pine Barrens

1992 NLCD"

999°5‘9’9‘PS'9N.‘

Open Water

Deciduous Forest

Evergreen Forest

Mixed Forest

Woody Wetlands

Emergent Herbaceous Wetlands
Grasslands/Herbaceous

Low Intensity Residential

High Intensity Residential

79

Open Water

Deciduous Forest

Coniferous Forest

Coniferous Forest

Coniferous Forest

Mixed Forest

Woody Wetlands

Woody Wetlands

Woody Wetlands

Woody Wetlands

Woody Wetlands

Emergent Herbaceous Wetlands
Grasslands/Herbaceous/Savannah
Grasslands/Herbaceous/Savannah

Open Water

Deciduous Forest

Evergreen Forest

Mixed Forest

Woody Wetlands

Emergent Herbaceous Wetlands
Grasslands/Herbaceous/Savannah
Developed

Developed

Table 5 (Continued).

 

 

 

Land Cover Classes Simplified Land Cover Classes
1992 NL CD (Continued)

10. Commercial/lndustrialfl'ransportation Developed

11. Quarries/Strip Mines/Gravel Pits Miscellaneous Unnatural

12. Transitional Miscellaneous Unnatural

13. Pasture/Hay ' Agriculture

14. Row Crops Agriculture

15. Urban/Recreational Grasses Miscellaneous Unnatural

2001 NLCD"

1. Open Water Open Water

2. Deciduous Forest Deciduous Forest

3. Evergreen Forest Evergreen Forest

4. Mixed Forest Mixed Forest

5. Woody Wetlands Woody Wetlands

6. Emergent Herbaceous Wetlands Emergent Herbaceous Wetlands
7. Grasslands/Herbaceous Grasslands/Herbaceous/Savannah
8. Developed-Open Space Developed

9. Developed-Low Intensity Developed

10. Developed-Medium Intensity Developed

11. Developed-High Intensity Developed

12. Barren Land Miscellaneous Unnatural

13. Pasture/Hay Agriculture

14. Cultivated Crops Agriculture

15. Shrub/Scrub Shrub/Scrub

 

'MiGDL = Michigan Geographic Data Library; “NLCD = National Land Cover Dataset

80

Table 6. Summary of hydrologic parameters used in Indicators of Hydrologic

Alteration (IHA) statistics (modified from The Nature Conservancy 2006).

 

lHA Statistics

Hydrologic Parameters

 

Gaga: Magnitude of
monthly water conditions
W: Magnitude and
duration of annual extreme

water conditions

Group 3: Timing of annual

extreme water conditions

QM: Frequency and
duration of high and low
pulses

M: Rate and
frequency of water

condition changes

Mean value for each calendar month
(Subtotal 12 parameters)
Annual minima and maxima for 1-day, 3-day, 7-day,
30-day, 90-day means; No. of zero-flow days; Base
flow index: 7-day minimum flow/mean flow for year
(Subtotal 12 parameters)
Julian date of each annual 1-day maximum and 1-
day minimum
(Subtotal 2 parameters)
No. of low and high pulses within each water year;
Median duration of low and high pulses (days)
(Subtotal 4 parameters)
Rise rates: median of all positive differences
between consecutive daily values; Fall rates:
median of all negative differences between
consecutive daily values; No. of hydrologic
reversals
(Subtotal 3 parameters)

Total 33 lHA Parameters

 

81

Table 7. Summary of hydrologic parameters used in Environmental Flow

Components (EFCS) statistics (modified from The Nature Conservancy 2006).

 

EFC Statistics

Hydrologic Parameters

 

Group 1: Monthly low flows

Group 2: Extreme low

flows

Group 3: High flow pulses

Group 4: Small floods

Group 5: Large floods

Median values of low flows during each calendar
month (Subtotal 12 parameters)
Frequency of extreme low flows during each water
year; Median values of extreme low flow event:
duration, peak flow*, and timing

(Subtotal 4 parameters)
Frequency of extreme high flows during each water
year; Median values of high flow pulse event:
duration, peak flow, timing, and rise and fall rates

(Subtotal 6 parameters)
Frequency of small floods during each water year;
Median values of small flood event: duration, peak
flow, timing, and rise and fall rates

(Subtotal 6 parameters)
Frequency of large floods during each water year;
Median values of large flood event: duration, peak
flow, timing, and rise and fall rates

(Subtotal 6 parameters)

Total 34 EFC Parameters

 

* minimum flow during event

82

Table 8. Data inputs required to build Stream Network TEMPerature model.

