Erosional impact of hikers, horses, off-road bicycles, and motorcycles on mountain trails
by Joseph Paul Seney
A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in
Earth Sciences
Montana State University
© Copyright by Joseph Paul Seney (1991)
Abstract:
Little is known about the erosional impacts of hikers, horses, motorcycles and off-road bicycles on
mountain trails. The purpose of this investigation was to determine the relative impacts of different trail
uses with respect to water runoff and sediment yield. A total of 108 sample plots were examined on
existing trails in or near the Gallatin National Forest of southwestern Montana. A modified Meeuwig
drip-type rainfall simulator was used to reproduce natural rainstorm events. Treatments of 100 passes
were applied to each of the sample plots. This approach meant that 24 sample plots were established
for each treatment type (hiking, horseback riding, motorcycling, bicycling) in addition to 12 sample
plots for the control or null hypothesis case to represent two soil texture groupings (clay and sandy clay
or loam and sandy loam), two antecedent soil moisture classes (dry and prewetted), and two slope
gradients (0-6 percent and 8-21 percent), and three replications.
The results of this study demonstrate the interaction of topographic, soil, and geomorphic variables and
the difficulty of understanding natural processes on existing trails. None of the hypothesized
relationships between water runoff and slope, soil texture, antecedent soil moisture, trail roughness, and
soil resistance were statistically significant. However, the multiple regression results for sediment yield
were statistically significant. Five independent variables or cross-products explained 42% of the
variability in sediment yield when soil texture was used as a series of indicator variables. The addition
of a trail user as a second series of indicator variables explained an additional 28% of the variability in
sediment yield. Ten variables combined to explain 70% of the variability in sediment yield with simple
or combined variables incorporating soil texture (37%), slope (35%), user treatment (35%), and
accounting for the largest contributions. The use of multiple comparison tests clarify the roles of the
different trail users and in particular showed that horses and hikers (hooves and feet) made more
sediment available than wheels (motorcycles and off-road bicycles). This effect was most pronounced
on prewetted trails. EROSIONAL IMPACT OF HIKERS, HORSES, OFF-ROAD BICYCLES,
AND MOTORCYCLES ON MOUNTAIN TRAILS
I
by
Joseph Paul Seney
A thesis submitted in partial fulfillment
of the requirements for the degree
of
Master of Science
in
Earth Sciences
i .
MONTANA STATE UNIVERSITY
Bozeman, Montana
Se, SL
ii
APPROVAL
of a thesis submitted by
Joseph Paul Seney
This thesis has been read by each member of the thesis committee
and has been found to be satisfactory regarding content, English usage,
format, citations, bibliographic style, and consistency and is ready for
submission to the College of Graduate Studies.
V ul—
Date
Chair
son, Graduate Committee
Approved for the Major Department
Date
mi
Head, (Major Department
Approved for the College of Graduate Studies
Date
Graduate
iii
STATEMENT■OF PERMISSION TO USE
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presenting
this
thesis
in
partial
fulfillment
requirements for a master's degree at Montana State
of
the
University, I agree
that the Library shall make it available to borrowers under rules of the
Library.
Brief
quotations
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thesis
are
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made.
Permission for extensive quotation
thesis may
be granted
in this
opinion of
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thesis is for scholarly purposes.
use of the material in this
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Any copying or
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ACKNOWLEDGEMENTS
The author wishes to thank the members of
Wilson
(Committee
Chairman),
Dr.
his committee.
James Schmitt, Dr. Katherine Hansen
and Dr. Clifford Montagne, for providing guidance and
throughout the
project.
I also
Sciences for their moral and
Wade
Stoelting,
students.
I would
Tori
especially
committee chairperson,
This project was
Bozeman
District,
Dr. John
partially
Angela
like
to
support; ;my
by
Service
field assistants,
Nielsen, and my fellow graduate
thank
Wilson, for
funded
USDA-Forest
helpful criticism
wish to thank the Department of Earth
financial
Robson,
Dr. John
the
my
thesis
advisor and
his guidance and support.
Gallatin
National Forest,
Intermountain Research Station
Grant INT-89436-RJVA, and the International Mountain Bike Association.
V
TABLES OF CONTENTS
Page
1.
2.
I N T R O D U C T I O N .......................
I
Scope and P u r p o s e ............... ■ . .............. ...
Quantifying Trail Impacts .................................
Description of Study Area . . . . . . . . . . . .
I
2
10
M E T H O D S ......................................................
15
Study Design and Site S e l e c t i o n ..............................
Soil Description and Classification ........................
Field Experiments and Measurements ........................
Laboratory Procedures
....................................
Statistical Methods ............
3.
R E S U L T S .............................................
15
16
16
24
25
28
Study Site Soil Descriptions ...........................
.
28
Water Runoff Scatterplots and Bivariate Regression
R e s u l t s ....................................................28
30
Water Runoff Multiple Regression Results ..................
Sediment Yield Scatterplots and Bivariate
Regression Results .......................................
31
Sediment Yield Multiple Regression Results
...............
37
Relative Impacts .............................................. 38
Multiple Regression Results
...........................
39
Multiple Comparison Test Results ........................
42
Changes in Soil Resistance and Trail Roughness
Through Time
.......................................... 46
4.
DISCUSSION............................................. ..
.
50
Regression Analysis ..................
. . . . . . .
50
Relative Impact
..........................................
53
Conclusions....................................................56
vi
TABLE OF CONTENTS —
Continued
Page
REFERENCES C I T E D ..................... ..................
APPENDIX
58
vii
LIST OF TABLES
Table
Page
12
1.
Study Site Characteristics
2.
Schedule of Data Collection' Activities
..................
18
3.
Comparison of Bouyoucos Mechanical and "Feel"
Method Textures
.......................................
25
4.
Independent Variables Measured in Study
...............
27
5.
Off-trail Soil Profile Description near Emerald Lake Trail
Study Site .............................................
29
Off-trail Soil Profile Description hear New World Gulch
Trail Study S i t e ................................. ..
30
Bivariate Regression Results Treating Water Runoff as the
Dependent Variable
...............
. ............ ..
31
Bivariate Regression Results Treating Sediment Yield as the
Dependent V a r i a b l e .............................. . .
34
9.
Initial Sediment Yield Multiple Regression Results
38
10.
Expanded Sediment Yield Multiple Regression Results
40
11.
Water Runoff Multiple Comparison Results ..................
43
12.
Sediment Yield Multiple Comparison Results
45
13.
Emerald Lake Trail Slope, Soil Moisture, and
Water Runoff Data
.
..................... ..
65
Emerald Lake Trail Soil Resistance and
Sediment Yield Data ....................................
67
15.
Emerald Lake Trail Roughness Data
........................
69
16.
New World Gulch Trail Slope, Soil Moisture, and
Water Runoff Data . . . . . .
.....................
71
6.
7.
8.
14.
..............................
.
...............
Z
\
viii
LIST OF TABLES —
Table
Continued
Page
17.
New World Gulch Trail Primary Data Soil Resistance and
Sediment Yield D a t a .................. ..................73
18.
New World Gulch Trail Roughness Data
75
ix
LIST OF FIGURES
Figure
Page
1.
Location of Emerald Lake and New World Gulch Trails
2.
Systematic Sample Design and Equipment Used for Trail
Roughness and Soil Resistance Measurements
............
19
3.
Sketch of CN-970 Proving Ring Penetrometer
...............
20
4.
Sketch of Meeuwig Rainfall Simulator
............
22
5.
Scatterplots for Water Runoff and Selected Variables .
32
6.
Scatterplots for Sediment Yield and Selected Variables
35
7.
Changes in Soil Resistance Through Time
..................
47
8.
Changes in Trail Roughness Through Time
..................
49
.
A.
...
11
ABSTRACT
Little is known about the erosional impacts of hikers, horses,
motorcycles and off-road bicycles on mountain trails. The purpose of
this investigation was to determine the relative impacts of different
trail uses with respect to water runoff and sediment yield. A total of
108 sample plots were examined on existing trails in or near the
Gallatin National Forest of southwestern Montana.
A modified Meeuwig
drip-type rainfall simulator was used to reproduce natural rainstorm
events.
Treatments of 100 passes were applied to each of the sample
plots. This approach meant that 24 sample plots were established for
each treatment type (hiking, horseback riding, motorcycling, bicycling)
in addition to 12 sample plots for the control or null hypothesis case
to represent two soil texture groupings (clay and sandy clay or loam and
sandy loam), two antecedent soil moisture classes (dry and prewetted),
and two slope gradients (0-6 percent and 8-21 percent), and three
replications.
The results
of this
study demonstrate
the interaction of
topographic, soil, and geomorphic variables and the difficulty of
understanding natural processes on existing trails.
None of the
hypothesized relationships
between water
runoff and slope, soil
texture, antecedent soil moisture, trail roughness, and soil resistance
were statistically significant.
However, the multiple regression
results for sediment yield were statistically significant.
Five
independent variables or cross-products explained 42% of the variability
in sediment yield when soil texture was used as a series of indicator
variables. The addition of a trail user as a second series of indicator
variables explained an additional 28% of the variability in sediment
yield.
Ten variables combined to explain 70% of the variability in
sediment yield with simple or combined variables incorporating soil
texture (37%), slope (35%), user treatment (35%), and accounting for the
largest contributions.
The use of multiple comparison tests clarify
the roles of the different trail users and in particular showed that
horses and hikers (hooves and feet) made more sediment available than
wheels (motorcycles and off-road bicycles).
This effect was most
pronounced on prewetted trails.
I
CHAPTER ONE
INTRODUCTION
Scope and Purpose
The tremendous
increase in
decades has created
impact in
crowded
nature reserves,
outdoor recreation during the past two
conditions
and
national forests
intensified environmental
and parks,
and state and
municipal recreation centers (McQuaid-Cook, 1978; Cole, 1989).
survey of
A
1975
land managers reported substantial erosion on mountain trails
during the previous decade (Godin and Leonard, 1979).
attributed to
The
erosion was
dramatic increases in horse and foot travel on trails not
designed to accommodate higher
volumes of
traffic.
The National Park
Service (1975) predicted that soil compaction and erosion caused by foot
and horse traffic on
damage to
trails
trail systems
would
in the
contribute
the
future; however,
most environmental
trail use during the
past ten years has grown to include off-road bicycles and motorcycles as
well as
the
horse and
same
additional
trails
use
Intensive use of
trails in
foot traffic.
as
hikers,
compounds
recreation
Off-road bicyclists in^many cases use
horses
erosional
areas
may
and
motorcycles,
concerns
cause
and
so
user
irreversible
that this
conflicts.
damage to
a short period of time as increased use reduces plant growth,
destroys ground cover, and increases runoff
and soil
erosion (Dotzenko
2
et al., 1967).
Today's
their trail
numbers
land
systems
of
users
experiences.
during the
managers
as
need
they
and
past ten
years has
to
maintain
use
concerns among land managers
Shovic, pers.
struggle
still
The increased
to assess the carrying capacities of
and
accommodate
high
quality
popularity
of
to
results of studies
managers to
and environmental
organizations (Douglas,
The
off-road
comparing
evaluate the
off-road bicycles
conflicts and erosional
comm., 1989).
regulate
recreational
increased user
development of
pertinent studies to
assess trail user impacts and conflicts will help
policies
the increased
bicycling
different
trail impacts
and
trail
land managers develop
other trail uses.
users
will
The
allow land
of all users and differentiate
the emotional and environmental arguments that are presently
invoked to
support and/or challenge one or more of these uses.
This
study
assesses
the
erosional
impacts
of
hikers, horses,
motorcycles, and off-road bicycles on two existing mountain trails.
study had
three objectives
as follows:
The
I) quantify the individual and
combined relationships between water runoff and selected topographic and
soil variables;
between sediment
variables; and
2) quantify
yield and
the individual
and combined relationships
selected hydrologic,
topographic, and soil
3) quantify the relative impacts of different trail uses
on water runoff and sediment yield.
Quantifying Trail Impacts
The durability of recreational sites is influenced by
and human
site characteristics.
the physical
Climate, terrain attributes, and soil
3
properties determine
human site
the physical
site characteristics.
characteristics are more complicated.
and volume of use
reflect the
distance of
The critical
For example, the type
the recreational
site from
population centers and the perceived intrinsic qualities of the site, so
that sites adjacent to large population centers often experience greater
levels
of
use.
Heavy
use
often leads to increased user conflicts,
erosional concerns, and a decrease in the perceived
of recreational
sites.
The ability
recreational use is partially
When the
intensity of
perceived
or
actual
managers may
intrinsic qualities
of recreational sites to tolerate
dependent on
intensity and
type of use.
use exceeds some threshold value related to some
human
limit the
or
physical
site
characteristic,
land
types and quantity of use or they may alter and
repair recreational sites to better withstand higher intensities of use.
For example,
a few trails located in Yosemite National Park, California
have been paved or graveled to
reduce
trail
erosion
caused
by heavy
use.
The choice
methods, and
durability
and location
types
of
and
these
of recreational
intensities
sites.
of
Trail
access to recreational sites; however,
experience
increase.
is
inevitably
Past
motorcycle traffic.
trail
reduced
studies
as
have
use
sites, site construction
affect
the
quality and
systems are necessary to provide
the
quality
of
a recreational
trail use and trail degradation
focused
on
foot,
horse,
and
Most studies have examined the relationship between
trail location, methods of trail construction, and type and frequency of
use with
vegetation and/or soil impacts (e.g., Bates, 1935; Dotzenko et
al., 1967; Ketchledge and Leonard, 1970;
Dawson et
al., 1974; Helgath,
4
1975; Bryan,
1977; Weaver and Dale, 1978; Bratton et al., 1979; Leonard
and Plumley, 1978; Summer, 1980, 1986; Coleman, 1981; Fish et al., 1981;
Kuss
and
Morgan,
Other studies
effects or
1980;
Jubenville and O'Sullivan, 1987; Kuss, 1987).
have attempted
to model
recreation-induced soil erosion
the physical.carrying capacity of natural areas by utilizing
the Universal Soil Loss Equation (e.g., Kuss and Morgan, 1980, 1984).
Alteration
subsequent
of
trail
ecosystem and
to
inducing
a
carrying
capacity is a function
can
a
to
trampling
and
related to the resilience of an
Burden
particular
change
of
be
due
and
Randerson (1972)
"the maximum intensity of use an area will
under
permanent
environment
capacity.
capacity as
support
influence the
biotic
development
its
define carrying
continue
the
in
the
the
biotic
physical
erosion potential
management
regime
environment".
and
biological
of recreation
without
Carrying
factors that
sites (Kuss and Morgan,
1984).
