Greeline to Greenline Width
(GGW)
Non vegetated
Channel width
GGW



The non-vegetated distance between
the greenlines on each side of the
stream.
Using “Greenline rules” adds
precision to the measurement
Consistent GGW measurements
between observers validates the
greenline rules
Greenline-to-greenline width
Why take out the Vegetated Islands?
GGW approximates the scoured, or
non-vegetated width of the channel.
•Including islands adds width bias to
the estimate.
•Typically, we are estimating the
width of the “active” channel which
normally excludes vegetated bars and
islands.
•
A
C
B
B
Excludes veg
A
Includes bar
B
Exclude
Vegetation
Using a Laser Range Finder
Measures Horizontal Distance
(Accuracy - .01 meter)
D
Ө
GGW = cos Ө x D
Long Tom Creek
GGW
Upstream – light
impacts- 4m wide
2007
Downstream – heavy
impacts – 8 m wide
Long Tom Creek - Repeatability
Non-Vegetated Width Variability in Means, by Stream
8
7
Width (m)
6
5
TimR1
ErvR
TimL
TimR2
ErvL
6.48 (6.33-6.64)
4.39 (4.21-4.48)
4
3
1.78 (1.54-1.96)
2
1
0
WF Long Tom
Lower Long Tom
Stream reach
Upper Long Tom
Long Tom - Spatial Variability
Widths by distance along WF Long Tom Creek
12.00
Right bank
10.00
Left bank
Width (m)
Channel shape
8.00
N = 125 each side
6.00
alpha = .05
4.00
2.00
0.00
0
50
100
150
Distance upstream (m)
200
250
Conclusions - GGW




Reasonably unbiased: measured
data
Repeatable: Precision +- 5%
Accurate: Alpha = .05 with sample
sizes of from 50 to 100
Fast: <1 hour with laser range
finder, 1.5 hours with rod
Streambank Stability
Frame
On
Greenline
Streambank
Uncovered/
Unstable (UU)
Scour Line
What are streambanks?
Streambanks
That portion of the channel cross section:
- above the scour line, and
- below the lip of the first bench.
Scour Line: The lower elevational limit of a streambank. On erosionsal banks, the scour line is
the elevation of seasonal scour – often marked by undercut sod.
Bank
Scour line: On depositional banks, the scour line is the lower limit of sod-forming or
perennial vegetation – or the potential lower limit of sod-forming vegetation
Top of Bank – lip of first bench
Bank
Bank
What is the streambank evaluated? Above the Scour Line and at the steepest angle to the water surface.
On erosional banks the measurement extends up to the bench or top of cutbank.
On bars, it extends up to the top of the depositional feature: approximately bankfull level
First Bench
Scour Line
The first bench is the first relatively flat area
above the scour line or edge of the water
Step 1 Depositional or Erosional?

Depositional: deposited
bars – Point bars, lateral bars

Erosional: all other banks
Step 2 Covered or Uncovered


Covered: at least 50% foliar cover
of perennial vegetation (including
roots); at least 50% cover of cobbles
six inches or larger; at least 50%
cover of anchored large woody debris
(LWD) with a diameter of four inches
or greater;
Uncovered: applies to all banks
that are not “Covered” as defined
above.
Erosional (E)
Uncovered (U)
Depositional (D)
Covered (C)
Step 3 IF “Erosional”, are any of
the following present….
Fracture: a visible crack is observed. The fracture has not
separated into two separate components or blocks of a bank.
Cracks indicate a high risk of breakdown. The fracture feature
must be at least ¼ of a frame length or greater than about 13
cm of bank length.
Slump: streambank that has obviously slipped down
resulting in a separate block of soil/sod separated from the
bank.
Slough or “sluff soil material has fallen from and
accumulated near the base of the bank. “Slough” typically
occurs on banks that are steep and bare.
Eroding: applies to banks that are bare and steep (within 10
degrees of vertical), usually located on the outside curves of
meander bends in the stream.
Slumps
Fracture
Erosional (E)
Uncovered (U)
Slough (SL)
Depositional (D)
Covered (C)
Trampling by large herbivores has caused obvious
“slumping.” The slump is greater than one-fourth
of the plot length and is recorded (S).
There is a hoofprint in the bank, but no slump or slough is
associated with the hoofprint; therefore, there is no indicator of
instability so covered (C) and absent (A) are recorded.
Scour line
Where is the scour line?
Where is the top of the bank?
What about entrenched
channels?
The key: whether slough enters the
stream
Where is the first Bench?
Slough enters stream – at or above
the angle of repose
Residual Pool and Pool
Frequency
Lisle (1987) suggested a quick
method for measuring pool
frequency and residual depth:
1.
2.
3.
4.
Measure distance between depth
measurements along the thalweg.
At thalweg depth measurements
note the habitat (pool or riffle crest)
To compute residual depth subtract
depth at riffle crests from maximum
pool depth
The pool frequency is the number of
pools per distance along the
thalweg.
Pool – Riffle Sequence –
Woody Creek
Potential Indicator of fish habitat
quality


