Sandbars and Sediment Storage in Marble and 

Grand Canyons: Response to Recent High-flow 

Experiments and Long-term Trends

GCMRC Project 3 (now Project B)

Paul Grams1, Daniel Buscombe2, Tom Gushue1, Dan Hamill2, Joseph 

Hazel2, Matt Kaplinski2, Keith Kohl1, Erich Mueller1,3, Robert Ross1, 

Robert Tusso1

1U.S. Geological Survey, Southwest Biological Science Center, Grand Canyon 

Monitoring and Research Center

2Northern Arizona University

3University of Wyoming

U.S. Department of Interior

U.S. Geological Survey
Overview

• Scientific background (geomorphology and sediment 

transport processes relevant to sandbars and HFE’s).

• Summary of FY 2015-2017 Project 3 Objectives and 

Accomplishments

• A bit more detail on:

• Sandbar change and sand budget

• Advances in remote sensing of the river bed

• Future work
The sand budget is affected by disruption of upstream sand supply 

(presence of Lake Powell) and change in flow regime (dam operations)

Grand Canyon

~ 16% of pre-dam sand supply

Little Colorado River

Marble Canyon

~ 6% of pre-dam 

sand supply

Glen Canyon Dam

Paria River

Colorado River at Lees Ferry, AZ

85 to 95% reduction in supply coupled with ~20% 

reduction in mean annual flow  sediment deficit

Topping et al. (2000)
Pre-dam: 

• Annual floods

• Abundant sand 

supply

• Large sandbars

Post-dam I: 

• Daily small floods

• Limited sand supply

• Eroding sandbars

• Unplanned floods (spills)

Pre-dam Post-dam I

1952 2003

Ratio of observed sandbar area to maximum 

potential sandbar area (reach-average)

1935

1965-19961996-2009

1935

1965-19961996-2009

1935

1965-19961996-2009

1935

1965-19961996-

1935

2009

1965-19961996-2009

RM:

29-35

RM:

0-8

RM:

42-49

RM:

50-56

RM:

60-72

About 25% reduction in 

sandbar area in Marble Canyon

Schmidt et al. (2004); 

Ross and Grams (2015)
Pre-dam: 

• Annual floods

• Abundant sand 

supply

• Large sandbars

Post-dam I: 

• Daily small floods

• Limited sand supply

• Eroding sandbars

• Unplanned floods (spills)

Science and Management 

Questions:

• With frequent HFEs, will 

sandbars increase in size 

and abundance?

• Will frequent HFEs cause 

sand supply in channel to 

decrease and exacerbate 

sediment deficit?

Pre-dam Post-dam I

Post-dam II:

• Restricted hydropower 

operations

• High Flow Experiments 

(HFEs) 

• triggered by sand 

supply from Paria River

Pre-HFE Post-HFE
Sandbars

• Geomorphic Framework – active 

sandbars part of natural river system

• Recreational Campsites

• Aquatic Habitats – nursery habitats for 

native fish

• Terrestrial Habitat – substrate for 

riparian vegetation

• Archaeological Site Preservation – most 

archaeological sites buried in sand/silt

campsites

sand dunes 

(archeological sites)
Building sandbars with HFEs

Debris Fan

Eroded sandbar before 

HFE

Sandbar following HFE

HFE inundates sandbar

HFEs transfer sand from channel and lowelevation parts of eddies to sandbars 

along channel margins
Project 3 – Overview

• 8 Project elements involving sandbar and sand storage 

monitoring and research

• 28 peer-reviewed publications (2015-2017)

– 13 Journal articles

– 7 Proceedings papers

– 4 MS or PhD theses

– 4 USGS Reports

– 2 USGS data releases

• At least 6 additional articles or reports in preparation

• Sandbar photographs and data on GCMRC website 

(www.gcmrc.gov/sandbar)
Sandbar surveys with digital cameras

SfM sandbar surveys (3.1.3)

• Objective:

– Evaluate SfM (structure-from-motion) 

photogrammetry for “surveying” sandbars

Rebecca Rossi, MS Thesis
Sandbar surveys with digital cameras

SfM sandbar surveys (3.1.3) Rebecca Rossi, MS Thesis

Bare sand bar = Very good agreement 

with conventional survey

Sandbar with dense vegetation = greater 

uncertainty compared with conventional 

survey

• Results:

– Method yields accurate, high-resolution topographic 

data, suitable for detailed investigations

– But, no substantial time or cost savings over conventional 

survey methods

– MS Thesis completed, journal article in preparation

• Management implications

– We have a tested method for high-resolution surveys.

– Would be most efficient if data were collected with 

drones
Historical Sandbar Topography

Historical sandbar topography (3.1.4)

• Objective:

– Reconstruct sandbar topography from 1984 

aerial photographs for long-term monitoring 

sites

• Results:

– Developed workflow for accurate 

reconstructions

– Applied method to 6 sites

– Additional 4 sites to be completed this year

– Report in preparation

• Management implications

– We have a working method for precise 

reconstructions of topography from old 

(1984) air photos.