The
pressure
exerted
integrates the ^effeet of
and frequency
rates
of
of use,
trail
per
area
of
ground
per
unit time
weight, size of impact area, length of impact
and may
degradation
information about location of
many researchers
unit
indicate recreational
(Burden
and
frequency
have resorted
user impacts and
Randerson, 1972).
use
is
seldom
However,
available and
to physical and biological variables to
estimate carrying capacity and recreational impacts.
Klock and McColley (1978), for example, suggested four site factors
that
determine
the
durability
of recreational sites: trafficability,
depth ,-^drainage, and erodibility.
X
capacity df a soil to bear a
Trafficability
moving
load.
was
defined
as the
This factor is highly
5
dependent on
the strength
of the soil which, in turn, is influenced by
the texture, structure, and permeability.
with soil
soils
moisture levels
often
particularly
increases
the
through time
compaction
volume
of
Soil resistance
(Lull, 1959).
and,
in
macropores
turn,
(Cole,
also varies
Travel over wet
reduces,
1987).
porosity,
This tends to
reduce water-holding capacity in fine-textured soils and increase
coarse-textured soils.
Coarse-textured soils (i.e., coarse sands) tend
to resist erosion because
moved; however,
the
the soils
easily increased.
In
it in
particles
are
not
easily
detached or
are structurally unstable and trail width is
contrast,
fine-textured
soils
(i.e.,
silt and
clays) are highly erodible and easy to detach and move (Cole, 1987).
Depth was
defined by
Klock and
McColley (1978)
unconsolidated soil material above bedrock.
the
durability
of
recreational
as the amount of
This factor
is related to
sites because it affects the moisture
and nutrient pools available for plant growth.
Hence, shallow soils are
more sensitive to vegetation disturbance and more susceptible to erosion
and/or trail widening once the soil is exposed.
Drainage was defined by Klock and McColley (1978) as the propensity
of
the
soil
within
an
area
to
retain groundwater.
related to the durability of recreational
species
found
in
poorly
drained
disturbance than the vegetation
Trails located
on poorly
sites because
areas
species
drained soils
usually
found
in
This factor is
the vegetation
suffer
more
from
well-drained areas.
are usually deeper and display
greater roughness than trails located on well-drained sites
(Weaver and
Dale, 1978).
Erodibility
was
defined
by
Klock
and
McColley
(1978)
as the
6
resistance of
a soil
to displacement
by the
action of wind or water.
Highly erodible soils are less likely to withstand a given
and therefore
are less
durable than
soils with good aggregation and
loams)
appear
to
be
least
less erodible
intermediate
erosive
and McColley,
soils.
texture
Generally,
(sandy
loams to
while silty-clay, clay, or fine-
textured soils are most susceptible to wind and
1971b; Klock
level of use
water erosion (Meeuwig,
1978; Leonard and Plumley, 1978).
However,
other factors affect rates of wind and/or water erosion as well.
and
Van
Haveren
(1971),
for
example,
variability in soil erosion was due to
and slope
gradient in
1978)
and
showed that 90 percent of the
variations in rainfall intensity
their study of mountain soils in Utah and Idaho.
Slope gradient and soil loss are
Smith,
Farmer
steeper
positively correlated
slopes
often
experience
(Wischmeier and
increased
trail
degradation (Leonard and Plumley, 1978).
Many
studies
have
motorcycle recreation,
Chappell
et
recreational
al.,
1971;
Moore, 1973; Dale and Weaver,
Smith, 1975;
composition
impacts
of
foot,
horse,
trails
(Bates,
increase
which, in
(Cole,
and
1935; Bayfield,
Burden and Randerson, 1972; Liddle and
1974;
Liddle,
1975;
Liddle
and Greig-
Cole, 1978; Weaver and Dale, 1978; Summer, 1980).
of vegetation can
along trails
the
uses on vegetation along trails without examining
the carrying capacity of
1971;
examined
direct
precipitation
turn, alters
1978).
Trampling
and
Removal
solar intensity
trailside microclimate and plant
or
removal
of
vegetation
is
generally the first consequence of planned and unplanned trail formation
(Quinn and Morgan, 1980).
By the time vegetation wear is noticed the critical period in which
7
accelerated erosion
occurs has already passed (Quinn and Morgan, 1980).
Water runoff is expected to increase
because infiltration
of the
soil (Quinn
studies
show
early stages
of trampling
is decreased due to increased density (compaction)
and Morgan,
that
in the
trampling
1980; Cole,
increases
which, in turn, changes soil porosity,
1987; Kuss,
the
1987).
Most
bulk density of the soil
moisture content,
aeration, and
the availability of soil nutrients (Baver, 1933; Liddle and Greig-Smith,
1975; Weaver and Dale, 1978; Kuss, 1983).
Once
vegetation
especially where
is
removed
trails channel
erosion
is
water which
the
is not
primary
diverted from the
tread (Cole, 1987).
Trails located parallel to the slope
down the
increase erosion
trail and
when compared
perpendicular to the slope (Bratton, et al, 1979).
influenced
by
the
position
bottom of a slope in addition
across the
trail.
the
to trails oriented
Potential erosion is
trail with respect to the top or
to the
gradient of
northcentral
the slope
increases exponentially
(Coleman, 1981).
as the
Slope gradient
(Helgath,
Colorado
and
suggested trails
1975).
the slope.
The
erosion potential
slope gradient exceeds 12 to 13 percent
is
closely
Bratton
et
associated
al.
(1979),
with
for
inventoried trail degradation in Great Smokey Mountain National
eastern Tennessee
and western
type of
example,
Park of
North Carolina, and found that the slope
of the trail was the most important factor in explaining
deterioration.
along and
the crest of a hillslope will erode at a higher rate than
trails located on other segments of
landform
channel water
Summer (1986), for example, examined trails in Rocky
Mountain National Park in
located below
of
problem,
rates of trail
8
The
results
influence
trail
from
other
studies
degradation.
Bryan
indicate that soil factors also
(1977),
for
example, examined
mountain hiking trails in Grovelsjon, Sweden and related the severity of
degradation to soil resistance.
several
soil
properties,
Soil
estimated from
embedded
in
or
on
the
trail.
He
particle aggregation initially increases up to a threshold
determined by
soil.
was
including aggregate stability, soil texture,
and quantity of coarse fragments
thought that
resistance
the soil
shearing strength
when pressure
is exerted on
Aggregation declined and soil credibility and soil loss increased
once this threshold was reached. The loose materials in or
tend to
counteract soil
early stages of trail
erosion
by
degradation.
increasing
runoff when trail use
the trail
compaction and
the
increase resistance during the
However, coarse
turbulence
and
continues because
corrading the
trail bed
on the trail
fragments promote
erosive capacity of trail
loose materials
saltate along
and undermining trail sides (Bryan,
1977).
In contrast to the
previous
group
of
studies
which
focused on
natural processes and controls,.a smaller group (Ketchledge and Leonard,
1970; Dale and Weaver,
Bratton, 1979;
1974;
Burde and
Helgath,
1975;
Renfro, 1986)
traffic on
trails.
and
Dale, 1978;
has examined the relationships
between topographic and soil variables and
and motorcycle
Weaver
the impacts
These studies
of foot, horse,
show that different
i,
trail uses result in different
trail
erosion
rates,
probably because
different users exert different impacts per unit area (Lull, 1959).
impact per unit area combines the
impact area
or "foot
print" of
weight of
the user.
the user
and size
The
of the
Weaver and Dale (1978), for
9
example, found that horse use caused more pronounced
width, depth,
increases in trail
litter, and soil compaction than hiking and motorcycling.
Horse traffic applies the
greatest impact
(force) per
unit area among
hikers, horseback riders, off-road bicyclists, and motorcyclists.
Weaver and . Dale (1978) also compared motorcycle erosion with horse
and foot erosion and found that motorcycles moving
narrow
rut
which
served
as
a
sediment transport capacity of
linear channel
funnel and increased the velocity and
trail
However,
affect
the
motorcyclists, hikers
forces to decelerate.
descending
which,
motorcycles
needed, did not greatly
contrast to
a
decelerating
runoff.
The
development
of a
was the direct result of the imprint of the tire and the
torque applied by the motorcyclist
erosion.
uphill established a
steep
and
Hikers
trail;
moving
turn,
hence,
a
led
to increased
moving downhill, when torque is not
rate
of
trail
and horses
and
down
in
horses
steep
do not
tend
greater
degradation.
to
trail.
rely on the same
loosen
forces
In
soil when
were applied when
Shear
stresses are
increased and compressional stresses are reduced on steeper slopes which
increases the
(Quinn and
quantities
Morgan, 1980).
of
loose
steep
slopes
and
available
for transport
Weaver and Dale (1978) suggested motorcycles
ascend gentle slopes and descend
ascend
sediment
steep
descend
slopes
and
hikers
and horses
gentle slopes to minimize erosional
impacts.
The studies referred to above have important implications
project, even
nor refer to
for this
though they do not examine erosion from off-road bicycles
existing
demonstrate the
trails
importance of
in
many
cases.
In
particular, they
rainfall intensity and slope gradient as
I
10
key
factors
in
explaining
properties, such
variation
as structure,
sediment
yield.
Soil
texture, and moisture content determine
the resistance to erosion and play
results from
in
secondary
roles.
The
variety of
past studies exemplify the difficulty of understanding the
natural variability of trail
degradation occurs
the geomorphic
degradation.
Several studies
show trail
regardless of specific uses and is more dependent on
processes that
occur in
different landscapes; however,
most studies to date have focused on one particular trail use.
The approach
made
to
influence
of this
superimpose
trail
study was
human
impacts
erosion.
This
relative impacts of different
different because
on
the
approach
an attempt was
"natural"
factors
that
was needed to evaluate the
trail uses.
Few
other studies reported
in the literature to date have attempted this type of analysis.
Description of Study Area
Two existing
trails near
Bozeman, Montana
were selected as study
sites for this project because of ease of access, availability
from
adjacent
streams,
diversity of slope
Emerald Lake
located 12
long
gradients
trail located
km southeast
consistent
and
39 km
soil
sections
textures.
south and
of Bozeman,
of
trail,
Both
of water
and
a
trails, the
the New World Gulch trail
Montana (population approximately
30,000) are easily accessible and have experienced all four types of use
(foot, horse, motorcycle and off-road bicycle traffic) over the past ten
years (Figure I).
Both
study
sites
were
located
in or near the Gallatin National
Forest, Montana (45°30/N, Ill0W) which borders the northern
and western
Figure I.
Location of Emerald Lake and New World Gulch trails.
boundaries
of
Yellowstone
forest ranges from forested
The
forests
consist
mainly
National
Park.
foothills
of
to rugged
lodgepole
Douglas fir (Pseudotsuga menziesii), with
occupying smaller
trails provide
examined
in
and more
recreational
this
The topography within this
a
pine
project.
The
alpine
of
other species
Approximately
for
peaks.
(Pinus contorta) and
variety
specific niches.
opportunities
rocky
the
characteristics
four
500 km of
user groups
of both trails are
summarized in Table I.
The Emerald Lake trail study site
National
Forest
and
consisted
of
was located within the
a
1.6 km
section of
Gallatin
trail at an
12
Table I. Study site characteristics.
Characteristics
Emerald Lake
Trail
New World Gulch
Trail
Parent material
Glacial till
Sandstone, shale
Soil texture
Sandy loam.
loam
Clay,
sandy clay
Elevation
2000 m
1600 m
Slope (on trail)
3-17%
2-21%
Aspect
Northeast
North
Vegetation
Lodgepole pine
Grasses,
Douglas-fir
approximate
elevation of
hummocky,
rolling
this study
site occupy
material consists
2000
glacial
m.
till
land
deposits.
the bottom
of glacial
The
of a
surface consists of
The morainal deposits at
U-shaped valley.
till deposits
of Pleistocene age and are
primarily derived from layered, volcanic rock (Davis and
These
medium-textured
deposits
The parent
Shovic, 1984).
contain variable amounts of subrounded
rock fragments.
Soils are generally well-drained with medium
Subsoil clay
accumulation occurs
in some locations.
the lower soil horizons range from 35-50 percent.
study site are
to moderate textures.
classified as mixed,
Rock fragments in
The
loamy skeletal,
soils within the
Typic Cryoboralfs
(Davis and Shovic, 1984).
A dense
study site.
lodgepole
pine (Pinus
The understory
grouse whortleberry
is
contorta) forest
composed
of
a
thick
surrounds this
groundcover of
(Vaccinium scoparium), dwarf huckleberry (Vaccinium
13
caespitosum),
and
precipitation in
Trail
twinflower
this area
accessibility
snowpack and
and
surface
occurs in
borealis).
The
is 65-90 cm and 60 percent
in April
saturated
stream usually
(Linnaea
May
is
soils.
late April
limited
falls
by
as snow.
the remaining
Peak runoff from the
and early
annual
nearby
May (Davis and Shovic,
1984).
The New World Gulch trail study site was located on land immediately
outside
the
Gallatin
National
Montana and consisted of
elevation of 1600
a 0.8
meters.
Forest
administered
km section
of trail
locations
of
bedrock, with
ridges
and
bedrock
ridges
the
more
shales
the State of
at an approximate
The topography consists of ridges with steep
slopes and occasional small valleys or swales (Davis
The
by
and
swales are controlled by the underlying
resistant
and
and Shovic, 1984).
siltstones
sandstones
forming
and
swales
limestones forming
and valleys.
The
of this area consists of Lower Cretaceous Mowry and Thermopolis
shale, Kootenai
Formation sandstone and mudstone, and Jurassic Morrison
Formation shale, siltstone and mudstone (Roberts, 1964).
Fine- and medium-textured soils
from thickly
have formed
bedded sandstones and shales.
in material weathered
The soils are classified as
mixed, fine loamy Typic Cryoboralfs (Davis and Shovic,
soils
in
this
area
are
1984).
Overall,
moderately well-drained with fine and medium
textures.
Vegetation
perennial
surrounding
grasses
and
this
some
annual precipitation is similar
percent
falling
as
snow).
trail
consists
predominantly
Douglas-fir (Pseudotsuga menziesii).
to the
first trail
(65-90 cm
of
The
with 60
Trail accessibility between April-May and
14
October-November is
clayey soils.
restricted due
to saturation
of the predominantly
15
CHAPTER TWO
METHODS
The first
two objectives
of this research were concerned with the
natural variability and geomorphic controls operating on the sections of
trails used
in the
study.