Pools are vital components of fish habitat
in streams, especially for larger fish
Mossop and Bradford (2006) found a
positive correlation between mean
maximum residual pool depth and the
density of Chinook salmon
FWS – Habitat Suitability Index
for Cutthroat Trout
Influenced by grazing
disturbances
In a review of the literature,
Powell et al. (2000) concluded that
channel characteristics, including
channel width and depth, as well
as bed material were often
reported to be affected by
livestock grazing in riparian areas.
Testing Repeatability

Conducted 4 tests in 2009
• Little Truckee (9 crews)
• SF Beaver (4 crews)
• Summit Creek (6 crews)
• Smith Creek (3 crews)
Little Truckee River



Pool structure – Complex
RESIDUAL DEPTH
• Average Difference between observers
= .01 meter
• Maximum Difference = .13 meters
• Coefficient of variation (SD/Mean) =
24%
POOLS PER MILE
• Average Difference between observers =
12 per mile
• Maximum Difference = 21 per mile
• CV = 16%
Smith Creek



POOL STRUCTURE - simple
RESIDUAL DEPTH
• Average Difference between observers
= .01 meter
• Maximum Difference = .1 meters
• Coefficient of variation (SD/Mean) =
11%
POOLS PER MILE
• Average Difference between observers =
7 per mile
• Maximum Difference = 10 per mile
• CV = 16%
Substrate
Size distribution and Percent Fines
Substrate


Channel instability often leads to
channel widening, where the energy
balance between erosion and
deposition shifts toward deposition
and therefore fining of the substrate
(Powell et al. 2000).
Such increases in fines may degrade
aquatic habitat by restricting the
living spaces of substrate-dwelling
organisms and by limiting the oxygen
transfer to incubating eggs (Powell et
al. 2000)
Substrate


Pebble counting is a relatively efficient way to
measure substrate size distributions and percent
fines
Site variability can be problematic. Pebble counts
normally detect change when relatively high
magnitude impacts are evaluated. The
confidence interval is 11% using 200 particles
minimum per site. The confidence interval
narrows with more particles.
Tendancy to underestimate
percent fines

As noted by Bunte and Abt (2001), using
different methods to sample substrate at
the same location may yield different
results. Thus trend over time should be
based upon the same technique applied to
each sampling event.
Spatial variability

Sampling the entire length of the DMA (20
channel widths) is recommended to
ensure spatial variability is accounted for
in the sample scheme. If not, variability
through time may reflect spatial
heterogeneity more than actual
adjustments in substrate size
distributions.
Substrate Size Distribution
At 20 plot locations (200 samples)
Sampled between the greenlines
10 particles per plot (cross
section) selected at heel and toe
across the channel from greenline
to greenline – total 200 particles
Can lay measuring rod across
small streams to obtain sampling
intervals
Small streams – use a
measuring rod
Source

The guidelines on bed material sampling
provided by Bunte and Abt (2001) include
an excellent summary of the literature and
the principal base reference for this
protocol.
Why the Substrate Template?
“Operator training is extremely important.
When selecting particles from a
predefined streambed location, or even
when measuring particle sizes in a
preselected sample of rocks, there is
less variability between the results of
experienced operators than between
those obtained by novices. Field
personnel need to be trained to perform
procedures accurately, to avoid bias,
and to use equipment that reduces
operator induced error.” (Bunte and Abt
(2001)