U.S. Department of the Interior

U.S. Geological Survey

Fact Sheet 2019–3024

April 2019

Strait Bering

Bering Sea

Chukchi

Sea ARCTIC OCEAN

GULF OF ALASKA

Anchorage

Fairbanks

UNITED STATES

B

CANADA

R O O K S

R A N G E

ALASKA

N O R T H S L O P E

Kodiak

Island

170° 160° 150° 140° 130° 120°

70°

65°

60°

55° 0 300 MILES

0 300 KILOMETERS

Base image copyright from Esri, 2019. Map projection: Alaska Albers 

Conic. Horizontal datum is North American Datum of 1983.

Remote Sensing of Streamflow in Alaska Rivers—New Technology 

to Improve Safety and Expand Coverage of USGS Streamgaging

Why Measuring River Flow in Alaska 

Is Important

The U.S. Geological Survey (USGS) 

monitors water level (water surface elevation 

relative to an arbitrary datum) and measures 

streamflow in Alaska rivers to compute and 

compile river flow records for use by water resource planners, engineers, and 

land managers to design infrastructure, manage floodplains, and protect life, 

property, and aquatic resources. Alaska has over 800,000 miles of rivers including 

the Yukon River, the third longest river in the United States. These rivers are 

home to rare and important ecosystems and are used for recreation, hydropower 

generation, commercial fishing, and transportation. River flow measurements are 

essential for wise and safe development and use of Alaska rivers.

Figure 1. Dots show U.S. Geological Survey continuous river monitoring stations 

in Alaska as of 2018.

How Streamflow Is Measured Today

The USGS monitors water levels at more than 

100 streamgages in Alaska (fig. 1). Converting the 

streamgage water-level data into flow (or discharge) 

data requires hydrologic technicians to travel to river 

monitoring sites to collect direct flow measurements several times each year, 

including when rivers are flooded or covered with ice.

Hydrologic technicians regularly visit streamgages to measure river flow 

with handheld mechanical-impeller and hydroacoustic velocity meters with 

depth-sounding rods, or with an Acoustic Doppler Current Profiler (ADCP) that 

simultaneously measures water velocity and channel depth across the channel 

as it is pulled across the river surface by boat or cable (fig. 2). Many streamgage 

stations are remote and are only accessible by airplane or helicopter, requiring 

multiple days per visit. When rivers are covered with ice, typically from 

December through April, technicians drill holes through the ice to access the 

flowing water. River conditions are particularly hazardous during periods of ice 

breakup, when shore ice becomes unstable and ice floes move downstream.

Alaska River Facts

• Alaska has more than 800,000 miles of 

rivers and streams

• The Yukon River is the third longest river in 

the U.S. and second largest by flow volume

• Alaska rivers are usually frozen from 

December to April

• Alaska rivers drain to the Arctic Ocean, 

Bering Sea, and Pacific Ocean

USGS Flow Measurements in Alaska 

• USGS has partnered with NASA and is 

using data from NASA satellites.

• Alaska has one streamgage per 6,500 

mi2, compared to one streamgage per 400 

mi2 in the lower 48 States

• Hydrologic technicians visit streamgages 

six to ten times annually for 

maintenance and data collection

• On frozen rivers, USGS hydrologic 

technicians drill holes through the ice to 

take streamflow measurements

• Streamflow measurements at remote 

sites can take up to 3 days, including 

travel time, and some locations are only 

accessible by airplane or helicopter 

because of difficult terrain
A

Figure 2. U.S. Geological Survey hydrologic technicians 

collecting flow measurement data using in-water methods: 

(A) technician measuring flow depth and velocity at 

Evaingiknuk Creek near Noatak, Alaska; (B) technician 

augering ice to measure flow depth and velocity beneath the 

ice at Copper River near Cordova, Alaska; and (C) technician 

ferrying hydroacoustic equipment across Ikalukrok Creek 

near Kivalina, Alaska. 

AAXXXX_fig 01

Bluetooth antenna

RS-485 

interface

Aluminum plate 

for structural 

support

Supply lines

Brown=12V

White=OV/GRND

B

B

C

A

Figure 3. River surface velocities can be measured with 

(A) doppler radar mounted on small unmanned aerial 

systems as shown during field trials (B) at the Tanana River, 

Nenana, Alaska, July 2018. 

How Remote Sensing Can Improve Flow Measurement

Measuring river flow with remote sensing from satellites 

or aircraft (such as airplanes, helicopters, and unmanned aerial 

systems [UAS], or drones) can improve field safety and increase 

the total number and frequency of streamflow measurements, 

particularly for isolated rivers (fig. 3). Measurements can be 

taken during floods without exposing hydrologic technicians 

to dangerous river conditions. River flow measurement during 

ice breakup, which is difficult or impossible with traditional 

streamgaging techniques, becomes feasible with remote sensing. 