The relationships between water runoff and
sediment yield, and selected hydrologic, topographic, and soil variables
were
examined.
The
third
and final objective sought to superimpose
human impacts on these geomorphic controls in an attempt to evaluate the
relative impacts
of different
trail uses.
This chapter describes the
methods used for data collection and analysis.
Study Design and Site Selection
A
modified
reproduce
Meeuwig
natural
drip-type
rainstorm
rainfall
events
simulator
was
used
to
and treatments of 100 passes were
applied to a total of 108 sample plots on the Emerald Lake and New World
Gulch trails.
There
were 54
sample plots on each of the Emerald Lake
and New World Gulch trails consisting of 12
of
travel
(hiking,
off-road
bicycling,
motorcycling treatment plots) and
null treatment
case.
sample plots
six sample
The twelve
horseback
plots for
for each mode
riding,
and
the control or
sample plots for each mode of travel
represented two antecedent soil
moisture classes
(dry and pre-wetted),
two
(0-6
percent),
slope
gradient
classes
and
8-21
and
three
16
replications for
each plot
type.
combined the two antecedent
The control or null treatment plots
soil moisture
regimes, requiring
only six
plots.
Sample plot
size (66
by 66
containment tray that came
sample plots
cm) was determined by the size of the
with the
were determined
rainfall simulator.
on reconnaissance hikes along the Emerald
Lake and New World Gulch trails in the spring of
with uniform
slope and
Locations of
soil conditions
1989.
Trail sections
were selected for study plots,
.
avoiding sections of
necessary litter
trail
and large
with
protruding
loose stones
rocks
or
were removed
.'i
roots,
where
from the sample
plots before treatments.
Soil Description and Classification
Soil pits were dug adjacent to each trail
Soil
Taxonomy
(Soil
classify the soils.
Survey
A
Staff,
total
of
1988)
four
segment and
were
shallow
the Keys to
used to describe and
pits
were
dug across
sections of trail to compare soils on and off the trail.
Field Experiments and Measurements
User
treatments
were
assigned
to
sample
plots
based
on
the
availability of the user (hikers, horses, motorcycle and rider, mountain
bike and
rider), antecedent
for the particular
user
Afternoon thundershowers
certain days.
(0-6
soil moisture, slope gradient class needed
or
8-21
percent),
and
daily weather.
reduced the number of experiments conducted on
Four plots were examined on most field days, although six
plots were examined on days with good weather and dry treatments.
17
Data collection consisted of the seven tasks summarized in Table 2.
Slope gradient, trail roughness, and soil resistance were measured prior
to treatment
for each
sample plot
during Task
measured with a Brunton compass and 3 by
was placed
on the
0.6 m
sections
slots.
board.
The 3
of
trail.
Trail
the slope
roughness
measured using 12 transects marked off at 2.54
sample plot
Slope gradient was
m board
trail and several slope measurements were taken with
the Brunton compass on the board to determine
specific
I.
and a
or
gradient along
micro-relief was
cm intervals
along each
91.5 cm long, 5 by 10 cm board with 13 evenly spaced
A metal ruler was then inserted
into each
slot moving
left to
right and a depth measurement was taken for each of the 13 slots (Figure
2).
High values
represented depressions and low values
elevated spots
on the trail.
Soil resistance was measured
with a
Soiltest, Inc.
at
selected
CN-970 proving
depth of
2.54 cm.
The
penetrometer which measures the
instrument
consists
of
a
along transects
ring penetrometer.
resistance was treated as a measure of the
to a
points
Hence, soil
force required
to penetrate
proving ring penetrometer is a cone type
penetration resistance
T-handle,
45.7
of soils.
The
cm penetration rod, 0.91 m
extension, proving ring of 113.4 kg capacity with a dial
indicator, and
removable cone point (Figure 3).
The cone
point used
conical area of 24.69 cm2 .
in this study had a base area of 6.34 cm2 and
When the cone is forced into the ground, the
proving ring is deformed in proportion to the force applied.
This force
is thought to represent the shearing resistance of the soil
(Liddle and
Moore, 1973).
The cone penetration
was limited to one-half of the area
18
Table 2. Schedule of data collection activities.
Task
I
Summary of data collection activities
Collection of soil samples for soil texture and
antecedent soil
moisture
measurements;
slope, trail roughness and
soil
resistance measurements were taken.
Skip tasks 2 and 3 for dry treatment plots.
2.
Meeuwig rainfall simulator was erected over the plot and a 20
minute rainstorm with an intensity of 127 mm hr-1 was applied.
3.
Water runoff and sediment yield were collected; soil samples were
taken for subsequent soil moisture measurements;
and trail
roughness and soil resistance were measured (again).
Conduct tasks 4 through 7 on all plots.
4.
Treatments were applied (i.e., 50 hiking, bicycling,
riding and motorcycling passes);
trail roughness
resistance were measured (again).
5.
Treatments were applied (i.e., an additional 50 hiking, bicycling
horseback and motorcycling passes);
trail roughness and soil
resistance were measured (again).
6.
Meeuwig rainfall simulator was erected over the plot and a 20
minute rainstorm with an intensity of 127 mm Ir1 was applied.
7.
Water runoff and sediment yield were collected; soil samples were
taken for subsequent soil moisture measurements;
and trail
roughness and soil resistance were measured (again).
of the cone; hence, the range
been doubled
to reflect
values on
the smaller
the indicator
base area.
doubled because they were only
used
types
the measurement
of
importance.
trail
use, so that
for
horseback
and soil
dial could have
These values were not
relative
comparisons between
scale was
not of prime
Soil resistance values are expressed in kg of force per cm2
19
<
.^
Tl ■
Figure 2. Systematic
sample design and equipment
roughness and soil resistance measurements.
used
for
trail
throughout the thesis.
All 12 of the transects
used for
(Figure 2) were used for soil
soil
resistance
during the
micro-relief measurements
resistance measurements.
measurements
tasks I, 3, 4, 5, and 7.
trail
were
taken
A
total of 11
for each sample plot during
Hence, the transects labeled I and 7 were used
first task, 2 and 8 for the third, 3 and 9 for the fourth, 4
and 10 for the fifth, and the fifth and eleventh transects were used for
the seventh
task (Figure 2).
to the next assigned
specific transects
this manner
task
used to
to assure
The designated transects were transferred
when
samples
were
task
measure soil
was
skipped
resistance
(Table
2). The
were assigned
in
consistency from plot to plot.
Soil texture and moisture
moisture
a
also
samples were
taken during task I.
taken
the
constituted the second and sixth tasks.
after
rainfall
Soil
events that
These samples were collected in
20
Figure 3.
the
field
Sketch of CN-970 proving ring penetrometer
(Modified from
Soiltest, Inc. operations and service instructions diagram).
and
stored
in
plastic
containers in shady locations for
transport back to Montana State University at the end of
The second
task consisted
of no activity for dry treatments and a
rainstorm if the treatment was to be applied to a
rainfall simulator
was erected
the field day.
prewetted plot.
The
over the plot and a 20 minute simulated
21
rainstorm with a constant intensity of
modified Meeuwig
127 mm
hr-1 was
rainfall simulator (Schmid, 1988).
applied with a
This simulator was
chosen because of its easy assembly and modest water requirements.
Field studies of erosion
potential in
mountainous terrain usually
rely on natural rainfall events to provide the erosive force or rainfall
needed for experiments.
provide
the
Simulated
timing,
rainfall
places and,
However,
location,
events
natural
and
provide
rainfall
intensities
rainstorms
does
not always
needed for research.
at
desired
times and
therefore, they allow the collection of large quantities of
runoff and erosion data in relatively short periods of time.
The erosive
kinetic energy
energy of
(RE =
rainfall is
1/2MV2).
reflects storm intensity.
usually expressed
in terms of
The velocity (V2) term in the equation
The intensity of a rainstorm varies
with the
distribution of raindrop sizes, since their size influences the velocity
at which they fall (Law and Parsons, 1943).
velocity are
the same
for natural
Drop
velocity and terminal
rainstorm events,
but the terminal
velocities accompanying natural rainstorms are difficult
to attain with
portable rainfall simulators (Young, 1979).
The rainfall
drip-type
simulator used
in this
study was.a modified Meeuwig
simulator (Meeuwig, 1971a) with a drop fall height
(Figure 4).
of 155 cm
The approximate waterdrop diameter at a simulated intensity
of 127 mm per hour was 2.8
mm.
raindrop
is
falling
155
cm
The
impact
approximately
velocity
470
for
a
cm s_1 (Laws, 1941),
whereas similar sized drops would achieve a terminal velocity of
s"1 in
natural rainstorms
energy of
(Gunn and Kinzer, 1949).
2.8 mm
780 cm
Hence, the kinetic
simulated rainfall events in this study was roughly one-third
22
155 cm
...... .
Figure 4. Sketch of Meeuwig rainfall simulator (from Schmid, 1988).
23
that of a natural rainfall event.
The modified Meeuwig simulator used in the study had a 61 by
2.5
cm
plexiglass
hypodermic tubing.
randomize the
above the
chamber
with
500
drip
needles
An electric motor was used to rotate
raindrops.
made from
the chamber to
A 18.9 I plastic container was elevated 20 cm
water chamber to provide a continuous supply of water
chamber, and
of
water
127 mm
The
61 by
a 20 minute rainstorm was applied at a constant
to the
intensity
h_1.
third
collection
and
of
simulated
seventh
the
surface
rainstorms
collection tray
containers.
tasks
at
runoff
the
which tunneled
The
containers
listed
2
required the
and sediment yield produced by the
downslope
water and
were
in. Table
then
end
of
each
plot
using
a
sediment into 0.76 I plastic
emptied
into
larger
3.8 I
containers for transportation back to the Montana State University Soils
Laboratory for further analysis.
Soil moisture samples
were also taken
and stored for future transport and analysis at the same times.
The
application
of
the
appropriate hiking, bicycling, horseback
riding, and motorcycling treatments consisting of two sets
so that
soil resistance
and trail roughness could be measured after 50
and 100 passes comprised Tasks 4 and 5.
Nielsen and
of 50 passes
The horses and
riders
(Angela
Wade Stoelting)
were supplied
by Dr. Gerald A. Nielsen of
the Montana State University
Department of
Plant and
the Honda
XLl25 motorcycle
was supplied by the Bozeman District office
of the Gallatin National Forest.
rider and
Joseph Seney
Soil Science and
Wade Stoelting
(author) was
the hiker.
was the
mountain bike
Trail user treatments
were applied with proper trail etiquette and within the speed
limits of
24
usual trail
activity.
User passes along the plots were at least 4 m in
length, so that users could attain a natural trail gait.
Laboratory Procedures
Soil moisture,
samples
were
Laboratory.
for
24
soil
texture,
analyzed
at
water
runoff,
and
Montana State University's Soil Testing
Wet soil moisture samples were weighed
hours
at
IlO0C.
sediment yield
and then oven-dried
Samples were then reweighed.
moisture was determined by
moist
divided by dry soil weight.
soil
weight
minus
Percent soil
dry
soil weight
The water runoff samples were weighed using
a Mettler PE 6000 digital scale and placed in a soil drying
until all
sediment
the water
and
containers
had evaporated from the containers.
container
were
were
weighed
to
containers, and this weight
and container
then
reweighed.
determine
was then
the
room (IS0C)
The remaining
Twenty-five
average
subtracted from
weight
3.8
of
I
the
the dry sediment
weights to determine sediment and water runoff masses for
r
each sample.
All 108 samples were
determine
a
textural
hand
class
textured
(Thien,
using
1979).
the
"Feel"
Method to
Twelve samples were then
selected at random and mechanical analyses were performed by the Montana
State
University
Mechanical
classes,
Analysis
and
"feel" method.
the
Soil
Testing
technique
results
Eleven of the
Laboratory
was
were
12
used
to
staff.
determine
The
Bouyoucos
soil textural
compared with those obtained with the
"feel"
method
texture
substantiated with the more elaborate technique (Table 3).
results were
25
Table 3.
Comparsion of Bouyoucos Mechanical and "Feel" Method textures
Sample1
% Sand
Bouyoucos Method
% Silt % Clay Texture
"Feel" Method
Texture
ELTl,
ELTl,
ELT3,
ELTl,
ELT3,
ELTl,
Control, 0-6%
Motorcycle, 0-6%
Hiker, 8-21%
Bicycle, 8-21%
Bicycle, 0-6%
Motorcycle, 8-21%
68
60
42
60
50
44
21
29
42
31
36
38
11
11
16
9
14
18
Sandy loam
Sandy loam
Loam
Sandy Loam
Loam
Loam
Sandy loam
Sandy loam
Loam
Sandy loam
Loam
Loam
NWG2,
NWG2,
NWG2,
NWG3,
NWG3,
NWG3,
Hiker, 8-21%
Bicycle, 8-21%
Horse, 0-6%
Motorcycle, 0-6%
Hiker, 0-6%
Motorcycle, 8-21%
23
19
25
26
29
21
40
36
35
39
36
37
37
45
40
35
35
42
Clay loam
Clay
Clay
Clay loam
Clay loam
Clay
Clay
Clay
Clay
Clay
Clay
Clay
I
loam
loam
loam
loam
ELT = Emerald Lake Trail; treatment type; and slope gradient (%).
NWG = New World Gulch Trail; treatment type; and slope gradient (%).
Statistical Methods
Three sets
of statistical
impacts of trail users.
represent
simple
variables (i.e.,
tests were used to assess the erosional
First, bivariate regression models were used to
relationships
between
independent variables)
yield (i.e., dependent variables).
developed
to
variables.
assess
Finally,
superimpose human
the
impacts on
and water
topographic and
soil
runoff and sediment
Multiple regression models were then
cumulative
several
the
relationships
statistical
these natural
approaches
controls in
between
were
these
used
to
an attempt to
evaluate the relative impacts of different trail users.
The REG
and
multiple
(regression) process
regression
models
in SAS was used to develop bivariate
(Freund
and
Littell,
1986).