– Provides perspective for sandbar size 

produced by HFEs compared to size that was 

produced by larger floods.

Sandbars reconstructed from 1984 images

RM 47

RM 55

Preliminary results, do not cite
Historical Sandbar Topography

Historical sandbar topography (3.1.4)

Sandbars reconstructed from 1984 images

RM 47

RM 55

1984

2008

1984

Larger sandbar now 

compared to 1983-84 floods

Much smaller sandbar 

following recent HFEs 

compared to 1983-84 

floods

Preliminary results, do not cite
Sand Bedload Transport

12:40 :00: 9536.6 c.f.s 

•. ~ ~ ""''' '"'~: 

''.-. .. ~ 

so 100 150 200 250 

X[m) 

8:45 :00: 12000. c.f.s 

E 20 

> 

0 so 150 200 250 

X [m) 

._s 20 

> 

0 

9:16:00: 37893. c.f.s 

0 

X[m) 
Sand Bedload Transport

• Objective:

– Improve our estimates of sand transport by 

better inclusion of bedload transport

• Results:

– Made repeat measurements of bed 

topography at different flows to measure 

sand dune migration

– Used data to estimate transport and develop 

model for general application

– Journal article in preparation

• Management implications:

– Less uncertainty in estimates of sand 

transport and better model predictions for 

sand transport

Sand bedload transport (3.4) Tom Ashley, PhD Thesis
Control network and survey support

Control network (3.5)

• Objective:

– Establish and maintain network of control 

points (bench marks) throughout Grand 

Canyon

• Results:

– Network involves over 12,000 

measurements made by conventional survey 

and GNSS (GPS)

– Both absolute and relative accuracy of are 

well-constrained (~ 3 cm relative accuracy at 

95% confidence)

– Points are “blue-booked” (published with 

National Geodetic Survey) and in GCMRC 

control database

• Management implications

– Network provides stable reference for longterm monitoring.
Sandbar monitoring and modeling

Sandbar monitoring and modeling (3.1.1 & 3.3)

• Objective:

– Track results of HFEs and long-term trends in sandbars 

and sandbar campsites

– Improve capacity for predictive modeling

• Results:

– HFEs cause increases in sandbar size at a majority of 

long-term monitoring sites

– Sandbars consistently larger than periods without 

HFEs

– Developed new framework for classifying sandbars 

(Mueller et al., 2018)

– Developed new predictive model for sandbar 

response (Mueller et al., in prep.)

– Developing improved methods for monitoring 

sandbars at higher frequency

• Management implications

– HFEs continue to be effective in building sandbars

– Sandbars continue to erode following HFEs (continued 

HFEs required to maintain sandbar size)

– Not seeing substantial cumulative increase in sandbar 

size
Types of sandbar response to HFEs

Deposition 

Erosion 

No change 

~ 50-60% of sites

~ 10% of sites

~ 30-40% of sites

Grams et al. (2018)
HFE Response: 2012 to 2017

11/17/2014

5/7/2015

Most sandbars erode near pre-HFE 

size within 6 to 12 months.

9/23/2015 Grams et al. (2018)

Erosion of HFE deposit
Sandbar monitoring

• Groups 1a and 1b: 

– relatively large and mostly open bare 

sandbars

– Strongest response to HFEs

• Groups 1c and 3: 

– heavily vegetated bars

– Less dynamic around HFEs, tend to 

accumulate over time

• Groups 2 and 4:

– Mostly smaller bars adjacent to debris 

fans (don’t project into eddy)

– Tend to be most stable

– HFEs still improve condition by filling 

gullies and burying/removing debris

New analysis of sandbar trends 

based on grouping of bars by 

morphology and average response

Mueller et al. (2018); unpublished data, do not cite
More than half of the long-term 

monitoring sites contain 

“typical” reattachment bars 

with varying degrees of 

vegetation encroachment

Sandbar Groupings

30-mile 172-mile 55-mile Mueller et al. (2018)
More than half of the long-term 

monitoring sites contain 

“typical” reattachment bars 

with varying degrees of 

vegetation encroachment

Sandbar Groupings

Additional common 

sandbar types

wave-dominated Upper pool Fan-attached

Mueller et al. (2018)
Sandbar monitoring Large and open bars 

with strongest 

response to HFEs

Smaller bars 

adjacent to 

fans: stable, 

HFE’s fill gullies 

and cover 

debris

Most heavily 

vegetated 

bars: aggrade

Mueller et al. (2018); 

unpublished data, do not cite
Large short-term changes

in sandbar area

Registered image with

identified sandbar outline 

Rectified image 

and sandbar

• Quantification of changes in 

sandbar area at short 

(daily/weekly/monthly) 

intervals

• Currently relies on semiautomatic delineation of 

sandbar from images

• Objective:

– Quantitative measure of HFE response and 

post-HFE erosion rates

Grams et al. (2018)
Completely automated 

sandbar segmentation

• Segmenting sandbars from imagery, by training artificial neural networks to 

reliably recognize features (sand, water, rocks, vegetation)