 

SNTEMP Data Input Requirements

 

Stream Geometry

Stream network definition

Latitude

Elevation

Distance from system reference point

Stream wetted width

Manning’s n

Stream shading

Ground temperature

Streambed thermal gradient
Meteorology

Weather station latitude, elevation, and mean annual air temperature

Mean daily air temperature

Mean daily wind speed

Mean daily relative humidity

Mean daily percent sunshine

Daily dust coefficient

Daily ground reflectivity
Hydrology and Time Period

Average daily discharge

Average daily stream temperature

Average daily lateral inflow temperature

First and last day of simulation time period

 

Source: (Bartholow 2000)

83

Table 9. Results from multiple linear regression, which was used to estimate

missing water temperature values for the mainstream and tributaries of the Rifle

 

 

River watershed.
Location Adjusted r 2

Rifle River at Melita Rd (F2, 852: 3531; p50.001) 0.8921
West Branch (F2, 1090: 4892; p50.001) 0.8996
Prior Creek (F2, 295: 935; p50.001) 0.8628
Klacking Creek (F2 994: 6747; p50.001) 0.9313
Houghton Creek (F2,1241= 6266; p50.001) 0.9098
Gamble Creek (F2, 909: 5590; p50.001) 0.9246

 

84

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86

Table 12. Tributaries the State of Michigan considers vital to the protection of the

Rifle River and were evaluated during the qualitative habitat assessment.

 

 

Tributary Name Total Length

Km
1. Vaughn Creek (source to Gamble Creek) 5.0
2. Gamble Creek (source to Mallard Pond) 4.3
3. Oyster Creek (Oyster Road to Mallard Pond) 6.4
4. Mayhue Creek (source to Oyster Creek) 4.8
5. Houghton Creek (source to Rifle River) 12.6
6. Wilkins Creek (source to Houghton Creek) 10.5
7. Prior Creek (source to Rifle River) 10.5
8. Little Klacking Creek (source to Klacking Creek) 4.5
9. Klacking Creek (source in Foose Swamp to Rifle River) 8.4
10. Fritz Creek (Fritz Road to Rifle River) 1.0
11. Dedrich Creek (source in Dedrich Swamp to Rifle River) 3.5
12. West Branch (outfall of Flowage Lake to Rifle River) 16.3
13. North Eddy Creek (source to South Eddy Creek) 5.8
14. South Eddy Creek (source to North Eddy Creek) 8.0
15. Mansfield Creek (source to Rifle River) 9.3
16. Townline Creek (source to Rifle River) 3.2

 

Source: (MDNR 2002)

Note: Silver Creek was also listed, but was not evaluated due to poor GPS

satellite reception that resulted from topography.

87

Table 13. Mesohabitat data collected at each site during the qualitative habitat

assessment of the Rifle River watershed.

 

Parameter

Description

 

1) Reach name

2) Habitat feature

3) Location

Feature Characteristics

4) Substrate

5) Primary riparian vegetation
6) Riparian vegetation density
7) Stream canopy

8) Site quality

Rifle River or tributary name
Pool, riffle, or run

Latitude/Longitude

Indicator

ln-stream habitat structure, upstream
conditions, hydrology

Hydrology, in-stream habitat, land use
Hydrology, in-stream habitat, land use
Water temperature and quality

Bank erosion (hydrology, land use,

soils)

 

88

Table 14. Feature characteristic categories used for qualitative habitat

assessment of the Rifle River watershed.

 

Feature Characteristics Categories

1) Substrate a Boulders: >256mm
Cobble: 64-256mm
Pebbles: 4-64mm
Granules: 2-4mm
Very coarse: 1.0-2.0mm
Coarse: 1/2 — 1.0mm
Medium sand: 1/4-1/2mm
Fine sand: 1/8-1/4mm
Very fine sand: 1/16—1/8mm

Silt: <1/16mm
2) Primary riparian Herb-Low (s30cm)
vegetation b Herb-Mixed

Herb-Tall (>30cm)
Shrubs-Low (51 m)
Shrubs-Mixed
Shrubs-Tall (>1 m)
Trees-Deciduous
Trees-Evergreen

Trees-Mixed
3) Riparian vegetation Dense: >75%
density Moderate: 25-75%
Sparse: <25%
None
4) Stream canopy ° Dense: >75% shade

Moderate: 25-75% shade
Sparse: <25% shade
None

5) Site quality d Good: <25% bank erosion
Fair: 25-50% bank erosion
Poor: >50% erosion
Sand-gage © 1984 by W.F. McCollough
Modified from Bain and Stevenson 1999
c Modified from USDA Stream Visual Assessment Protocol (USDA-NRCS 1998)
Modified from Qualitative Habitat Evaluation Index (Ohio State University 2007)

0'0

0.

89

Table 15. Selected habitat variables for sub-watersheds (n=9) and tributaries

(n=3) of the Rifle River.

