Testing a basin-scale method for predicting salmonid spawning habitat via grain size

Allison Pfeiffer, Noah Finnegan, and Madeline RichardsUC Santa Cruz, Dept. of Earth and Planetary Science

photo: Morgan Bond

Goal: Use DEMs and minimal field work to identify reaches of

good spawning habitat at the landscape scale

Goal: Use DEMs and minimal field work to identify reaches of

good spawning habitat at the landscape scale

Approach: grain size (D50), movable fraction (Fm)Buffington et al. (2004); Riebe et al. (2014)

Goal: Use DEMs and minimal field work to identify reaches of

good spawning habitat at the landscape scale

Approach: grain size (D50), movable fraction (Fm)Buffington et al. (2004); Riebe et al. (2014)

Tests: 1. Accurately predict Fm (grain size) from topography?2. Fm correlate with spawning density? 3. Do calibrations apply across the landscape?

Study site: Scott Creek Santa Cruz Mtns, CA

Area = 80 km2

Steelhead (and a few Coho)

1-m airborne LiDAR

Spawning site survey(7 years, NMFS)

Movable Fraction (Fm)

Threshold grain size(12 cm)

Fm= 90%

Fm= 10%

lFish length

(65 cm)

Moveable fraction (Fm)

Riebe et al. (2014); Overstreet et al. (2016)

redd

Movable Fraction (Fm)

Threshold grain size(12 cm)

Fm= 90%

Fm= 10%

lFish length

(65 cm)

Moveable fraction (Fm)

Riebe et al. (2014); Overstreet et al. (2016)

redd

Standard approach:

Grain size reflects total bankfull shear stress.

τ bf = τ c

Predicting grain size

Snyder et al. (2013)

Bankfull shear stress can be higher than critical stress.

Grain size reflects only a part of the total bankfull shear stress.

e.g. Buffington and Montgomery (1999 a, b)

Partitioned shear stress

τ bf > τ c

Predicting D50

D50 =β ρg h S

(ρs- ρ) g τ*crit

Input for:whole drainage each site

slope-variable

Lamb et. al (2008)

from LiDARh= c Area fτc is some portion of τbf:

β(τ bf) = τ c

β = Partitioning coefficient

Field measurements

Grain size

71 sites, 100m apart

Wolman (1954) pebble counts

Depth measurements

high flow indicators

Predict D50

Successful prediction of grain size +/- 30% in 10/11 reaches

β = 0.28

Predict grain size distribution for each site

D50

lognormal distribution

Median:

D84

D50= constant

~ Standard deviation:Grain size, log scale

coun

t

D50

Grain size distribution Fm

Grain size, log scale

For any site, calculate movable fraction of the bed area by avg. fish

fract

ion

Threshold grain size

lFish length

Fm

Tests:

✓ Accurately predict Fm (grain size) from topography?

2. Fm correlate with spawning density?

3. Do calibrations apply across the landscape?

Is moveable fraction driving redd site selection?

photo: Morgan Bond

Moveable fraction (Fm)

Low spawning Fm < 0.7.

Range of spawning Fm > 0.9.

Tests:

✓ Accurately predict Fm (grain size) from topography?

✓* Fm correlate with spawning density?

3. Do calibrations apply across the landscape?

Do the same calibrations work in other drainages in the area?

photo: Morgan Bond

Test the Scott Creek calibrations (β, h = cAf ) in the San Lorenzo River.

Testing predictions in San Lorenzo

Using the Scott Creekcalibrations (β, h = cAf )

1:1

Tests:

✓ Accurately predict Fm (grain size) from topography?

✓* Fm correlate with spawning density?

✓ Do calibrations apply across the landscape?

Predicted D50

0

1

Predicted Fm

Inputs: - LiDAR DEM- h = cAf- β

0.01

0.20

Predicted D50 (m)

5 km

Predicted Fm

0

1

Predicted Fm

Inputs: - LiDAR DEM- h = cAf- β- D84/D50- Fork length

5 km

Predicted Fm

0

1

Predicted Fm

Inputs: - LiDAR DEM- h = cAf- β- D84/D50- Fork length

5 km

Predicted Fm

0

1

Predicted Fm

Inputs: - LiDAR DEM- h = cAf- β- D84/D50- Fork length

5 km

0

1

Predicted Fm

Two small dams. Which do you remove?

We can use maps of Fm to quantify the distribution of potential habitat.

Field work assistants: NMFS, Nic Retford, Larry Bush, Neal Hetzel & many othersFunding: California Department of Fish and Wildlife to Sean Hayes, NMFS LiDAR: NSF grant #: DEB1009018 to Jon Moore. Collected by NCALM

photo: Morgan BondContact: ampfeiff@ucsc.edu