• Each image takes only a minute or two

• Thousands of images processed per day

“water” “rock” “sand” “vegetation”

Create “library” of image parts for each class, the neural network “learns” to identify
Completely automated 

sandbar segmentation

Initial classification using image 

library and “deep neural network”

Final classification using 

“conditional random 

fields”

• Promising method for processing the images from remote cameras

• Plan to start using for data analysis this year or next

• Evaluating extending the method to canyon-wide remote sensing images

meters

meters
Sandbar modeling

• Predicts changes in sandbar volume 

based on:

– Discharge (observed)

– Suspended sediment concentration 

(observed)

– Sand grain size (observed)

– Water depth

– Sandbar and eddy size (measured)

– Eddy exchange rate (estimated)

– Sandbar erosion rate (estimated)

New physically-based model for 

sandbar response based on bar 

groupings

Observed sandbar volume

Modeled sandbar volume

Mueller et al. (in prep.)

- Model can be applied to predict bar response over 

periods of many years with HFEs of different 

magnitude and frequency

- Can be improved with additional measurements of bar 

erosion from analysis of remote camera images

preliminary results, do not cite
• Erosion in Lower Marble Canyon (2009-2012)

– Sandbars: ~ 22 cm decrease in sand thickness (10% 

of vol. change)

– Eddies: ~ 3 cm increase in sand thickness (7% of vol. 

change)

– Channel: ~ 10 cm decrease in sand thickness (83% of 

vol. change)

• Erosion in Eastern Grand Canyon (2011-2014)

– Sandbars: ~ 8 cm increase in sand thickness (3% of 

vol. change)

– Eddies: ~ 9 cm decrease in sand thickness (12% of 

vol. change)

– Channel: ~ 10 cm decrease in sand thickness (85% of 

vol. change)

- Equalization without HFEs  erosion everywhere

- Equalization followed by HFEs  erosion in channel 

and eddies, but deposition on bars

- Sand patches decrease in thickness but are stable in 

area and location during evacuation

Maps of sand 

distribution, 

erosion and 

deposition, 

RM 30-87

Sand-storage changes in Lower Marble Canyon 

and Eastern Grand Canyon (RM 30 – 87)

preliminary results, do not cite
• Equalization flows in 2011 caused net erosion

• Net accumulation in Lower Marble Canyon

– Consistent sand inputs from Paria River (5 out of 7 

years)

– Export during HFEs has been less than accumulation

• Net evacuation in Eastern Grand Canyon

– Paria inputs either retained in Marble Canyon or 

transported through (to Lake Mead)

– Period of low inputs from Little Colorado River

2011 2017

Sand-storage changes in Lower Marble Canyon 

and Eastern Grand Canyon (RM 30 – 87) LMC flux mass-balance

EGC flux mass-balance

EGC measured storage change

LMC measured storage change

preliminary results, do not cite
Project B: FY 2018-20

• Sandbar monitoring and research

– Continue annual monitoring

– Maintain remote cameras for HFE response

– Develop quantitative metrics for monitoring from remote camera 

images (see Ryan Lima poster)

– Complete publications (modeling, analysis of 1984 images)

• Sand storage change

– Complete processing of data collected in 2017

– Work on control network in preparation for mapping between RM 87 

and 166 in next work plan

– Analysis and reporting on measurements of sand thickness (see 

Andrew Platt poster)

– Mapping of Lower Marble Canyon and Eastern Grand Canyon in 2019

– Publications and reporting on sand storage changes in Marble and 

Eastern Grand Canyon
Referenced cited:

Grams, P. E., Tusso, R. B., & Buscombe, D. (2018). Automated Remote Cameras for Monitoring Alluvial Sandbars 

on the Colorado River in Grand Canyon, Arizona. U.S. Geological Survey Open-File Report 2018-1019, 61. 

https://doi.org/10.3133/ofr20181019

Mueller, E. R., Grams, P. E., Hazel, J. E., & Schmidt, J. C. (2018). Variability in eddy sandbar dynamics during two 

decades of controlled flooding of the Colorado River in the Grand Canyon. Sedimentary Geology, 363, 181–199. 

https://doi.org/10.1016/j.sedgeo.2017.11.007

Ross, R. P., & Grams, P. E. (2015). Long-term monitoring of sandbars on the Colorado River in Grand Canyon 

using remote sensing. In Proceedings of the 5th Federal Interagency Hydrologic Modeling Conference and the 

10th Federal Interagency Sedimentation Conference (pp. 86–96). Reno, Nev. Retrieved from 

http://acwi.gov/sos/pubs/3rdJFIC/index.html

Schmidt, J. C., Topping, D. J., Grams, P. E., & Hazel Jr., J. E. (2004). System-wide changes in the distribution of fine 

sediment in the Colorado River corridor between Glen Canyon Dam and Bright Angel Creek, Arizona. Flagstaff, 

Ariz.: U.S. Geological Survey, Grand Canyon Monitoring and Research Center. Retrieved from 

http://www.gcmrc.gov/library/reports/Physical/Fine_Sed/Schmidt2004.pdf