Wetted Width Depth (m) Percent Habitat Area

 

§ite (m) Average Average Pool Riffle Rtm
Upper 13.4 0.46 49% 23% 28%
Mayhue Creek 2.3 0.30 97% 0% 3%
Oyster Creek 2.3 0.24 71% 0% 29%
Gamble Creek 4.9 0.29 57% 4% 39%
Vaughn Creek 2.7 0.30 74% 2% 24%
Wilkins Creek 5.7 0.41 83% 13% 4%
Houghton Creek 6.6 0.35 79% 10% 12%
Prior Creek 6.2 0.12 71% 9% 21 %
Little Klacking Creek 2.8 0.20 50% 18% 32%
Klacking Creek 3.7 0.39 74% 0% 26%
Dedrich Creek 1.6 0.07 56% 32% 12%
Middle 19.5 0.41 48% 15% 37%
West Branch 7.6 0.42 65% 15% 21 %
North Eddy Creek 2.8 0.11 67% 2% 32%
South Eddy Creek 3.1 0.18 46% 7% 47%
Lower 27.5 0.62 29% 31% 40%
Fritz Creek 2.3 0.08 47% 20% 33%
Townline Creek 1.9 0.11 50% 9% 41%

90

Table 16. Results of qualitative habitat assessment for sub-watersheds of the

 

 

Rifle River.

Variable Upper Middle Lower

Riparian Canopy
>75%
25—75% 78% 48% 58%
<25o/o 22% 52% 420/0
0%

Riparian Vegetation
Herb — Low (<=30cm) 4% 14% 3%
Herb — Mixed 1% 2%
Herb — Tall (>30cm) 5% 33% 15%
Shrubs — Low (<=1 m)
Shrubs — Mixed
Shrubs — Tall (>1 m) 18% 2% 3%
Trees — Deciduous 38% 34% 41%
Trees — Evergreen 2% 2% 6%
Trees — Mixed 32% 15% 32%

Riparian Density
Dense (>75% native) 40% 30% 49%
Moderate ( 25-75%) 54% 61% 41%
Sparse (<25%) 6% 8% 10%
None (0%)

Substrate
Boulders (>256mm) 1% 6% 2%
Coarse: (1 /2-1 .0mm) 3% 15% 2%
Cobbles (64-256mm) 40% 22% 35%
Fine sand (1/8-1/4mm)
Granules (2-4mm) 1% 6% 2%
Med Sand (1/4-1/2mm) 1% 24%
Pebbles (4-64mm) 34% 27% 25%
Silt (<1/16mm)
Vcoarse (1 -2mm) 19% 27% 33%
Vf sand (1/16-1/8mm)

Site Quality
Fair (10-25% bank erosion) 22% 16% 27%
Good (<10% bank erosion) 66% 43% 15%
Poor (>25% bank erosion) 12% 40% 58%

91

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Ogemaw County

Arenac County

Saginaw Bay

Figure 2. Location and map of the Rifle River watershed in northeastern-lower

Michigan.

93

Headwaters of Rifle River (60 Km)

Houghton Cr (59.1 Km)

 

‘— Ranch Bridge (59.0 Km)

Klacking Cr (47.2 Km)
‘— State Rd (44.8 Km)

West Branch of the ‘— M-55 (39,1 Km)
RifleLBixeLmJAKmL.

° USGS gage at Melita RdlM-70 (0.0 Km)

@519

 

iauuqu maN

Figure 3. Composite stream network of Rifle River watershed from the

headwaters to the mouth.

94

800 *-

 

- - fl — -1997
700 ‘ / \
55 i / \ —2000
3 600 ~~ / \
GD - - - -
9 I / \ 11 Year Avg
2 500 .-
0 .
.2 !
0 400 i
>
.3.
g 300 -f
2
F. 200 i
0 I
2 :
100
0 ' ‘ T """"" 1
'\ \. \. A 0

Date

Figure 4. Mean monthly discharge at Melita Road (M-70) for calendar years
1997, 2000, and the 11 year average (1995-2005) in the Rifle River Watershed,

Michigan.

95

o ’ \
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d — d

_
5050
Jr

-15 ‘

_fl30_m¢m

-20

35 40 45 50 55 60 65 7O 75 80
Mean Daily Water Temp (°F)

30

Figure 5. Residual plot from multiple linear regression for Rifle River at Melita

Road.

96

0| 0') \l 0)
O O O O

Melita predicted
A
O

 

30 ~
20
10 Adjusted r2 = 0.89
O - ‘ #77 ' 9 ~ ‘ *7*' t > '7 ., 777‘ 77w
30 4o 50 so 70 so

Melita known

Figure 6. Scatterplot of predicted value from multiple linear regression vs.

known values for Rifle River at Melita Road.

97

25.0 :
, it: .i
3 . Maximum (Dexter and O’Neal 2004)
8 20.0“- h '2' J ' 1“
L H, j . ,2 3.,“ ‘ Preferred temperature range
2 g , Q . (Wehrlyetal.1999)
3 150~ ‘ '4‘ 1
E ' '4 1,. ‘3 1'?
fl) ‘ l A 1: ‘
a. t '
g .
I- 10.0: . g .' _ —Ranch Bridge
3 . 1 . - Selkirk
'5 l" —- 'Melita Rd
3

 

 

 

Figure 7. Mean daily water temperature collected by the Michigan DNR from
February 1997 to January 1998 at the upper (Ranch Bridge, rkm 58.7), middle
(State Road, rkm 44.8) and lower (Melita Road, rkm 0.0) sections of the Rifle
River watershed, Michigan. The preferred temperature range (15-19°C) is based
on the mean temperature measured at sites where Wehrly et al. (1999) found
peak densities of brook, brown, and rainbow trout. Maximum is the temperature
(206°C) that should not be exceeded for optimal growth and survival of trout

(Dexter and O’Neal 2004).