Remote sensing can help the USGS expand its monitoring 

network to cover more rivers, which is a major focus of the 

agency, particularly in Alaska (figs. 4 and 5).

New Remote Sensing Technology

Monitoring flow from the air or space is now possible using 

new technology and improvements in:

• the number of satellites;

• image processing techniques;

• unmanned aerial systems (UAS);

• advanced sensors for water-velocity and depth measurements; 

and,

• data storage and processing capabilities.

Analysis of remote sensing data yields estimates of river 

properties such as width, depth, water-surface slope, and watersurface velocity. River discharge is calculated by using hydrodynamic 

equations and measurements made by multiple sensors.

Surface Velocimetry

Measurement of water velocities on a river surface (surface 

velocimetry) is based on time-lapse images of the river surface and 

computer analysis of features moving on the river surface. Surface 

velocity data is then paired with channel bathymetry (water depth) 

data to calculate river discharge.

Image processing techniques such as Large-Scale Particle 

Image Velocimetry (LSPIV), calculate displacement of 

surface features such as foam or other debris, on a river surface. 
Figure 4. Measurements of river flow have been tested 

using a helicopter-mounted thermal camera, optical 

camera, and lidar; all fully integrated with a global 

navigation satellite system (GNSS) for direct 

geo-referencing, Anchorage, Alaska. 

Displacement distances and time between frames in the video are

used to compute flow velocities at many locations on the river 

surface (fig. 6). A variety of velocimetry techniques and imagery 

can be used to compute surface velocities. For example, thermal

cameras with high sensitivity to medium and long wavelengths 

can resolve features on the water surface caused by turbulent 

mixing. Thermal images are then processed to compute surface 

velocities based on the displacement of thermal features on the

surface as they move downstream (Legleiter and others, 2017).

Doppler Radar

Doppler radar technology, commonly used to track weather 

conditions and to measure vehicle speeds, can also be used to 

measure river surface velocities. Radar signals are transmitted 

towards the surface of the water and the reflected signal is 

shifted to a different frequency because the water is moving. 

This Doppler shift is used to calculate the velocity of the 

water surface relative to the radar. Both hand-held and bridgemounted radar Doppler velocity sensors are currently in use 

(Fulton and Ostrowski, 2008) and UAS mounted sensors are 

being tested.

Bathymetry Measurement

Knowledge of river depths (bathymetry) improves flow 

estimates. In clear-flowing rivers, sunlight reflected from the 

river bed can be measured to estimate bathymetry using spectral 

analysis, as demonstrated using reflectance measurements 

made from a bridge over the Salcha River in Alaska (Legleiter 

and others, 2017). In other rivers, airplane-mounted green 

Figure 5. River surface height and slope of large rivers can be 

measured by radar altimetry from satellites, such as the Jason-2 

OSTM shown here (image from NASA Jet Propulsion Laboratory, 

California Institute of Technology, https://www.jpl.nasa.gov/news/

news.php?feature=6890).

lidar (light detection and ranging) systems have been used to 

measure river bathymetry (fig. 7; Miller-Corbett, 2018). New 

technologies are being tested for UAS deployment including 

midar (multispectral imaging, detection and active reflectance) 

that uses multispectral light-emitting diodes (LEDs). These 

techniques are limited to relatively clear and shallow streams in 

which sunlight, lasers, or LED light can penetrate the water and 

reflect from the river bed back to the surface (fig. 8).

Ground penetrating radar is another promising technology 

for measuring bathymetry (Costa and others, 2006) and, unlike the 

other methods mentioned here, can also be used in turbid or muddy 

streams but performs poorly in high conductance (salty) water and 

against channel beds comprised of low-reflectance materials.

Altimetry

Satellites are used to measure river surface altitudes or 

elevations of wide rivers (more than 100 m) with an accuracy 

of 5 cm (Zlinszki and others, 2017) and can measure changes in 

river elevation with accuracies of less than 1 cm. River surface 

altitudes measured by satellite can be converted to river flow 

values using hydraulic equations such as Manning’s equation 

(Bjerklie and others, 2018) and at-many-stations hydraulic 

geometry equations (Gleason and Smith, 2014).

The Future of Remote Sensing Streamgages in Alaska

In Alaska, the USGS is establishing remote sensing 

streamgages on river reaches that have frequent satellite 

overpasses and channel shapes—ideally straight and steep, 

more than 100 m wide, and with little turbulence or flood debris 

such as boulders or trees that are well suited for measurement 

by satellite. The first four remote sensing streamgages have 

been established at the Tanana River at Nenana, Tanana River 

at Fairbanks, Susitna River at Sunshine, and Yukon River 

at Stevens Village. These locations coincide with existing 

streamgaging stations so that the accuracy of remotely sensed 

flow measurements can be assessed. The goal is to expand the 

network of remote sensing streamgages to cover additional 

rivers, including some of the many rivers in Alaska that are not 

currently monitored. As methods for computing river flow with 

remote sensing become more refined from this effort in Alaska, 

100

100 200 300 400 500 600 700 800 900 1000 1100 1200

0

0

200

300

400

500

600

700

X [pixel]

Y [pixel]

EXPLANATION

Displacement [pixel]

0 10

Figure 6. Infrared image overlain with flow velocity vectors derived 

from infrared video velocimetry analysis, Chena River, Alaska (from 

Kinzel and others, 2017). 
156

150

125

100

75

53

0 500 1,000 Meters

Meters

B

Figure 7. Images showing (A) topography and (B) laser intensity, 

Matanuska River near Palmer, Alaska. Water surfaces shown in blue. 