The
26
bivariate
models
compared
water
runoff
continuous variables listed in Table 4.
incorporated
variables
the
for
same
continuous
soil texture
variables divided
and
The
multiple regression models
variables
and trail
user
horse,
off-road bicycle,
soil texture
of
incorporating
five trail
motorcycle,
and trail
indicator variables, respectively, and
capable
well
as
The indicator
user subgroups
and control).
of
the
continuous
produced regression
models that
as many as five independent variables,
and
allowed the
impacts of
other trail
users with respect
This
user into three and four
seven indicator variables, and 75 new variables representing
products
indicator
the entire data set into four soil textural subgroups
procedure transformed
were
as
(Table 4).
(clay, sandy clay, loam, and sandy 16am) and
(hiker,
sediment yield with the
indicator
specific trail
variables.
the cross-
This approach
users to be differentiated from
to individual
soil textures as well as
the other measured variables.
In general, this
regression
models in which
eliminated.
permitted
significant
method involves
The
the
SAS
variables
sequential
stepwise
selection
until no
and
multiple
regression
backward
elimination
further
additions
permitted using the significance levels chosen
Littell, 1986).
The
development
of
statistically non-significant variables are
REG
forward
the
by the
or
procedure
of
deletions
non­
are
user (Freund and
models developed to help explain water runoff and
sediment yield variations
in
this
study
used
the
0.05 significance
threshold for both forward selection and backward elimination.
Although the
multiple regression
information about the
models described above
relative impacts of
provided
the different trail users to
27
Table 4.
Independent variables measured in study.
Indicator Variables
Soil texture class (clay, sandy clay, loam, and sandy loam)
Trail user (hiker, horse, off-road bicycle, motorcycle, and no activity)
Continuous Variables
Antecedent soil moisture
Slope
Soil resistance (soil compaction)
Trail roughness (trail micro-relief)
Water runoff (when sediment yield was treated as a dependent variable)
the
extent
that
representing
one
the
indicator
or
more
variables
trail
and
interaction
users were incorporated in the final
models, a more direct test was needed to assess the
the different trail users.
in terms
multiple comparsions
and compiles
of water
in a
A 0.05 level of significance was
1986).
in the
expected had
The use
models which
sediment yield.
multiple difference
The SAS
of means tests
series of tables (Freund et al., 1986).
used
and
the
Bonferroni
option was
The means of the variables measured in the study were
multiple comparsions test by least-squared, means,
because the latter estimate
would be
to develop
this option allows for comparisons of means from samples
of unequal sizes.
also replaced
was used
runoff and
test performs
the results
chosen because
relative impacts of
The multiple comparsions test within the GLM
(General Linear Model) module of SAS
compared users
effects
of 108
the class
the study
samples in
or subclass
design been
this study
marginal means that
balanced (Freund et al.,
(24 for
hiking, horse,
motorcycling and off-road bicycling, respectively; 12 for the control or
null treatment case) meant the study design was not balanced.
28
CHAPTER THREE
RESULTS
Study Site Soil Descriptions
Soil-profile descriptions for the soil pits located adjacent to the
Emerald Lake and New World Gulch
Tables 5
and 6.
The
trail
two in-trail
study
sites
A and
Bt horizons.
horizons were missing (eroded) from both trail sites, so that the
Bt horizons represented the soil surface.
on the
presented in
soil profile descriptions differed
from the off-trail descriptions with respect to the
The A
are
The removal of
the A horizon
Emerald Lake and New World Gulch trails meant that 5 and 7 cm of
soil had been eroded by previous activity along the respective trails.
Water Runoff Scatterplots and Bivariate Regression Results
The first objective of the study was to determine what
variability
of
water
runoff
was
statistically
variability in topographic and soil variables.
bivariate
variables.
regression
The
results
measurement
for
of
water
slope
and
explained
Table
runoff
part of the
by
the
7 summarizes the
and four independent
antecedent
soil moisture,
trail roughness, and soil resistance prior to each rainstorm event meant
that three sets of comparisons were possible; however, the relationships
between these variables and water
runoff
produced
similar
regression
29
Table 5.
Depth
(cm)
Off-trail soil
study site.
Horizon
profile
description near Emerald Lake
Color Texture2 Struc­
moist1
ture3
Consist­ Roots
ence4
Coarse
frags.
trail
PH
0-5
A
IOyr
2/2
si
single
grain
Io
fr
so
common
vfine
none
4.5
5-22
Bt
IOyr
2/2
si
abk(sub)
fine
mod.
sh
fr
common
vfine
10%
6.0
SS
22-55
Cl
IOyr
4/6
gs
single
grain
Io
Io
so
none
50%
6.5
55+
C2
IOyr
3/4
gsl
single
grain
Io
fr
so
none
30%
6.0
1 Represents the color of the soil when moist. Looking at the A horizon,
for example, IOyr represents hue or gradation of color, the first 2
represents the value or the lightness or darkness of the color, and
the second 2 represents the chroma or the strength and purity of the
color.
2 Texture of the soil: si, gs, and gsl represent sandy loam, gravelly
sand and gravelly sandy loam, respectively.
3 Structure of the soil horizon: abk(sub) fine moderate
represents a
subangular blocky structure with moderate or easily observable blocks
and sizes between 5 and 10 mm.
4 Consistence is a measure of the soil's ability to adhere or cohere.
This property was measured for dry,
moist,
and wet samples
abbreviations: Dry-lo, loose; sh, slightly hard; Moist-fr, friable,
lo, loose; and Wet-so, nonsticky; ss, slightly sticky.
results for
all three data sets.
Overall, these results show that none
of the simple relationships between water
runoff and
slope, antecedent
soil moisture, antecedent trail roughness and antecedent soil resistance
respectively
level.
were
statistically
The large scatter of
significant
points in
at the 0.05 significance
the scatterplots
reproduced in
30
Table 6.
Depth
(cm)
Off-trail soil profile description near New World Gulch
study site.
Horizon
0-7
A
7-38
Bti
38-73
73+
Bt2
C
Color Texture2 Strucmoist1
ture3
2.5yr
2.5/4
sc
2.5yr
3/4
sc
2.5yr
2.5/4
sc
2.5yr
3/6
si
Consist- Roots
ence4
abk
f ,vf
mod.
h
fi
abk
fine
mod.
h
vf i
abk
f ,vf
mod.
h
vf i
abk
fine
mod.
trail
Coarse pH
frags.
common
fine
<1%
8.0
few
vfine
1%
8.0
none
1%
7.5
none
2%
8.0
S
VS
VS
h
fi
S
I Represents the color of the soil when moist. Looking at the A horizon.
for example,
2.5YR represents the hue or gradation of color, 2.5
represents the value
or the lightness or darkness of the color, and
the 4 represents the chroma or the strength and purity of the color.
2 Texture of the soil: sc and si represent sandy clay and sandy loam.
3 Structure of the soil horizon: abk fine i
moderate represents an angular
blocky structure with the moderate or easily observable blocks and
sizes between 5 and 10mm.
4 Consistence is a measure of the soil's ability to adhere or cohere.
moist,
and wet samples
This property was measured for dry,
abbreviations: Dry-h , hard; sh, slightly hard;
Moist-fi, firm, vfivery firm; and Wet-s , sticky, vs, very sticky.
Figure 5 confirms this conclusion.
Water Runoff Multiple Regression Results
This
situation
did
not
change
when
multiple
regression
was
substituted for the bivariate regression in that none of the independent
variables were related to water runoff
at the
0.05 significance level.
31
Table 7.
Bivariate regression results
dependent variable.
First rainstorm
(n=60)
Variable
Slope
Antecedent
soil moist.
Antecedent
trail roughness
Antecedent
soil density
Both
the
Second rainstorm
(ni=108)
R2
P Value
R2
.012
.41
.002
.0002
.92
.0001
.002
bivariate
treating
water
runoff as the
All rainstorms
(n=168)
R2
P Value
.69
.0004
.58
.002
.66
.0001
.60
.79
.003
.61
.0001
.93
.74
.004
.55
.0001
.69
and
P Value
multiple
regression results suggest that the
variability of water runoff cannot
be
statistically
explained
by the
independent variables that were included, at least as they were measured
in this study.
Sediment Yield Scatterplots and Bivariate Regression Results
The second objective of the study was to determine what part of the
variability of
sediment yield
could be explained with the topographic,
hydrologic, and soil variables.
The same procedure
runoff
here
analysis
was
followed
used for
the water
except for the inclusion of water
runoff as another independent variable.
The relationships between sediment
roughness
independent
0.01 level (Table 8).
distinguish
variables
This
yield and
the slope
and trail
were statistically significant at the
table
rainstorms on dry soils
is
divided
(n=60),
into
three
rainstorms on
parts to
pre-wetted
32
1000
♦ DI
B
♦♦
♦
•;=h:
800
^
600-
0
c
□
9
E
1
8
•♦ -
2
♦
0
Rainstorm I
♦
Rainstorm 2
400♦□gO
200
-
■
□ fi 8
□ Q
B
B
B
8♦
♦
♦ _
♦
Slope (%)
A.
Water runoff versus slope.
1000 I
f
•
- * $
800♦♦
B •
fa
0
600-
C
D%
B
S
B S
400-
♦
B ♦
«•
Q
♦
Q
♦
0
Rainstorm I
•
Rainstorm 2
B
♦
B
1
: : 4°
200
B •
-
♦
B
I 0
20
30
40
60
Soil moisture (%)
B.
Water runoff versus antecedent soil moisture.
Figure 5.
Scatterplots for water runoff and selected variables
33
1 0 0 0 -I
Q Rainstorm I
♦
Rainstorm 2
♦ q ♦
o
«0 ♦
G
Soil Resistance (kg/cm2)
C.
Water runoff versus antecedent soil resistance.
1 0 0 0 -I
Q ♦
♦ Q ♦
G no ♦
0
Rainstorm I
♦
Rainstorm 2
Trail roughness (mm)
D.
Water runoff versus antecedent trail roughness.
Figure 5.
Scatterplots
(continued).
for
water
runoff
and
selected
variables
34
Table 8.
Bivariate regression results treating
dependent variable.
First rainstorm
(n=60)
Variable
Slope
Antecedent
soil moist.
Antecedent
trail roughness
Antecedent
soil density
Water runoff
I
sediment
Second rainstorm
(n=108)
yield as
the
All rainstorms
(n=168)
R2
P Value
R2
P Value
R2
P Value
.117
.01
.139
.01
.127
.01
.0231
.34
.038
.11
.022
.12
.125
.01
.091
.01
.100
.01
.024
.026
.41
.29
.003
.001
.33
.79
.010
.001
.35
.59
represents inverse relationship
soils (n=108),
and all
rainstorms events (n=168).
Variations in slope
and antecedent trail roughness explained 12.7 and 10% of the variability
in sediment
yield, respectively
when all
the plots were examined. The
results were similar for all three groups of rainstorms because sediment
yield (like water runoff in the previous
section) was not statistically
related to variations in antecedent soil moisture.
points in
all five
scatterplots reproduced
The large scatter of
in Figure 6 confirms
the variability in sediment yield was only partially
variables measured in this study.
that must be understood to decipher
explained
that
by the
These results indicate the complexity
how the
natural controls
runoff and sediment yield operate on existing trails.
of water
35
200-1
$
Q
♦
♦
□
«3
Sediment yield (g)
D
•
0
100
»#
♦♦ ♦
0
•
♦
*
ill.
•
Q ftQ
&
♦
■♦6 Q
*
S . : :
e "#
Rainstorm I
Rainstorm 2
*
6
♦
0 0 Q
I*
30
10
M
Slope (%)
Iediment yield versus slope.
200i
Sedim ent yield (g)
♦
♦
♦
100
-
0
Rainstorm I
♦
Rainstorm 2
Q
*0
♦
B
♦
OT
0
I
10
'
1
I
20
I
'
30
I
40
'
I
50
'
I
60
Soil M oisture (%)
B.
Sediment yield versus antecedent soil moisture.
Figure 6.
Scatterplots of sediment yield and selected variables.
36
200 "I
♦
B
Bq
0
Rainstorm I
♦
Rainstorm 2
♦
□
♦ □
9 ♦
□
B
I.
a *$
Ot 10
♦
I
B
B
1
Q
I
20
B 0
B
1
30
I
1
40
I
1
50
I
60
Soil Resistance (kg/cm2)
C.
Sediment yield versus antecedent soil resistance.
♦♦♦
0
Rainstorm I
♦
Rainstorm 2
*#<
Trail roughness (mm)
D.
Sediment yield versus antecedent trail roughness.
Figure 6.
Scatterplots
(continued).
of
sediment
yield and
selected
variables
37
0
•
Rainstorm I
Rainstorm 2
W ater runoff (g)
E.
Sediment yield versus water runoff.
Figure 6.
Scatterplots
(continued).
of
sediment
yield
and
selected
variables
Sediment Yield Multiple Regression Results
The multiple
Table 9
show
regression results
that five
independent
variability in sediment yield.
indicate
that
steeper
for sediment
slopes
The
variables
first
and
the
four
plots
yield summarized in
explained
42% of
variables
in
cross-product
sediment yield.
sediment yield
variables
Steep
in many
slopes
explained
have
increased
of
the
positively
These
variability
in
correlated with
environments (e.g., Wischmeier and Smith, 1978)
and the combination of trail roughness
indicates
been
41%
Table 9
with the most clay and
terrain variability (roughness) produced the most sediment yield.
four
the
availability of
and sandy
sediment for
clay soils presumably
removal.
The final
38
Table 9.
Sediment yield multiple regression results.
Variable
Parameter
estimate
Intercept
Slope*clay1
Trail roughness*
sandy clay2
Slope
Trail roughness*
clay3
Soil Moisture*
loam4
30.64
5.79
0.18
0.20
1.49
I
2
3
4
Partial
R2
Model
R2
Prob>F
F
0.18
0.0001
0.0001
29.11
15.85
0.17
0.04
0.35
0.39
0.0001
0.0055
48.53
7.91
0.23
0.02
0.41
0.0231
5.26
0.39
0.01
0.42
0.0411
4.24
Slope*clay represents the slope continuous variable and the clay
indicator variable cross-product.
Trail roughness*sandy clay represents the trail roughness continuous
variable and the sandy clay indicator variable cross-product.
Trail roughness*clay
represents the trail roughness
continuous
variable and the clay indicator variable cross -product.
Soil moisture*loami represents the soil moisture continuous variable
and the loam indicator variable cross- product.
variable combines
soil moisture
and loam soils, but this variable only
explained 1% of the remaining variability in sediment yield.