98

   
 

Maximum (Dexter and O'Neal 2004)

Preferred temperature range

    
 
  

g (Wehrly et al. 1999)
2 15.0 ‘
3
a
E .
3. ”0 ‘ —Rifle(Melita Rd)
5 » ~West Branch
'- 7.0
3 , - Klacking
E Ho hton
ug

; 3.0

> - -Ganble

l --—Pn'or

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1 1 1 1 1 1 1 s s s s s
“We 3&3 N93 599.9 0&5 $019 9&9 We “0.9% 9,319 We “do “(.9

Date

Figure 8. Mean daily water temperature collected by the Michigan DNR from
late June 1997 to early June 1998 for the West Branch Rifle River, Klacking
Creek, Houghton Creek, Gamble Creek, and the lower section of the mainstream
(Melita Road). The preferred temperature range (15-19°C) is based on the mean
temperature measured at sites where Wehrly et al. (1999) found peak densities
of brook, brown, and rainbow trout. Maximum is the temperature (20.6°C) that
should not be exceeded for optimal growth and survival of trout (Dexter and

O’Neal 2004).

99

   
  

Upper Sub-watershed

Middle Sub-watershed

 

«(1 \
Lower Sub-watershed /

 

o = ln-stream data collection site

~ = River

 

 

 

Figure 9. ln-stream data collection sites within the Rifle River watershed,

Michigan.

100

m
V

 

 

v Percent Land Cover

CT

Percent Land Cover

 

 

0
v
A
()1
4_l

Percent Land Cover
N
O

5 _
0 _
E Open Water Deciduous Forest
1:] Evergreen Forest L—J Mixed Forest
a G. ' “1:...“ "‘ "nah E] Woody Wetlands
Emergent Herbaceous Wetlands D Developed
E Agriculture (hay/pasture/crops) D Shrub/Scmb

 

 

Figure 10. Land use results from GlS watershed scale analysis for a) circa

1800, b) 1992, and c) 2001 for the Rifle River watershed, Michigan.

101

ID
V

35-
304
25—
20—
15-

10]
5

04

Percent Land Cover

 

S

35-
30J
25-
20—
15‘
10—

5_

OJ

Percent Land Cover

 

 

E] Open Water E] Deciduous Forest
D Evergreen Forest [I Mixed Forest
Grassland/Herbaceous/Savannah D Woody Wetlands
fl Emergent Herbaceous Wetlands [:l Developed

D Agriculture (hay/pasturelcrops) D Shrub/Scrub

Figure 11. Results from GIS land use analysis within the 90m buffer for a) 1992

and b) 2001 for the Rifle River watershed, Michigan.

102

 

 

 

 

 

 

 

8)

as
>
O
o
'0
C
(U
_l
E
d)
8
a:
o.

b)
as
>
O
o
‘O
C
(D
.J
E
§
m
a

Open Water fl Deciduous Forest

D Evergreen Forest D Mixed Forest

Grassland/Herbaceous/Savannah DWoody Wetlands
a Emergent Herbaceous Wetlands E] Developed
3 Agriculture (hay/pasture/crops) D Shrub/Scrub

Figure 12. Results from GIS land use analysis within the 60m buffer for a) 1992

and b) 2001 for the Rifle River watershed, Michigan.

103

4o _.
35 l
30 .
25 a

 

 

 

 

 

Percent Land Cover
N
O

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

E] Open Water Deciduous Forest
D Evergreen Forest [:l Mixed Forest
D Grassland/Herbaceous/Savannah E] Woody Wetlands

Emergent Herbaceous Wetlands E] Developed
[3 Agriculture (hay/pasture/crops) [:1 Shrub/Scrub

Figure 13. Results from GIS land use analysis within the 30m buffer for a) 1992

and b) 2001 for the Rifle River watershed, Michigan.

104

a)

b

v

Percent Land Cover

Percent Land Cover

401

zsl

 

 

 

 

45 l r—

 

 

 

 

a Open Water Deciduous Forest
CI Evergreen Forest E] Mixed Forest
G.assla..d/l' * 'C_._.u.ah (:1 Woody Wetlands
fl Emergent Herbaceous Wetlands E] Developed
Agriculture (hay/pasture/crops) D Shrub/Scmb

 

Figure 14. Results from GIS land use analysis for circa 1800 at the a)

watershed scale and b) 90m buffer scale for the Rifle River watershed, Michigan.

105

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106

 

 

 

 

 

Figure 16. GIS sub-watershed delineation of the Rifle River watershed,
Michigan. Upper, middle, and lower sub-watersheds comprise 31 °/o, 30%, and

39% of the total watershed area respectively.