Data collected by helicopter-mounted Riegl VQ-580 lidar (light detection 

and ranging) scanning system (data from U.S. Army Corp of Engineers).

A

Distance from right bank, in meters

Distance from left bank, in meters

0.5

1.5

0 10 20 30 40 50 60

100 20 30 40 50 60 70 80 90

2.5

Depth, in meters

A. Chena River

B. Salcha River

0

1

2

3

0.5

1.5

2.5

0

1

2

ADCP OBRA L11,1

EXPLANATION

Figure. 8. River bathymetry for two study sites in Alaska, the Chena 

and Salcha Rivers. Field-based ADCP data provided measured 

ground truth for evaluating depths estimated from field spectra based 

on Optimal Band Ratio Analysis (OBRA) and from the length scale 

(L_{11,1}) of turbulent features expressed at the river surface and 

captured in thermal image time series

they can be applied elsewhere in the United States and the world to 

provide valuable data for use in emergency response, water-supply 

development, hydroelectric planning and operation, transportation, 

and natural resource management.

References 

Bjerklie, D.J., Birkett, C.M., Jones, J.W., Carabajal, C., Rover, 

J., Fulton, J.W., and Pierre-André, G., 2018, Satellite remote 

sensing estimation of river discharge—Application to the 

Yukon River, Alaska: Journal of Hydrology, v. 561, p. 1,000–

1,018, 10.1016/j.jhydrol.2018.04.005.

Costa, J. E., Cheng, R.T., Haeni, F.P., Melcher, N., Spicer, N.R., 

Hayes, E., Plant, W., Hayes, K., Teague, C., and Barrick, D., 

2006, Use of radars to monitor stream discharge by noncontact methods: Water Resources Research, v. 42, W07422, 14 

p., doi:10.1029/2005WR004430.

Fulton, J.W., and Ostrowski, J., 2008, Measuring real-time 

streamflow using emerging technologies—Radar, hydroacoustics, and the probability concept: Journal of Hydrology, v. 357, 

p. 1–10, 10.1016/j.jhydrol.2008.03.028.

Gleason, C.J., and Smith, L.C., 2014, Toward global mapping 

of river discharge using satellite images and at-many-stations 

hydraulic geometry: Proceedings of the National Academy 

of Sciences of the United States of America, v. 111, n. 13, p. 

4788–4791, doi:10.1073/pnas.1317606111.

Kinzel P.J., Legleiter C., Nelson J.M., and Conaway J.S., 2017, 

Remote measurement of surface-water velocity using infrared 

videography and PIV–A proof-of-concept for Alaskan rivers: 

E-proceedings of the 37th IAHR World Congress, August 

13–18, 2017, Kuala Lumpur, Malaysia, 9 p.

Legleiter, C.J., Kinzel, P.J., and Nelson, J.M., 2017, Remote 

measurement of river discharge using thermal particle image 

velocimetry (PIV) and various sources of bathymetric information: Journal of Hydrology, v. 554 (Supplement C), p. 

490–506.

Miller-Corbett, C., 2018, A comparison of synthetic flowpaths 

derived from light detection and ranging topobathymetric data 

and National Hydrography Dataset high resolution flowlines: 

U.S. Geological Survey Open-File Report 2018–1058, 29 p., 

https://doi.org/10.3133/ofr20181058.

Zlinszky A., Boergens E., Glira P., and Pfeifer N., 2017, Airborne laser scanning for calibration and validation of inshore 

satellite altimetry—A proof of concept: Remote Sensing of 

Environment, v. 197, August 2017, p. 35–42.

Authors: Jeff Conaway, Alaska Science Center; John Eggleston, WMA–

Observing Systems Division; Carl J. Legleiter, WMA–Integrated Modeling 

and Prediction Division; John W. Jones, WMA–Observing Systems 

Division; Paul J. Kinzel, WMA–Integrated Modeling and Prediction 

Division; John W. Fulton, Colorado Water Science Center

Photographs: All photographs by U.S. Geological Survey

For more information: U.S. Geological Survey, 4210 University Drive, 

Anchorage, AK 99508

http://alaska.usgs.gov

Publishing support provided by the U.S. Geological Survey 

Science Publishing Network, Tacoma Publishing Service Center

ISSN 2327-6932 (online)

https://.doi.org/10.3133/fs20193024