Relative Impacts
Three approaches were used to
different
trail
development of
use
as
a
uses
on
water
several multiple
series
of
indicator
determine
runoff
and
the
relative
sediment
regression models
impacts of
yield:
I) the
that included trail
variables; 2) the comparison of water
runoff and sediment yield means by user type;
and 3)
the comparison of
soil resistance and trail roughness changes by user type over time.
39
Multiple Regression Results
Two
sets
of
indicator
regression models developed to
before) and
five trail
variables
were
used
differentiate four
user classes
in this
in
multiple
textural classes (as
part of
addition of four new indicator variables to accommodate
that four
the
the study.
The
trail use meant
continuous variables, seven indicator variables and 60 cross-
products were considered with water runoff as the dependent variable and
that five continuous variables, seven indicator variables, and 75 crossproducts were considered with sediment yield as the
Both models
used the
dependent variable.
results from the second rainstorms (n=108), since
these rainstorms followed user treatments (Table 2).
None of the independent variables were
the 0.05 significance level.
related to
water runoff at
This result suggests (once again) that the
variability of water runoff cannot
be
statistically
explained
by the
independent variables that were included, at least as they were measured
in the study.
Ten variables explained 70% of
the
variability
in
sediment yield
from the
sample plots (Table 10). This regression model included three
**
4
interaction variables with one indicator variable (Soil Moisture*Clay),
and
seven
interaction
variables,
two
indicator
continuous variable (e.g., Water Runoff*Clay*Horse).
represents
an
interaction
of
the
continuous
variables,
and one
The Slope variable
variable slope and the
default case for both series of indicator variables
(i.e., soil texture
and user treatment)..
This model
Overall,
the
clearly is
same
better than
variables
explained
the model
most
of
without trail user.
the
variability in
40
Table 10. Sediment yield multiple regression results.
Variable
Parameter
estimate
Intercept
29.33
Slope1
1.72
Slope*horse2
2.18
Trail roughness*clay3 0.21
Water runoff*sandy
clay*horse4
0.15
Water runoff*loam*
horse3
0.06
Soil moisture*clay6
1.17
Soil moisture*loam*
motorcycle7
0.91
Soil moisture*clay*
hiker8
-1.04
Soil moisture*sandy
clay*horse9
-2.12
Slope*clay*
motorcycle10
-4.60
Partial
R2
Model
R2
Prob>F
F
0.18
0.15
0.13
0.18
0.33
0.46
0.0001
0.0001
0.0001
0.0001
46.96
15.01
16.81
77.74
0.05
0.51
0.0001
20.66
0.04
0.04
0.55
0.59
0.0005
0.0001
12.86
27.31
0.04
0.63
0.0001
17.93
0.03
0.66
0.0012
11.17
0.02
0.68
0.0039
8.73
0.02
0.70
0.0142
6.24
1
Slope represents the slope continuous variable and the default case
for both series of indicator variables.
2 Slope*horse represents
the slope continuous variable and horse
indicator variable cross-product.
3 Trail roughness*clay
represents the trail roughness
continuous
variable and clay indicator variable cross-product.
4 Water runoff*sandy clay*horse represents the water runoff continuous
variable and clay and horse indicator variable cross-products.
5 Water runoff*loam*horse represents the
water runoff
continuous
variable and loam and horse indicator variable cross-products.
6 Soil moisture*clay represents the soil moisture continuous variable
and clay indicator variable cross-product.
7 Soil moisture*loam*motorcycle represents the soil moisture continuous
variable and loam and motorcycle indicator variable cross-products.
8 Soil moisture*clay*hiker
represents the soil moisture continuous
variable and clay and hiker indicator variable cross-products.
9 Soil moisture* sandy clay* horse
represents
the
soil
moisture
continuous variable and sandy clay and horse indicator variables
cross-products.
10 Slope*clay*motorcycle represents the slope continuous variable and
clay and motorcycle indicator variable cross-products.
sediment yield.
Hence
various combinations
of slope, trail roughness
41
and clay (with horses)
and
soil
texture
explained 46%
was
part
of
of the
all
seven of the cross-products that
explained another 24% of the variability.
and slope
appeared in
four, two,
variability by themselves
Soil
moisture, water runoff,
and one of the cross-products in the
second group, respectively.
This particular model was different from the earlier
five
trail
treatments,
represented
by
four
variables and 60 cross-products, were added.
The
one such that
additional
indicator
28% increase
in the
overall R2 (from 42% to 70%) must be attributed to the inclusion of this
second series of indicator variables and
of these
indicator variables
independent
variables
contributions
of
in
the
not surprisingly,
appeared in
the
seven of the ten significant
regression
different
of
their
cumulative
stood out: soil texture
Soil moisture,
model
variables
independent variables to the final result
guide
one or more
to
(R2 =
(Table
the
ten
10).
The
significant
70%) provided
a rough
impacts and confirmed that thrjee variables
(37%), slope
trail roughness
(35%), and
(both 13%),
user treatment (35%).
and water runoff (9%) made
much smaller contributions and soil resistance did not show up at all.
User treatments, of course, are of most interest here and following
the
last
approach,
their
independent variables can
be
contributions
isolated
as
to
the
follows:
ten
horse
significant
(appears 4
times that explain 26% of the variability in sediment yield), motorcycle
(2 times; 6%), and hiker (I
this
type
suggested.
to
increase
of
analysis
time; 3%).
further,
It
is very
difficult to take
although
certain
relationships are
Some examples follow: the
sediment
yield
(partial
Slope*Horse cross-product appears
R2 = 15%) but the cross-product
42
Slope*Clay*Motorcycle decreases
moisture appears
to
sediment yield (partial R2 = 2%).
increase
sediment
yield
when
Soil
the cross-product
includes clay (partial R2 = 4%) or loam and motorcycle use (partial R2 =
4%) but appears to decrease sediment yield when combined with Clay*Hiker
(partial R2
= 3%)
and Sandy
the user type helps
conducted,
to
although
Loam*Horse (partial R**2 = 2%).
explain
sediment
distinguishing
the
yield
from
Clearly,
the experiments
contributions of the specific
treatments clearly requires a different approach.
Multiple Comparison Test Results
The multiple comparsion test in SAS was used to perform a series of
difference
of
means
tests
and
assess
different trail users with respect to
the
relative
water runoff
impacts of the
and sediment yield.
However, the water runoff and sediment yield means compiled by treatment
type were replaced by least-squared means in these tests for the reasons
1 noted in
Chapter 2.
Table
11 shows the p values as well as the least
squared water runoff means by treatment type.
probability
such
that
(likelihood)
a
value
of
that
0.05
different
or
The p values indicate the
pairs of means are different
less
indicates
two
statistically
significant different means using a 0.05 level of significance.
Clearly, there
are no statistically significant different pairs of
means for water runoff
case of
(Table 11).
This
result is
reassuring in the
the first subset since the result confirms that the trails used
for the five treatments were similar
in
behavior prior
In other
words, the sample design
any
bias
to the
used in this study did
treatments.
not
introduce
terms
of
their
significant
water runoff
into the
43
Table 11.
Water runoff multiple comparsion results.
User treatment
Mean water
runoff (g)
Bicycle
Control
Hiker
Horse
Motorcycle
p values
A.
Prior to User Treatments (n = 54)
Bicycle
Control
Hiker
Horse
Motorcycle
B.
4650
4498
4713
4507
4889
5619
5862
5767
5628
5730
N/A
0.46
0.67
0.98
0.75
219
216
148
-140
-173
N/A
0.33
N/A
N/A
0.78
0.48
0.72
N/A
0.66
0.91
N/A
0.76
N/A
N/A
0.99
0.74
0.07
0.07
N/A
0.74
0.07
0.07
N/A
0.12
0.10
N/A
0.86
N/A
N/A
0.31
0.34
N/A
0.95
N/A
User Treatments on Dry Plots (n = 54)
Bicycle
Control
Hiker
Horse
Motorcycle
I
N/A
0.59
0.66
Prior Minus After, User Treatments (n = 54)
Bicycle
Control
Hiker
Horse
Motorcycle
D.
N/A
0.60
0.98
0.37
After User Treatments, Prewetted Plots (n = 54)
Bicycle
Control
Hiker
Horse
Motorcycle
C.
N/A1
0.70
0.88
0.72
0.57
4659
4550
4720
4462
4478
N/A
0.73
0.82
0.42
0.48
N/A
0.58
0.77
0.82
N/A represents no result
results.
The
results
in
multiple regression results in
Parts
B through D of Table 11 confirm the
that they
significantly alter runoff behavior.
show that
user type
did not
Table 12
displays the sediment yield p values as well as the least
squared sediment yield means by treatment
substantial
than
the
water
runoff
type.
results
differences
The results
because
significance.
The results from Part A. suggest that the trails used for
yield behavior
the treatments.
prior to
similar in
were statistically different from
bicycles) at
Tables
terms of
Trail
of
the
level
of
their sediment
plots used for hikers
other
groups (off-road
the 0.05 level of significance, and all groups if one uses
the 0.15 level of significance.
some bias
one
0.05
are some
significant
were not
the
there
statistically
the five treatment types
at
are more
for the
Thus, the sample design did incorporate
sediment yield
11
and
12
indicate
detachment
and
entrainment
results.
that
on
less
the
The results from Part A in
sediment
hiker
was
plots
available
since
for
water runoff
generated from the plots used for the different treatments prior
to the
user treatments themselves were not significantly different.
The sediment
horse plots
control,
produced
and
hiker
presumably due
used by
yields reported
to an
in Part
significantly
trail
plots
increase in
more
at
the
B of Table 12 indicate that
sediment
than
the bicycle,
0.05 level of significance,
sediment availability.
Trail plots
motorcycle were statistically significant from one of the other
groups (hiker) at the 0.05 level of significance and bicycle and control
trail plots if one uses the 0.15 level of significance.
The results
caution
the
mean
in Part
C of
Table 12
when looking at the results.
differences
between
the
indicate the need to exercise
This part of the table summarizes
sediment
prewetted and post treatment rainstorms by user.
yields produced from the
These comparisons are
45
Table 12. Sediment yield multiple comparison results.
User treatment
A.
Mean
Bicycle
sediment
yield (g)
69
59
38
60
65
N/A1
0.46
0.04
0.53
0.81
63
65
63
96
83
Motorcycle
N/A
0.70
N/A
N/A
0.18
N/A
N/A
0.40
0.70
N/A
0.24
N/A
N/A
0.01
0.65
N/A
0.03
N/A
p values
N/A
0.14
0.93
0.67
N/A
0.11
0.06
N/A
0.81
0.98
0.01
0.06
N/A
0.80
0.01
0.08
N/A
0.01
0.04
Prior minus After, User Treatments (n = 54)
Bicycle
Control
Hiker
Horse
Motorcycle
D.
Horse
After User Treatments, Prewetted Plots (n = 54)
Bicycle
Control
Hiker
Horse
Motorcycle
C.
Hiker
Prior to User Treatments (n = 54)
Bicycle
Control
Hiker
Horse
Motorcycle
B.
Control
N/A
0.57
0.19
0.03
0.33
-2
7
21
34
15
N/A
0.38
0.09
0.64
User Treatments on Dry Plots (n = 54)
Bicycle
Control
Hiker
Horse
Motorcycle
58
61
55
75
59
N/A
0.68
0.76
0.02
0.89
N/A
0.49
0.16
0.76
I N/A represents no result
important because
outset.
These
the hiker plots were significantly different from the
data
show
bicycle and hiker plots
respectively.
that
at the
This result
horse
0.05 and
plots are
different
0.10 levels
from
the
of significance,
focuses attention on the differences due to
46
the treatments; most importantly, it reduces
results from
second
largest
increase
treatments, and overall the
hooves and
of significant
Part B, presumably because it removes some or possibly all
of the bias inherent in the original data.
the
the number
in
Hikers,
sediment
horse and
therefore, produced
yield
following
hiker differences
horse
indicate that
feet make more sediment available for removal than wheels on
prewetted soils.
The results in Part
significantly
more
D of
Table 12
sediment
than
did
indicate horse
plots produced
bicycle, hiker, and motorcycle
plots at the 0.05 level of significance on the dry
plots as
well. [ The
final test will be to look at changes through time to see whether or not
changes
in
soil
interpretation
resistance
that
horse
and
plots
trail
roughness
produce
the
substantiate
the
greatest net change on
sample plots used in the study.
Changes in Soil Resistance and Trail Roughness Through Time
Figure 7 shows changes in soil resistance through time by treatment
type
in
which
SDl,
measurements taken when
prewetting
rainstorm
SD2,
the
(only
SD3, SD4, and SD5 represent soil resistance
plot
for
was
first
selected,
following the
prewetted treatments) , following the
first 50 passes, another 50 passes, and after the second rainstorm.
changes through
time for dry treatments indicate that 50 bicycle passes
increased soil resistance, whereas the other three users
resistance.
The
decreased soil
The application of the second 50 bicycle, hiker, and horse
passes increased soil resistance, although soil resistance on motorcycle
plots decreased.
The most dramatic change occurred as a result of
47
Hiker
Bicycle
AA
Soil resistance (kg/cm2)
Horse
Motorcycle
20
I
SR l
'
I
SR2
'
I
SR3
1
I
1
SR4
I
SR5
Soil resistance (kg/cm2)
n
Soil resistance (m easurem ent events)
hanges in soil resistance on dry trail plots.
40 -
30 -
SRl
SR2
SR3
SR4
SR5
Soil resistance (m easurem ent events)
B.
Changes in soil resistance on prewetted trail plots.
Figure 7.
Changes in soil resistance through time.
48
rainfall.
Soil resistance
decreased noticeably
plots following each rainstorm (SR2, SR5 and SR5
plots, respectively).
why different users
Overall,
produced
these results
statistically
for dry and prewetted
for prewetted
and dry
provide few clues about
significant
and different
sediment yields.
Figure 8 shows changes in trail roughness through time by treatment
type in which TRl, TR2, TR3,
TR4,
and
TR5
represent
trail roughness
measurements taken when the plot was first selected, following the first
rainstorm (only for
passes,
another
prewetted
50
passes,
roughness on dry trail
respects
hiker
produced
passes
and
plots
decreased
on
50
increased
trail
first 50
net
change
and
Trail
in some
For example, the first 50
and
the
second
50
passes
the first 50 bicycle and horse passes
passes
decreased
plots
bicycle and horse passes decreased
passes
the
second rainstorm.
little
roughness
prewetted
following
the
results.