107

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Jero pue-l waxed

108

340 n ‘

 

3204 y = 0.49x — 794.1
R2 = 0.06

300“ F1, 67 = 4-21; 9:0-05

2801 "

260-

 

240 1
220 1

 

Discharge (cfs)

 

 

 

 

 

 

     

 

il’j‘rTf

. ....f.....-.-.. ....... ................ efi.........e..l
1938 1943 1949 1955 1961 1967 1973 1979 1985 1991 1997 2003
Date

Figure 18. Parametric analysis of monthly flows for August, Rifle River at Melita

Road.

109

 

800‘ Y =1.31x — 2283

750“ R2 = 0.05

700‘ F1.67 = 355; p=010
650«
600-
550

500- T1
4504
400.
3504
300*
250 ........ - - - --. l
200

 

Discharge (cfs)

 

 

 

 

 

 

 

 

Tf‘l'fff‘r‘r r IVIT

1933 1943

fifi ft

19499 1933 I 1961 1967’ ' 19731979

1935' ' '1'9'9'1' ‘ '1’9'9'7' ' 2003 '
Date

Figure 19. Parametric analysis of monthly flows for November, Rifle River at

Melita Road.

110

 

-—8
4:
%J

0-9‘ Y = -o.oox + 7.15
0.8 R2 = 0.06
F1, 67 = 4.59; p=0.05

 

 

 

 

Large Flood Frequency
0
A

 

 

 

 

 

 

 

 

 

 

 

 

19381943 1949 1955 1961 1967 1973 1979 1985 1991 1997 2003

Date

Figure 20. Parametric analysis of large flood frequency, Rifle River at Melita

Road.

111

 

165'? v = 0.24x — 342.5
160- R2 = 0.13
155 F1,67 = 9.86, p<0.01

150*
145*
140*
135*
130
125*
120
1151
110
105-

Discharge (cfs)

 

 

 

 

 

 

 

 

100;. - L- ..
19381943 1949 1955 1961

 

l

rrrrrrrrrrrrrrrr

1967 1973 1979 Y19857T1991r 1997 '2003f
Date

Figure 21. Annual 1-day minimum discharge for the Rifle River at Melita Road.

112

1% {1

165 Y = 0.19X - 236.4
160 R2 = 0.09
1554 F1, 67 = 6.24; p=0.025

150.
1454
140
135
130
125 "
120
115
110
105
1001:. .. ....... . .......................... .:J
1938 1943 1949 1955 1961 1967 1973 1979 1985 1991 1997 2003
Date

 

.....

 

,
0"
------
-
-no-

 

.-
........
u
C--

-0
.......

 

Discharge (cfs)

 

 

 

 

 

Figure 22. Annual 3—day minimum discharge for the Rifle River at Melita Road.

113

Y = 0.19x — 239.4
”0‘ R2 = 0.09

F1, 67 = 6.32; p=0.025

 

........

 

--
ooooo
------
p.
-0
--l'-

....

Discharge (cfs)

 

 

 

1101
105i.

 

 

 

+9... ..... T ........ Y.......r7vfj—.fi—f....vr.. ............ 3
19381943 1949 1955 1961 1967 1973 1979 1985 1991 1997 2003

Date

Figure 23. Annual 7-day minimum discharge for the Rifle River at Melita Road.

114

{.1

 

1901
185
1801
1751
170*
165’
160
155-
1501
1451

Discharge (cfs)

140

135
130
1251

120L

 

Y = 0.26X - 359.4
R2 = 0.10
F1.67 = 7.85; p=0.01

 

 

YYYYYYYY l' Y ' l 7

1938 1943YY1949Y'1955 1961

1967
Date

 

 

 

f

 

 

A

fTi'f‘r’fi'T J

1973 '1979"1985 1991"1997 2003

Figure 24. Annual 30-day minimum discharge for the Rifle River at Melita Road.

115

 

 

 

 

Discharge (cfs)

 

 

 

 

1938 1943 1949 1955* 1961 1967

Date

     

 

 

 

 

 

Y = 0.29X — 398
R2 = 0.06
F1,67 = 4.01; p=0.05 J

 

TfTYTfrTTTfi’TJ

1973 71979"1985rr1991r91997 2003

Figure 25. Annual 90-day minimum discharge for the Rifle River at Melita Road.

116

 

3605‘ i’

340* Y = -0.55X +1328
3201 R2 = 0.07
300. F1, 67 - 5.20, p—0.025

280«
260 ~.“ -
24o-

220~
200
180
160-
140
120~
1004

BOL' g
1938 1943 1949 1955 1961 1967 1973 1979 1985 1991 1997 2003

Date

 

 

 

 

 

   

Julian Day (1-366)

 

 

 

 

 

Figure 26. Date of minimum flow for the Rifle River at Melita Road.

117

160~ _
155, Y - -0.47x +1025

145 F1, 67 = 30.51; p<0.01

140
135
130-
125
120--.,
115
110‘
105
100
95
90’
85

I
TfrT T 1' rW—fYfi‘r‘rY Yrj’ rfTI I Yfir‘rT J

C. .....H........- ........ .......
1938 1943 1949 1955 1961 1967 1973 1979 1985 1991 1997 2003
Date

 

.....
.

 

.....
Q

-
N.—
‘-

Number of Hydrologic Reversals

 

‘-
.....
o
O

 

 

 

 

Figure 27. Number of hydrologic reversals for the Rifle River at Melita Road.

118

1; Y = -0.08X +158.4 _

,5 R2 = 0.18
14, F1.57 =14.51;p<0.01

131
12
11
10*
9

 

 

 

 

 

 

~~~~~
.....
.

 

Frequency of Extreme Low Flow

 

 

 

 

 

 

 

A 3
r rTTfiYjYVfi'T‘l—V’T

38 1943 '1949 1955551961 [1967 1973 1979 1985' 1991 19971'20035
Date

 

8.
7
6*
5
4
3
2-
1.
0
1

Figure 28. Frequency of extreme low flows for the Rifle River at Melita Road.

119

 

 

 

TillTlTTlTTlTTTTTTlTerfT TrTllIIIIWTTllllllllllllflllilllTTlllll’Tl

 

600

500

0 0 0
0

0 0
4 3 2
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100

 

0

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mom Twmm F
0mm thmm F
mom F-Nmm F
omm 795 F
hvm Tova F
g Tavm F
:5 Ftovm F
wmm Ffimm F

Water Year

Figure 29. Mean annual discharge for the Rifle River at Melita Road.

120

+ Measured -- Predicted

 

 

 

 

 

8)
30 7
A 25 7
SJ .
V 20 7 1.
g .1 t
10H _ . I 1
E 15 ‘3 '
a t
E 10 7 . , .
c 1 "
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5 1. 3 1‘
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\ \ \ \ \ \ \
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F 25 7
.0
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'3 5 . H1 ‘
. . a ,
O l’ 1 fl l l l l l l i
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\ \ \ \ \ \ \ \ \ \ \ \
‘— N ('0 ‘1‘ LO (0 l\ (I) O) O ‘— N
\— ‘— \—

Figure 30. SNTEMP daily calibration predictions and measured temperature
(°C) values from 1997 at Melita Road (0.1 rkm; figure a). SNTEMP daily
validation predictions and measured temperatures (°C) values from 2000 at

Melita Road (0.1 rkm) located in the Rifle River watershed, Michigan (figure b).

121

(D
V

Percent of Year

Mean Error (°C)

I 1997 2000

80
>4
Performance (°C)
8
6 o
O . 9.
4 ; ,
2 ‘0 . :9 ..
‘4’. o
O. .0. O .0 0. a . . .
0 : .i .. . .}~ .2. :0.:~... fig. 0.. .s..
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o 9 ’° 0 3 00...” °° . ’
-4 . 9. . ...} , o
o
o
—6 l , , 7 [ 7
0 5 10 15 20 25 30

Observed Mean Daily Temperature (°C)

Figure 31. SNTEMP model’s predictive performance for calibration (1997) and

validation (2000) datasets based on the percentage of time predictions exceeded

measured temperatures: <1 °C = optimal, <2°C = acceptable, 2-4°C = marginal,

and >4°C = unacceptable (figure a). Plot of model error (predicted-measured)

against measured water temperatures for 1997 calibration dataset to assess

simulation quality (figure b).

122

N
U!

N
O
L 4

 

Mean Weekly Temperature (°C)

 

 

—<>— 3-Dec I 10-Dec —a—— 17-Dec —0— 24-Dec —xe— 1-Jan
—+— 8-Jan —1— 15-Jan 22-Jan ------ 29—Jan —+— 5-Feb
—D— 12-Feb + 19-Feb - - -x--- 26-Feb

25 w

20 -

 

15L

l
x.-x---x--x--x-~><--X-~x--x--x—~x--x--x--x-.x---x..
X'~*-~x--x---x--x--x-.x..x.--x--x--x---x--x

    

Mean Weekly Temperature (°C)

 

10
5
0 " '7 l?" i "" ‘V ¥ 7* fl A '7i—'—’_—'-7
3 11 19 27 35 43 51 59
River Kilometer
—<>— 5-Mar I 12-Mar —a— 19—Mar —o— 26-Mar —)11— 2-Apr
_._ 9-Apr ,, %16-Apr 23-Apr ------ 30-Apr —0— 7-May

—o—14-May +21-May ---x---28-May

Figure 32. Mean weekly temperatures for the Rifle River watershed as predicted
by the SNTEMP model from the headwaters to Melita Road, Sterling, Michigan.
Model simulation conditions represent a) winter and b) spring temperatures.
Shaded area represents optimal growth temperatures for salmonids (Werhly et

al. 1999).

123

1»

Mean Weekly Temperature (°C)

0'
v

 

 

 

3 1 1 19 27 35 43 51 59
+ 4-Jun —-—— 1 1-Jun or 18-Jun -- -x- -- 25-Jun + 2-Jul
—o— 9-Jul ~1— 16-Jul 23-Jul - -1 30-Jul —o— 6-Aug

 

—o—13-Aug +20-Aug —o—27-Aug

 

 

 

 

 

 

 

 

Mean Weekly Temperature (°C)

25
15 977 ----------
m2==\
——————— _- \==_==!——-----
10 '
j ‘#+——+—+--+—-i\+—+—~r111111111fi1——+—fi+
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-A—A— ——————————————————————
I II
0 I '_ 1 f T —r — " ‘1.
3 11 19 27 35 43 51 59

River Kilometer
——o——— 3-Sep -+—- 10—Sep —a—-— 17-Sep -~-x- ~- 24-Sep -———x—— 1-Oct

—o— 8-Oct ——+— 15-Oct —-— 22-Oct - 29-Oct —<>— 5-Nov

—o—12—Nov —¢— 19-Nov —a— 26-Nov
Figure 33. Mean weekly temperatures for the Rifle River watershed as predicted