Similarly,
increased and the second
changes
trail
plots),
after
showed
contradictory
increased roughness.
roughness
trail
were
trail
roughness.
trail
more
roughness. Trail
pronounced.
roughness,
whereas
Fifty
50 hiker
The application of the second 50
bicycle, horse, and hiker passes decreased trail roughness with the most
dramatic decreases
increased following
bicycle, horse,
occurring on horse and hiker plots.
the
hiker, and
the greatest net change.
resistance
application
results)
the
final
motorcycle plots,
The
provide
of
insights
rainstorm
on the
with horse plots showing
trail roughness
few
Trail roughness
results (like
about
the soil
why different users
produced statistically significant and different sediment yields.
49
214i
2 12 -
E
E
-#
#-
U
U3
l 210
0)
-
C
-A -
A-
-A
Hiker
-S
3
208"
£
E
£
— #—
Bicycle
— A—
Horse
— ■—
Motorcycle
206-
204'
TRl
TR2
TR3
TR4
TR5
Trail roughness (measurement events)
A.
Changes in trail roughness on dry trail plots.
212 -
E
E
210 -
%
0)
C
- o
208
-
---- • —
Bicycle
£
— A—
Horse
TZ
— ■—
Motorcycle
1
2
H
206-
Control
204
TRl
TR2
TR3
TR4
TR5
Trail roughness (measurement events)
B.
Hiker
Changes in trail roughness on prewetted trail plots.
Figure 8.
Changes in trail roughness through time.
50
CHAPTER FOUR
DISCUSSION
Regression Analysis
An understanding of the natural processes and controls operating on
trails is necessary before trail users
isolated
from
those
of
Dale, 1974; Helgath, 1975;
Summer,
This
1980
study
geomorphic
and
demonstrates
variables
and relationships.
important
1986;
and
added and
their impacts
the physical site characteristics (Weaver and
Bratton et
al., 1979;
Quinn et
al., 1980;
Jubenville and O'Sullivan, 1987; Kuss, 1987).
the
complexity
the
of
topographic,
soil,
and
difficulty of quantifying these effects
The results do
relationships
can be
between
confirm and/or
trail
users
clarify some
and
water
of the
runoff
and
sediment yield identified in other studies, and the following discussion
focuses on the broader significance of these results.
Bivariate
and
multiple
relationships between water runoff
regression
and
the
reveal
no
variables
significant
of
slope, soil
texture, antecedent soil moisture, trail roughness, and soil resistance.
There are two possible explanations: (I) the study
did not
measure the
variables in ways that the natural variability of the sample plots could
be expressed, and/or (2) the study
variables.
did
not
measure
all
the relevant
51
The first
explanation may
apply to
the antecedent soil moisture,
trail roughness, and soil resistance measurements.
example,
may
not
have
been
Trail roughness, for
sampled frequently enough (each time) to
accurately quantify the roughness of the plot surfaces.
encourages
ponding
infiltration, and
and
therefore
thereby
reduce
measurements (each
time) may
Similar problems may have
soil resistance
may
increase
runoff.
Trail roughness
residence
The
density
time
and
(number) of
have been too few to capture this effect.
affected
measurements.
the
antecedent
soil
moisture and
Hence, these variables may not have been
measured at enough sites to estimate their spatial variability and their
impact on infiltration rates and therefore runoff volumes.
Two
potentially
important
variables
(elapsed
time
of
water
application, and the swelling properties of clays found at the New World
Gulch site)
were not
measured.
Although 41.75 mm of water was applied
in every case, the application time
This variability
varied between
20 and
23 minutes.
meant that the application rate was reduced as much as
15% (109 mm hr-1) compared to the desired rate of 127 mm hr-1.
Most of
these problems occurred on the New World Gulch trail, since these sample
plots were located beside a stream
sediment load.
The
but
not
all
the
This
in the
sediment
state
of
container was
able to prevent
from being processed through the
affairs
meant
that
some
of the
as drip formers by the rainfall simulator were blocked for
some applications.
measurement
of water
of
rainfall simulator.
needles used
a considerable stream
practice of allowing the water to settle and using
only the upper portion
most
which carried
The
problems
potential
noted
above
impact
was
the
same
as
with the
since lower intensities may produce
52
more infiltration and less runoff.
The failure
to (I)
examine the
clay mineralogy
sites and (2) incorporate these results
in the
represent another
Smectites
present at the New
more water
important omission.
World Gulch
than non-swelling
have helped to decipher
some
regression analysis may
sample plots.
clays and
of
the
at the different
(swelling clays) are
These
hence the
clays can absorb
clay mineralogy may
differences
in
runoff behavior
between plots.
The bivariate
and multiple regression results were more successful
in explaining the variability in sediment yield.
antecedent
variables
trail
when
variables or
roughness
bivariate
(introduced as
models
multiple
a series
trail roughness
10%)
were
were
were significant independent
developed.
Five independent
to explain 42% of the variability
regression
was
used.
Soil texture
of indicator variables), slope, and antecedent
part
of
four,
two,
and
two
of
these terms,
The influence of slope and soil characteristics on trail
erosion has been documented
Dale, 1978;
=
cross-products combined
in sediment yield when
respectively.
(R2
Slope (R2 = 12.7%) and
Bratton et
al., 1980; Coleman, 1981;
in other
al., 1979;
Fish
et
studies (Bryan,
Klock and
al.,
1977; Weaver and
McColley, 1978; Quinn et
1981;
Kuss,
1983
and 1987;
Jubenville and. O'Sullivan, 1987).
Perhaps
the
most
important
runoff to explain any of the
itself or
This result
trails is
as one
of the
presumably
discovery
variability in
terms in
indicates
one or
that
was
the failure of water
sediment yield,
either by
more of the cross-products.
sediment
detachment rather than transport-limited.
yield
from existing
The exploration of
53
the relative
impact results/ confirms this
state of affairs, and these
results and their implications are examined next.
Relative Impact Analysis
Four new indicator variables and their cross-products were added to
the
multiple
regression
models
noted
impacts of the different, trail uses.
above
to examine the relative
There were
two major findings:
(I) no significant relationships were uncovered between water runoff and
the
indicator
combined
to
variables
explain
.70%
(as
of
noted
earlier),
and
(2), 10 variables
the variability in sediment yield.
second result is impressive and once again it shows,that
This
sediment yield
is probably detachment limited for the two trails examined in the study.
Treating the cumulative contributions of the different variables
final result
(R2 = 0.70) as a rough guide of their importance confirmed
that soil texture (37%), slope, (35%), and user treatment (35%)
most impact.
Water
runoff (9%)
smaller contributions.
and other
to the
was one
had the
of three variables that made
The importance of soil texture and slope in this
studies has already been noted.
However, the contribution of
treatment type and the limited role of water runoff tend to confirm that
sediment yield is detachment rather than transport-limited.
The multiple
comparisons test results further clarify the roles of
the different treatments and in particular
(hooves and
show that
feet) make more sediment available than wheels (motorcycles
and off-toad bicycles) on prewetted trails.
12 shows
of
that only
sediment
horses and hikers
Reviewing the data in Table
horses stand out from the other treatments in terms
production .and
that
the
other
treatments
produced no
54
significant
differences.
Three
problems
with
explain the lack of statistically significant
other treatments.
Two
of the
the study design may
differences between these
problems have to do with the concept of
geomorphic thresholds and the third with mechanical removal
of sediment
from the sample plots.
Schumm
(1977)
noted
that
under
different external and internal
stress conditions there could be a dramatic change in geomorphic systems
and significant
alterations of
must be applied varies
vary.
Kuss (1987)
with
the landscape.
the
environment,
very
large
storms
and
quantity
of
of use
and/or
change on existing trails.
type
hence
threshold values
applied this concept to recreational trails, noting
that almost any rainstorm or level
that
The minimum energy that
very
would impact
new trails but
heavy use is needed to initiate
This threshold level varies depending on the
use
along with climatic, soil, and topographic
variables.
Two problems with the current study
ability to
distinguish between
may have
inhibited our
hiker, off-road bicycle, and motorcycle
uses: (I) the limitations of the rainfall simulator,
and (2)
the small
number (100 passes) of treatments.
The most
important limitation
with the
modified Meeuwig rainfall
simulator is that it produces rainstorms of only one-third the intensity
of
natural
rainstorm
events.
This
comparing simulated and natural
was
a
very
noticeable
flowing down the
simulator events.
between the
trail
rainstorm events
difference
from
relationship
these
in
was
in the
very evident
field.
There
the quantities of water runoff
events
compared
to
the rainfall
The impact of rainfall intensity on the relationships
pre-existing
trail
condition
(i.e.,
trail
history) and
55
sediment production,
and threshold values is not obvious.
restrictions placed upon the
applied
were
in
this
attained,
study
duration and
intensity of
However, the
the rainstorms
decreased the likelihood that threshold values
especially
since
the
study
focused
exclusively
on
existing trail segments.
The
application
of
only
100
passes
(for
all four treatments)
probably contributed to the failure to attain the appropriate thresholds
for all
but horse
could help
traffic.
account for
Lull (1959) suggested impact per unit area
the relative
impacts of
different trail uses.^
Horses produce the greatest impact per unit area and as a result, horses
appeared to produce the
treatments may
greatest
net
change
in
this
study, pother
not have been applied enough times and/or in conjunction
with long and/or heavy
enough
simulated
rainstorms
for statistically
significant differences to show up between the other treatment types.
The failure
to measure
the quantities of soil removed with (stuck
to) feet and tires from the prewetted plots may have
contributed to the
V
lack
of
statistically
motorcycle,
mechanical
and
significant
bicycle
removal
of
prewetted plots and in
differences
plots
that
were
sediment
in
these
some cases - most of
between
uncovered
ways
as
the
well.
hiker,
The
was observed on most
the moist
soil was removed
from the sample plot and a dry soil surface was exposed as the treatment
was applied.
The quantities of sediment removed in these
measured when
clearly these
were measured in order
yield
and
precisely.
trail
losses need to be combined with those that
to quantify
roughness,
ways were not
soil
the relationships
between sediment
resistance and other variables more
56
Despite these problems, two sets of findings emerge from this study™
which probably apply to many (if not most) environments.
produced greater
sediment yields
then the
First, horses
other trail
uses.
other studies (Dale and Weayerft 1974; Weaver and Dale, 1978;
Several
Bratton et
al., 1979) reached similar conclusions, although further comparisons are
difficult because
finding from
of differences
this study
tended to occur on
application of
is that
in study
designs.
The second major
the largest and most frequent changes
prewetted trails.
This
result occurs
rainfall and the increase in soil moisture which follows
reduces soil resistance, which, in turn, reduces the
bear
a
moving
load.
Helgath
(1978), Bratton et al.
strong connection
and McColley
(1978) all
noted a
between soil moisture conditions and a soil's ability
to
soil moisture
the
decrease
is increased.
example, noted that trails located on
:wider, deeper,
trail's ability to
(1975), Bryan (1977), Weaver and Dale
(1979), Klock
to bear a moving load due
trails when
because the
and less
in
soil
Weaver
and Dale (1978), for
poorly drained
uniform (i.e.,
resistance along
soils are usually
greater roughness) than trails
located on well drained sites.
Conclusions
Land managers need to assess the carrying capacities of their trail
systems.
Trail use
in the last ten years has seen a dramatic increase
in off-road bicycles.
trails
as
hikers,
In many
horseback
cases off-road
riders
and
land
managers
with
some
new
the same
motorcyclists, so that this
additional use compounds erosional concerns.
provide
bicyclists use
data
The results
of this study
summarizing the relative
57
impacts
of
four
different
users
on two existing trails in southwest
Montana.
The four major findings of the study are: (I) the natural processes
occurring
on
the
complexity of
two
existing
trails
topographic, soil,
in this study demonstrate the
human, and
geomorphic variables: (2)
sediment yield from existing trails is detachment rather than transportlimited; (3)|horses produce significantly larger
compared
to
hikers,
off-road
bicycles,
quantities of sediment
and motorcycles; and (4) the
largest and most frequent changes tend to occur on prewetted trails.
Future research needs to examine higher intensities of
1000
passes)
and
increased, rainfall
conditions (longer rainstorms).
increase
the
development
possibility
of
thresholds on
models
intensities and soil saturation
Higher levels of use and rainfall would
of
threshold
which
simulate
existing trails
could help
values
the
the results
being
attainment
However,
a need
for this
user conflicts and
parks,
and
type of
environmental
municipal
us to
erosion
to assess the
it is
still not
larger areas. Clearly,
analysis prior to plans that reduce
impacts
recreation
in
national
centers.
The
forests, state
development
implementation of geographic information systems, models,
may help
of
The
from small sample plots like those in this study
can be extrapolated to other locations and/or to
there is
attained.
land managers
carrying capacities of their trail systems.
clear how
use (500 to
bridge this
gap and
therefore help
perceived intrinsic qualities of recreation areas.
and
and databases
in preserving the
58
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59
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impact on wildland conference; Seattle, WA. Portland,
OR: U.S.
Department of Agriculture,
Forest Service, Pacific Northwest
Region,
U.S. Department of Interior, National Park Service, pp.
66-70.
Kuas, F.R.,1983. Hiking boot impacts on woodland trails. Journal of Soil
and Water Conservation, 38: 119-121.
Kuss, F.R. and J.M. Morgan, 1980.
Estimating the physical carrying
capacity of recreation areas: A rationale for application
of the
Universal Soil Loss
Equation.
Journal of Soil and
Water
Conservation, 35: 87-89.
Kuss, F.R. and J.M. Morgan, 1984.
Using the USLE to estimate the
physical carrying capacity of natural areas for outdoor recreation
planning. Journal of Soil and Water Conservation, 39: 383-387.
Kuss, F.R., 1987. The effect of two hiking intensities on wildland trail
wear. In: Lucas, R.C.,
(ed.).
Proceedings of the
National
Wilderness Research Conference: Issues, State of Knowledge, Future
Directions. Ogden: U.S. Department of Agriculture, Forest Service,
Intermountain Research Station Research Note INT-212, pp. 158-165.
Law, J.O., 1941. Measurements of the fall velocity of waterdrops and
raindrops. Transactions of American Geophysical Union, 24: 452-460.
Law, J.O. and D.A. Parsons, 1943. The relation of raindrop size to
intensity. Transactions of American Geophysical Union, 24: 452-460.
Leonard, R. and H.J. Plumley, 1978.
The use of soils information for
dispersed recreational planning. In: Ittner, R.; Potter, D.; Agee,
J. and S. Anschell, (eds).
Proceedings — Recreation impact on
wildland conference;
Seattle, WA. Portland, OR: U.S. Department
of Agriculture, Forest Service, Pacific Northwest Region, U.S.
Department of Interior, National Park Service, pp 50-63.
Liddle, M.J., 1975.
A selective review of the ecological effects of
human trampling on natural ecosystems. Biological Conservation, 7:
17-36.
Liddle, M.J. and P. Greig-Smith, 1975. A survey of tracks and paths in a
sand dune ecosystem. Journal of Applied Ecology, 12: 893-930.
Liddle, M.J. and K.G. Moore, 1973. The microclimate of sand dune tracks:
The
relative
contribution
of vegetation removal and soil
compression. Journal of Applied Ecology, 8: 1057-1068.
62
Lull, H.H., 1959. Soil compaction on forest and range lands. Washington
D.C., U.S. Department of Agriculture, Forest Service, Miscellaneous
Publication 768, 33p.
McQuaid-Cook, J., 1978. Effects of hikers and horses on mountain trails.
Journal of Environmental Management, 6: 209-212.
Meeuwig, R.O., 1971a. Soil stability on high elevation rangeland in the
intermountain area. Ogden: U.S. Department of Agriculture, Forest
Service, Intermountain Forest and Range Experiment Station Research
Note INT-94, IOp.
Meeuwig, R.O., 1971b.
Infiltration
soils. Ogden: U.S. Department
Intermountain Forest and Range
INT-Ill, 15p.
and water repelency in granitic
of Agriculture, Forest Service,
Experiment Station Research Paper
National Park Service, 1975. Environmental
Assessment,
Pine Springs
development concept plan, Guadalupe Mountains National Park, Texas.
U.S. Department of Interior, Denver, CO, 125pp.
Quinn, N.W., R.P.C. Morgan, and A.J. Smith, 1980.
Simulation of soil
erosion induced by human trampling.
Journal of Environmental
Management, 10: 155-165.
Roberts, A.E., 1964. Geologic map of the Mystic Lake
quadrangle,
Montana. Denver, Colorado. U.S. Geological Survey, Miscellaneous
Geologic Investigations, Map 1-398.
Schmid, G.L., 1988. A rainfall simulator study of soil erodibility in
the Gallatin National Forest, southwest Montana: Unpublished M.S.
Thesis, Department of Plant and Soil Science, Montana State
University, 115p.
Schumm, S.A., 1977.
The Fluvial System. John Wiley, New York, 338p.
Shovic, H.F., 1989.
Personal
communication.
Agriculture, Forest Service, Bozeman, MT.
Soil Survey Staff, 1988. Keys to Soil
Cornell University. 280p.
U.S.
Department
Taxonomy (4th edition).
of
Ithaca:
Summer, R.M., 1980. Impact of horse traffic on trails in Rocky Mountain
National Park. Journal of Soil and Water Conservation, 35: 85-87.
Summer, R.M., 1986. Geomorphic impacts of horse traffic on
montane
landforms. Journal of Soil and Water Conservation, 41: 126-128.
Thien, S.J., 1979. A flow diagram for teaching texture by feel analysis.
Journal of Agronomic Education, 8: 54-55.
63
Weaver, T» and D. Dale, 1978.
Trampling effects of hikers, motorcycles
and horses in meadows and forests. Journal of Anplied Eco Ioctv. 15:
451-457.
Ifischmeier, tf.H. and D.D. Smith,
1978.
Pedicting rainfall
erosion
losses: A guide to conservation planning. Washington D.C.? U.S.
Department of Agriculture, Agriculture Handbook No.537 58p.
Young, R.A., 1979.
Interpretation of rainfall simulator data.
In:
Proceedings of the rainfall simulator workshop. Seattle, WA: U.S.
Department of Agriculture, Forest Service, ARH-W-IO, pp. 108-112.
r
64
APPENDIX
I
65
Table 13.
User
Emerald
data.
Prior
cond­
itio n 1
Lake trail slope,
Slope
(%)
Soil
moisture2
I
(%)
bike
bike
bike
b ik e
bike
bike
bike
bike
bike
bike
bike
bike
control
control
control
control
control
control
hiker
hiker
hiker
hiker
hiker
hiker
hiker
hiker
hiker
hiker
hiker
hiker
horse
horse
horse
horse
horse
horse
horse
horse
horse
horse
horse
D
D
D
D
D
D
P
P
P
P
P
P
B
B
B
B
B
B
D
D
D
D
D
D
P
P
P
P
P
P
D
D
D
D
D
D
P
P
P
P
P
I
I
I
12
12
15
I
2
2
14
13
10
2
2
2
15
12
11
2
2
2
9
11
12
2
2
2
13
10
14
2
3
3
11
13
11
5
5
6
9
9
27.8
22.4
50.2
21.8
32.8
21.9
16.7
23.5
20.8
30.0
26.6
25.6
8.1
8. 4
7. 8
16.1
18.3
22.6
3.3
8. 6
19.2
25.4
29.1
29.7
10.3
9. 4
9. 7
22.5
30.0
44 .0
32.5
30.5
30.0
17.0
23.2
4 7 .0
21.4
24.9
20.1
20 .0
24 .0
soil moisture,
and
Soil
Water
moisture3 ru n o ff4
2
I
(%)
(g)
27.8
22.4
50.2
21.8
32.8
21.9
39.9
55.6
24.2
49.4
29.6
27.3
12.0
16.0
17.8
24.2
18.5
25.4
3.3
8.6
19.2
25.4
29.1
29.7
19.2
22.6
23.8
32.7
37.3
33.2
32.5
30.5
30.0
17.0
23.2
47 .0
30.1
32.2
40 .0
35.1
37.5
-
377.4
513.7
249.2
734.3
465.6
424.8
469.4
847.5
459 .0
740.6
169.2
153.1
-
969.1
429.9
390.5
71 9 .0
263.7
774.5
-
-
57 .8
127 .4
985.1
735.9
155 .0
water runoff
Water
runoff3
2
(g)
810.1
432.3
762.7
924.8
147.7
566.3
354.4
799.7
788.9
732.3
851.3
794.9
399.8
262.4
500.0
753.4
772.8
909.5
260.0
654.8
843.4
205.6
696:7
806.6
981.9
123.7
276.7
982.2
781.4
723.6
788.5
799.4
487.3
231.7
588.1
173.4
307.3
755.5
156.8
592.1
201.3
66
Table 13.
User
horse
motor
motor
motor
motor
motor
motor
motor
motor
motor
motor
motor
motor
I
Emerald Lake trail
data (continued).
Prior
condition1
P
D
D
D
D
D
D
P
P
P
P
P
P
Slope
(%)
11
3
4
3
12
13
11
3
4
3
12
14
14
slope,
Soil
moisture2
I
(%)
19.6
20.1
18.4
20.2
21.3
18.0
20.4
20.6
51.9
50.3
26.2
32.4
31.7
soil moisture,
and
Soil
Water
moisture3 runoff4
2
I
(%)
(sr)
26.0
20.1
18.4
20.2
21.3
18.0
20.4
35.5
32.4
59.6
29.8
34.1
19.3
962.5
-
-
560.0
891.8
100.8
685.1
781.0
301.1
water runoff
Water
runoff0
2
(g )
784.3
318.9
900.3
187.6
396.8
799.6
328.0
531.2
749.8
774.4
145.6
649.9
901.1
Prior condition refers to state of sample plot prior to user
treatments such that D, PW, and B indicate treatments applied to
dry, prewetted, and both dry and prewetted situations, respectively.
2 Soil moisture I refers to antecedent soil moisture.
3 Soil moisture 2 refers to soil moisture prior to user treatments.
4 Water runoff I refers to runoff collected prior to user treatments.
5 Water runoff 2 refers to runoff collected after user treatments.
67
Table 14.
User
Emerald Lake trail soil resistance and sediment yield data.
Prior
SRl1
condition
bike
bi k e
bike
bike
bike
bike
bike
bike
bike
bike
bike
bike
control
control
control
control
control
control
hiker
hiker
hiker
hiker
hiker
hiker
hiker
hiker
hiker
hiker
hiker
hiker
horse
horse
horse
horse
horse
horse
horse
horse
horse
horse
horse
horse
D
D
D
D
D
D
P
P
P
P
P
P
B
B
B
B
B
B
D
D
D
D
D
D
P
P
P
P
P
P
D
D
D
D
D
D
P
P
P
P
P
P
37.02
37.64
39.55
31.99
34.59
40.8 0
42.75
42.53
44.33
36.75
39.28
39.03
19.25
27.71
37.61
30.24
34.15
37.48
23.58
16.04
28.42
37.22
34.78
33.95
33.16
38.58
33.14
40.2 2
47.96
29.85
33.55
27.22
31.54
31.62
29.18
31.84
27.94
41.22
29.25
50.22
42.1 2
39.19
SR22
-
31.83
36.83
37.88
34.28
35.48
41.11
14.38
16.58
22.96
27.62
31.43
33.65
-
29.78
33.52
28.85
36.72
31.89
25.47
-
24.27
32.74
23.50
41.88
30.48
30.68
SR33
SR44
(kg/cm2 )
44.72
43.41
41.18
37.49
35.59
42.33
30.05
37.62
39.03
32.48
33.72
40.68
39.94
42.98
39.67
37.81
39.94
39.32
31.38
27.48
36.92
32.70
36.53
34.43
-
-
-
-
-
-
-
-
-
-
-
-
26.85
18.70
26.99
37.18
33.82
29.28
32.03
30.22
28.28
38.88
31.35
23.13
34.48
29.16
28.52
30.20
26.65
32.56
22.58
32.11
20.72
42.82
29.55
27.44
24.75
21.07
30.65
33.05
33.37
32.18
29.47
30.81
28.98
38.72
33.23
21.87
34.38
28.04
28.55
28.53
33.72
30.85
23.22
34.08
20.72
38.90
30.02
29.53
SRS3 Sed im ent 6 Sediment7
yield
yield
22.54
33.32
28.39
34.76
36.55
37.56
25.62
18.42
27.50
32.52
34.51
37.68
12.50
21.84
21.83
28.61
33.08
37.68
19.85
15.35
31.81
33.28
32.63
26.26
31.38
31.15
29.88
30.25
29.36
25.15
24.65
19.29
20.34
21.22
24.48
19.26
17.79
25.45
23.30
39.02
26.09
28.76
I
2
(g)
(g)
-
4 0 .7
44 .6
20 .8
34 .2
40 .8
57 .8
13 .5
1 2 .0
16.1
4 4 .4
58 .4
15 .1
-
48.1
13.6
26.3
22 .8
11 .4
23 .1
-
24.0
7 6 .0
6 6 .5
4 1 .6
8 1 .0
106.3
50.9
22.2
27.0
55.1
78.0
70.8
38.8
58.1
33.0
60.8
54.2
41.5
20.3
14.6
11.9
35.4
28.1
52.8
13.9
13.4
32.6
46.3
38.5
38.9
55.8
32.2
55.2
31.3
24.1
44.1
53.0
39.4
65.1
81.0
84.0
59.1
61.5
1 1 1 .1
79.5
79.7
83.1
117.5
68
Table 14.
User
motor
motor
motor
motor
motor
motor
motor
motor
motor
motor
motor
motor
1
2
3
4
5
6
7
Emerald Lake trail soil resistance and sediment
(continued).
Prior
SRl1
condition
D
D
D
D
D
D
P
P
P
P
P
P
40.50
41.62
47.60
38.72
40.52
40.41
40.56
44.02
34.76
42.90
34.62
34.52
SR21
23
7
6
5
4
-
36.68
40.99
31.48
34.57
29.35
27.61
SR33
SR44
(kg/cm2)
41.24
41.31
45.73
43.08
38.99
36.35
33.98
35.23
29.33
33.98
27.06
23.04
37.98
42.55
45.00
43.50
34.32
34.38
36.84
37.18
29.17
32.90
28.83
26.40
yield
data
SRS3 Sediment6 Sediment7
yield
yield
33.06
35.63
39.46
36.58
26.65
20.20
32.42
38.23
28.27
28.44
26.35
16.45
I
2
(g)
(g)
-
-
-
78.6
24.3
10.6
83.4
45.5
39.2
24.4
27.1
39.7
27.8
86.6
14.8
81.8
84.0
85.3
112.6
114.8
51.3
SRl refers antecedent soil resistance.
SR2 represents to soil resistance prior to user treatments.
SR3 represents soil resistance after 50 passes of a user.
SR4 represents soil resistance after 100 passes of a user.
SRS represents soil resistance after final rainfall.
Sediment yield I refers to sediment collected prior to user treatment.
Sediment yield 2 refers to sediment collected after user treatment.
69
Table 15.
User
bike
b ik e
bike
b ik e
bike
bike
bike
bike
bike
bike
bike
bike
control
control
control
control
control
control
hiker
hiker
hiker
hiker
hiker
hiker
hiker
hiker
hiker
hiker
hiker
hiker
horse
horse
horse
horse
horse
horse
horse
horse
horse
horse
horse
horse
motor
motor
Emerald Lake t r a i l r o u g hn es s d a t a .
Prior
condition
D
D
D
D
D
D
P
P
P
P
P
P
B
B
B
B
B
B
D
D
D
D
D
D
P
P
P
P
P
P
D
D
D
D
D
D
P
P
P
P
P
P
D
D
TRl1
(mm)
202.31
201.17
199.52
205.52
208.80
205.60
200.62
196.17
202.06
200.29
204.15
200.05
195.72
195.46
198.97
201.92
206.86
203.48
192.45
200.89
206.37
212.57
216.43
213.45
211.28
199.35
208.54
199.69
199.34
195.37
198.25
203.95
201.57
200.97
214.74
197.51
204.12
207.48
206.72
205.40
206.83
211.32
200.23
209.09
TR22
(mm)
-
-
200.68
195.77
201.35
199.94
204.42
199.88
195.26
193.91
196.75
202.22
203.58
202.94
-
211.89
199.20
208.57
199.52
198.68
194.94
-
205.42
208.26
207.22
205.05
206.77
211.52
-
TR33
(mm)
TR44
(mm)
TR53
(mm)
202.63
200.89
198.77
204.20
209.80
204.88
201.00
195.78
201.62
199.77
204.32
198.11
203.62
201.18
197.60
204.97
209.74
206.51
199.98
196.43
198.54
200.03
203.74
197.60
203.12
201.11
198.40
204.85
208.55
204.43
200.42
196.58
198.68
200.12
203.85
197.66
195.80
193.92
195.22
201.68
203.97
202.88
189.17
200.15
204.66
212.45
215.12
213.63
210.12
199.12
208.43
198.62
198.17
194.98
198.37
203.11
201.65
200.80
213.45
197.80
206.48
208.23
207.35
206.17
206.65
210.75
199.63
209.22
-
-
-
-
-
-
-
-
-
-
-
-
190.92
201.52
206.29
213.74
214.63
213.29
212.43
199.89
209.23
199.25
199.17
195.57
198.69
203.29
203.42
200.57
215.26
198.09
205.14
208.12
207.15
205.75
207.40
212.54
200.32
209.09
191.34
201.98
204.40
212.86
216.26
212.89
209.72
199.14
208.17
198.72
199.17
194.49
198.88
205.45
203.66
201.02
212.85
197.03
205.28
208.52
205.98
205.58
206.12
211.14
199.92
209.48
70
Table 15.