by the SNTEMP model from the headwaters to Melita Road, Sterling, Michigan.
Model simulation conditions represent a) summer and b) fall temperatures.

Shaded area represents optimal growth temperatures for salmonids (Werhly et

al. 1999).

124

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125

 

Mean Weekly Temperature (°C)

 

+3-Dec » I ~10—Dec —c—17-Dec ---x-- 24-Dec —x— 1-Jan
—o—8—Jan '1 15-Jan 22-Jan ..-- 29-Jan w ,0 ~ 5-Feb
—o—12-Feb —.-—1Q-Feb —o—26-Feb

 

 

 

Mean Weekly Temperature (°C)
a

 

0 ‘1 ’ t ’ 1 ’ ’ 1 7 v if r

1 9 17 25 33 41 49 57
River Kilometer
+ 5-Mar a 12-Mar —n— 19-Mar --x-- 26-Mar + 2-Apr
—o— 9—Apr 7 . , 16-Apr —-—— 23-Apr - 30-Apr —-o— 7-May
—o—— 14-May —*— 21-May —o— 28-May

Figure 35. Mean weekly temperatures for the Rifle River watershed as predicted
by the SNTEMP model from the headwaters to Melita Road, Sterling, Michigan.
Model simulation conditions represent a global increase of 27°C in mean annual
air temperature for a) winter and b) spring. Shaded area represents optimal

growth temperatures for salmonids (Werhly et al. 1999).

126

Mean Weekly Temperature (°C)

0'
V

Mean Weekly Temperature (°C)

30
25

- ,,,- , _ - :-;—.=‘ —' _- _'. o
‘ - _....,__,-' _
A A ‘--l I -'

‘ ‘ “'F:"

    

        
   
  

   

  

 

15 .
10 1
5 .

0 ' i ' ' 7 7' 7' ' l 7 7
1 9 17 25 33 41 49 57
—o—4-Jun 4—911-Jun +18-Jun ---x---25-Jun +2-Jul
—o—9-Jul —+—16—Jul ——23—Jul — 30-Jul o 6-Aug

—D—13-Aug —c—20-Aug —x—-27-Aug
30 J
25

 

 

 

 

 

 

1 9 17 25 33 41 49 57
River Kilometer

+ 3-Sep + 10-Sep + 17-Sep ---x- -- 24-Sep —x— 1-Oct

—o— 8-Oct —~ 15-Oct —— 22-Oct .4., 29-Oct *0» , 5-Nov

—o— 12-Nov —¢— 19-Nov —x— 26-Nov

Figure 36. Mean weekly temperatures for the Rifle River watershed as predicted

by the SNTEMP model from the headwaters to Melita Road, Sterling, Michigan.

Model simulation conditions represent a global increase of 27°C in mean annual

air temperature for a) summer and b) fall. Shaded area represents optimal

growth temperatures for salmonids (Werhly et al. 1999).

127

a) Pool 1:1 Riffle Run

 

Lower 1 ,1 l

 

i
l

 

Site

Middle 'j _ 7 f .;: .; l

 

 

Upper . . . , . l

 

 

1’71 1 1 1* 7+ 1 1' 1 1 1 . 3 7*71 1 1
0 10 20 30 4O 50 60 70 80 90 100
Percent Habitat

Pool l Riffle 1:] Run

0'
v

Percent Habitat

 

Site

Figure 37. Mesohabitat characteristics based on in-stream habitat assessment
of the Rifle River for a) segments of the mainstream Rifle River and b) tributaries

to the Rifle River.

128

a)
Fair (10-25% bank erosion) [:1 Good (<10% bank erosion)

1:] Poor (>25% bank erosion)

 

 

 

 

 

 

 

0 10 20 30 40 50 60 70 80 90 100

     

Percent Habitat
b)
Good (<10% bank erosion) Fair (10-25% bank erosion)
El Poor (>25% bank erosion)
E ' ’ : . “
N
:I:
‘5
§
0
:L
Q00 ®Q&d§\g&\6\ odonb‘prsty '\\Q %0\
$9 +9 9° $00 {6‘ 36‘ «0‘9 or:
\> $0 90 Q‘

Figure 38. Site quality (percent streambank erosion) based on in-stream habitat
assessment of the Rifle River for a) segments of the mainstream Rifle River and b)

tributaries to the Rifle River.

129

a) Moderate (25-75%) Sparse (<25%)

 

 

 

 

 

 

Lower ]
2 .
i7) Mlddle
Upper
0 10 20 30 40 50 60 70 80 90 100
Percent Habitat
b)

I 1 = Dense (>75%shade) 2 = Moderate (25-75%)
El 3 = Sparse (<25%) [:1 4 = None (0 shade)

Percent Habitat

 

Figure 39. Stream canopy results based on in-stream habitat assessment of a)

segments of the mainstream Rifle River and b) tributaries to the Rifle River.

130

a)

I Boulders: >256mm 1m Cobbles: 64-256mm
I III Gravel: 2-64mm El Sand: 0.06-2.0mm

 

Lower

 

 

 

 

 

 

%Middle
Upper
0 10 20 30 40 50 60 70 80 90 100
Percent Habitat
b) I Boulders: >256mm Cobbles: 64-256mm

El Gravel: 2-64mm El Sand: 0.06-2.0mm
ISilt: <1/16mm

l'_.‘

Percent Habitat

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 40. Substrate results based on in-stream habitat assessment of a)

segments of the mainstream Rifle River and b) tributaries to the Rifle River.

131

 

Figure 41. Rehabilitation sites (signified by dots) completed from 1998-2005

throughout the Rifle River watershed, Michigan.

132

APPENDIX A
Ecosystem Analysis at the Watershed Scale, Federal Guide for Watershed

Analysis Version 2.2, Revised August 1995, p. 3., p.12 (RIEC 1995).