Emerald Lake trail roughness data (continued).
User
Prior
condition
TRl1
(mm)
TR22
(mm)
TR33
(mm)
TR4«
(mm)
TR5a
(mm)
motor
motor
motor
motor
motor
motor
motor
motor
motor
motor
D
D
D
D
P
P
P
P
P
P
199.26
205.60
210.49
204.42
201.66
207.52
201.17
202.02
216.93
206.12
201.48
206.85
200.51
201.85
217.34
205.94
199.34
204.78
200.88
204.25
202.66
208.14
199.14
202.98
217.66
205.18
198.35
204.68
210.57
203.71
204.12
207.22
199.82
202.00
218.38
205.35
198.74
205.06
201.02
204.00
203.55
207.52
200.09
202.38
217.26
205.85
I
2
3
4
5
TRl
TR2
TR3
TR4
TR5
refers to antecedent trail
represents trail roughness
represents trail roughness
represents trail roughness
represents trail roughness
roughness.
prior to user treatment.
after 50 passes of a user.
after 100 passes of a user
after the final rainfall.
•
71
Ta b le 16.
User
Prior
cond­
itio n 1
4
4
4
13
15
19
4
5
5
16
14
11
3
3
4
15
17
21
4
4
4
13
13
14
3
2
2
13
13
13
5
5
5
15
15
11
5
5
5
12
13
Soil
moisture2
I
(%)
18.0
15.8
16.9
20.6
22.3
27.3
8.9
9.5
8.1
7.9
18.0
16.9
6.7
6.9
6.6
22.8
23.8
23.6
12.0
10.1
11.2
28.7
24.8
25.1
CO
D
D
D
D
D
D
P
P
P
P
P
P
B
B
B
B
B
B
D
D
D
D
D
D
P
P
P
P
P
P
D
D
D
D
D
D
P
P
P
P
P
Slope
(%)
OO
bike
bike
bike
bike
bike
bike
bike
bike
bike
bike
bike
bike
control
control
control
control
control
control
hiker
hiker
hiker
hiker
hiker
hiker
hiker
hiker
hiker
hiker
hiker
hiker
horse
horse
horse
horse
horse
horse
horse
horse
horse
horse
horse
New World Gulch t r a i l s l o p e , s o i l m o i s t u r e ,
data.
17.9
20.6
13.7
12.1
6.0
15.0
14.5
15.9
17.1
13.7
15.3
7.8
12.1
15.6
6.6
29.7
and w ate r r u n o f f
Soil
Water
moisture3 ru n o ff4
2
I
(%)
(g)
18.0
15.8
16.9
20.6
22.3
27.3
25.3
29.8
24.5
27.6
25.8
20.3
23.9
24.6
27.5
32.6
41.3
48.3
12.0
10.1
11.2
28.7
24.8
25.1
26.5
25.1
27.6
27.9
26.3
29.5
15.0
14.5
15.9
17.1
13.7
15.3
29.8
27.2
18.6
23.7
37.6
-
121.6
607.2
702.1
295.9
134.2
99.5
521.2
618.9
271.2
52.1
364.8
371.1
-
-
616.4
127.2
286.3
760.7
456.9
469.2
-
709.4
57.2
685.4
649.5
431.4
Water
runoff5
2
(g)
228.4
203.4
765.1
994.9
279.6
753.1
590.0
249.3
781.9
849.1
581.1
722.8
769.7
829.0
823.2
814.7
723.2
372.1
330.0
751.8
761.2
254.4
301.8
254.1
650.9
926.7
741.2
457.9
524.0
854.4
85.5
203.2
353.7
369.4
288.4
320.4
217.6
953.2
303.1
972.4
111.9
72
Table 16.
User
horse
motor
motor
motor
motor
motor
motor
motor
motor
motor
motor
motor
motor
1
2
3
4
5
New World Gulch trail slope,
data (continued).
Prior
condition1
P
D
D
D
D
D
D
P
P
P
P
P
P
Slope
(%)
17
5
5
6
9
14
19
4
3
5
14
19
21
Soil
moisture2
I
(%)
27.6
14.4
15.2
16.9
16.3
19.5
19.5
11.8
14.4
14.8
19.6
24.9
21.2
soil moisture, and water runoff
Soil
Water
moisture3 runoff4
2
I
(%)
(g)
34.0
14.4
15.2
16.9
16.3
19.5
19.5
24.7
26.3
29.6
35.6
31.6
41.6
896.2
-
916.8
931.6
226.8
671.3
766.2
579.8
Water
runoff3
2
(g)
715.3
941.1
815.3
506.5
408.5
901.1
358.6
666.6
649.5
631.5
497.4
493.6
827.9
Prior condition refers to states of sample plot prior to user
treatments.
D, PW and B indicate treatments were applied to dry,
prewetted, and both dry and prewetted situations, respectively.
Soil moisture I refers to initial antecedent soil moisture.
Soil moisture 2 refers to soil moisture prior to user treatments.
Water runoff I refers to collected runoff prior to user treatments.
Water runoff 2 refers to collected runoff after user treatments.
73
Ta b le 17.
User
New World
data.
Prior
SRl1
condition
bike
bike
bike
bike
bike
bike
bike
bike
bike
bike
bike
bike
control
control
control
control
control
control
hiker
hiker
hiker
hiker
hiker
hiker
hiker
hiker
hiker
hiker
hiker
hiker
horse
horse
horse
horse
horse
horse
horse
horse
horse
horse
horse
D
D
D
D
D
D
P
P
P
P
P
P
B
B
B
B
B
B
D
D
D
D
D
D
P
P
P
P
P
P
D
D
D
D
D
D
P
P
P
P
P
49.12
44.37
40.88
40.89
38.61
33.54
54.32
48.97
49.34
40.12
31.25
35.97
56.61
57.25
56.45
34.19
34.50
32.52
52.42
52.09
51.63
26.55
27.98
25.56
48.42
42.35
34.60
49.04
54.30
36.01
52.22
49.90
50.39
47.54
50.02
46.03
56.78
49.10
45.27
46.59
28.70
Gulch t r a i l s o i l
SR22
43.44
31.25
35.62
24.36
27.95
31.66
43.93
50.95
43.77
29.72
32.72
22.79
42.28
31.93
26.02
41.63
43.69
17.64
41.19
41.08
35.30
32.74
20.39
r e s i s t a n c e and
SR33
SR44
(kg/cm2 )
43.50
43.44
42.75
39.10
36.05
29.12
43.06
34.45
31.76
20.73
26.65
31.57
46.64
47.70
47.19
27.41
25.61
26.92
41.48
31.66
26.22
36.95
41.23
21.15
46.14
44.72
49.61
40.35
45.34
46.59
42.84
32.40
35.12
33.18
24.50
43.27
46.01
42.41
38.29
37.32
30.71
41.35
39.61
36.12
24.38
30.65
32.59
47.92
49.18
51.18
26.96
29.97
27.83
37.71
30.20
24.45
36.25
37.55
21.82
49.15
49.60
47.70
44.92
48.72
45.77
46.18
35.72
35.52
33.45
24.75
SR53
40.84
36.59
30.67
33.62
30.34
30.48
36.65
30.15
37.51
18.97
26.59
27.99
35.74
38.21
31.25
28.47
23.98
24.39
44.62
45.47
48.25
24.09
23.84
25.27
36.38
27.95
24.98
34.65
38.98
16.42
28.31
28.89
31.10
33.53
33.15
38.46
35.94
36.90
34.26
25.92
23.95
se d im en t
yield
S ed im en t6 Sediment7
yield
yield
I
2
(g)
(g)
69.3
138.6
94.3
161.2
48.2
55.2
49.9
87.7
111.1
48.1
137.8
58.8
75.2
49.7
73.7
81.4
47.0
56.8
58.5
57.1
37.3
50.4
56.3
78.6
86.3
98.8
96.6
60.4
62.4
69.2
117.7
85.3
102.1
84.4
138.2
84.4
90.7
92.4
81.0
87.4
106.8
84.9
95.9
93.7
95.3
106.5
66.7
70.4
81.9
126.3
64.2
132.8
115.2
73.5
178.5
63.2
97.5
115.1
6 6 .6
37.1
47.9
115.5
63.7
42.6
74
Table 17.
User
horse
motor
motor
motor
motor
motor
motor
motor
motor
motor
motor
motor
motor
1
2
3
4
5
6
7
New World Gulch trail soil resistance and sediment yield data
(continued).
Prior
SRli
condition
P
D
D
D
D
D
D
P
P
P
P
P
P
24.66
38.47
38.42
35.74
34.78
29.95
28.90
47.20
45.77
42.26
45.05
31.12
32.13
SR21
2
19.98
-
35.90
35.92
28.38
26.01
28.46
22.25
SR33
SR445
(kg/cm2)
22.25
37.13
37.25
36.05
30.22
30.80
28.72
32.82
29.92
28.53
27.30
21.50
26.29
20.39
34.99
33.95
35.24
31.47
27.32
28.38
30.98
33.12
27.70
27.57
22.68
26.75
SR53
19.92
30.55
29.42
28.72
22.44
22.55
26.16
31.02
32.68
25.24
25.96
10.35
25.05
Sediment6 Sediment7
yield
yield
I
2
(g)
(g)
85.9
137.6
41.6
61.3
33.1
66.5
41.2
102.8
67.4
74.0
92.4
90.2
85.9
141.8
-
37.4
96.9
60.8
72.8
156.2
153.0
SRl refers antecedent soil resistance.
SR2 represents to soil resistance prior to user treatments.
SR3 represents soil resistance after 50 passes of a user.
SR4 represents soil resistance after 100 passes of a user.
SR5 represents soil resistance after final rainfall.
Sediment yield I refers to sediment collected prior to user treatment.
Sediment yield 2 refers to sediment collected after user treatment.
75
Table 18.
User
bike
bike
bike
bike
bike
bike
bike
bike
bike
bike
bike
bike
control
control
control
control
control
control
hiker
hiker
hiker
hiker
hiker
hiker
hiker
hiker
hiker
hiker
hiker
hiker
horse
horse
horse
horse
horse
horse
horse
horse
horse
horse
horse
horse
motor
motor
New World Gulch trail roughness data.
Prior
condition
TRl1
(mm)
D
D
D
D
D
D
P
P
P
P
P
P
B
197.00
195.97
197.00
237.69
229.12
247.48
201.40
204.78
208.74
242.38
243.29
227.97
206.92
215.11
217.58
220.74
213.58
218.46
205.48
204.05
202.32
236.45
236.60
227.02
211.20
195.40
197.14
201.38
197.75
237.65
196.80
197.75
199.29
243.52
238.11
223.62
210.83
218.85
206.00
200.08
234.95
218.17
210.51
211.14
B
B
B
B
B
D
D
D
D
D
D
P
P
P
P
P
P
D
D
D
D
D
D
P
P
P
P
P
P
D
D
TR22
(mm)
-
203.12
206.22
210.77
242.09
244.22
228.18
207.65
217.08
219.11
219.89
214.12
219.02
212.95
195.72
197.48
201.82
198.08
236.26
209.77
220.42
206.49
200.34
236.09
217.82
-
-
TR33
(mm)
TR44
(mm)
TR53
(mm)
197.62
196.60
196.22
237.05
229.28
253.91
203.23
206.82
211.75
238.00
243.66
228.55
-
197.85
197.09
196.86
237.94
229.05
247.83
202.17
206.72
210.48
242.62
242.55
228.00
-
198.78
198.22
196.52
238.83
230.35
246.78
202.28
207.20
210.80
243.06
243.28
228.09
206.22
216.22
218.98
220.22
214.20
219.43
207.28
202.22
202.14
237.83
235.57
226.57
210.35
196.55
198.43
201.69
198.51
234.65
198.29
196.86
200.34
246.52
240.68
223.68
204.42
219.66
206.46
210.43
235.55
214.37
211.98
210.80
-
205.85
203.26
202.08
235.86
239.31
227.08
212.49
196.15
197.92
202.38
198.29
235.55
196.20
200.95
199.03
243.95
239.94
223.05
206.66
218.83
205.60
199.98
232.62
216.00
211.82
210.74
-
206.54
202.62
201.58
230.06
237.26
226.20
210.12
196.29
197.68
201.66
198.08
235.43
196.40
196.35
200.12
244.00
239.86
222.54
203.57
218.68
206.35
200.49
232.00
213.25
211.43
211.15
76
Table 18.
New World Gulch trail roughness data (continued).
User
Prior
condition
TRl1
(mm)
motor
motor
motor
motor
motor
motor
motor
motor
motor
motor
D
D
D
D
P
P
P
P
P
P
205.35
208.26
208.98
204.42
215.40
205.25
194.71
233.88
214.49
208.80
1
2
3
4
5
TR21
2
(mm)
-
216.45
205.65
194.66
234.80
215.72
209.43
TR334
5
(mm)
206.28
209.18
209.48
202.40
217.02
206.05
195.25
231.09
215.77
209.60
TR4«
(mm)
TRS=
(mm)
205.62
208.92
213.57
200.85
215.94
205.98
195.12
232.88
216.14
208.45
205.15
208.00
207.92
202.20
216.94
206.35
195.22
233.20
216.08
208.72
TRl refers to antecedent trail roughness.
TR2
represents trail roughness prior to user treatment.
TR3
represents trail roughness after 50 passes of a user.
TR4
represents trail roughness after 100 passes of a user.
TR5
represents trail roughness after the final rainfall.
MONTANA STATE UNIVERSITY LIBRARIES
I lll ll I I I i iI I I
3 1762 10088526 6