Summary of the Six-Step Process
The process for conducting ecosystem analysis at the watershed scale has six
steps:

1. Characterization of the watershed. The purpose of step 1 is to identify the
dominant physical, biological, and human processes or features of the
watershed that affect ecosystem functions or conditions. The relationship
between these ecosystem elements and those occurring in the river basin
or province is established. When characterizing the watershed, teams
identify the most important land allocations, plan objectives, and
regulatory constraints that influence resource management in the
watershed. The watershed context is used to identify the primary
ecosystem elements needing more detailed analysis in subsequent steps.

2. Identification of issues and key questions. The purpose of step 2 is to
focus the analysis on the key elements of the ecosystem that are most
relevant to the management questions and objectives, human values, or
resource conditions within the watershed. The applicability of the core
questions and level of detail needed to address applicable core questions
is determined. Rationale for determining that a core question is not

applicable are documented. Additional topics and questions are identified

133

Appendix A (Continued)

based on issues relevant to the watershed. Key analysis questions are
formulated from indicators commonly used to measure or interpret the key
ecosystem elements.

. Description of current conditions. The purpose of this step is to develop
information (more detailed than the characterization in step 1) relevant to
the issues and key questions identified in step 2. The current range,
distribution, and condition of the relevant ecosystem elements are
documented.

. Description of reference conditions. The purpose of step 4 is to explain
how ecological conditions have changed over time as a result of human
influence and natural disturbances. A reference is developed for later
comparison with current conditions over the period that the system
evolved and with key management plan objectives.

. Synthesis and interpretation of information. The purpose fo step 5 is to
compare existing and reference conditions of specific ecosystem elements
and to explain significant differences, similarities, or trends and their
causes. The capability of the system to achieve key management plan
objectives is also evaluated.

. Recommendations. The purpose of this step is to bring the results of the
previous steps to conclusion, focusing on management recommendations
that are responsive to watershed processes identified in the analysis. By

documenting logical flow through the analysis, issues and key questions

134

Appendix A (Continued)

(from step 2) are linked with the step 5 synthesis and interpretation of
ecosystem understanding (from steps 1, 3, and 4). Monitoring activities are
identified that are responsive to the issues and key questions. Data gaps and

limitations of the analysis are also documented.

Core Topics

The core topics represent the major and common ecological elements,
and their relationships, in all watersheds. The topics are purposely broad and
general, as they encourage a watershed-level perspective of the system as
opposed to a site or project-level perspective. The purpose of the core topics is
to ensure that responsible officials and their teams adequately address the major
elements and their relationships in the watershed. The core anlysis topics help
ensure that analyses are sufficiently comprehensive to develop a basic
understanding of the watershed. The analysis team should demonstrate
understanding and knowledge of the basic ecological conditions, processes, and
interactions in the watershed by addressing the following core topics through the
six-step process:

. Erosion processes

- Hydrology

o Vegetation

. Stream channel

0 Water quality

. Species and habitats

. Human uses

135

Appendix B

 

Site: Oyster Creek at Rose City Road near Lupton, Michigan.

Problem: Culvert is inundated with silt.

136

Appendix B (Continued)

“: ran- -._,_ ' 9. '~ ‘ 1 1‘,“

 

Site: North Eddy Creek at Patricia Lane.

Problem: Perched culvert.

137

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