Trends in Stream Temperature in the Snake River

Identification

1.	Description

This feature examines water temperatures in the Snake River, which winds through Washington,
Oregon, and Idaho. Water temperature is particularly important in the Snake River and other rivers of
the Columbia Basin because they are home to many species of salmonids (salmon and related fish),
which require relatively cold water to migrate, spawn, and thrive. Migrating salmon are an important
part of the ecology of the Pacific Northwest, and they also play vital cultural, spiritual, and economic
roles for tribal nations in the region. Rising air temperatures associated with climate change can also
raise water temperatures, which could make some watersheds less hospitable to salmon. This feature
focuses on temperatures in August, which is when rivers and streams in the region typically register
their annual maximum temperatures.

2.	Revision History

August 2016: Feature published.

Data Sources

3.	Data Sources

This feature is derived from stream temperature measurements collected by the U.S. Geological
Survey's (USGS's) long-term stream gauge on the Snake River near Anatone, Washington (site #
13334300). The site is located in eastern Washington, near Nez Perce tribal lands. Data from this site
have been collected since October 1959.

4.	Data Availability

Daily temperature data, including maximum, minimum, and mean temperatures, are publicly available
through the USGS National Water Information System (NWIS) database at:
http://waterdata.usgs.gov/nwis. Records for this particular location can be found at:
http://waterdata.usgs.gov/nwis/inventorv/Psite no=13334300&agencv cd=USGS. Site information

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available at this link includes location, streamflow, temperature, partial pressure of dissolved gases, and
gauge height.

Methodology	

5.	Data Collection

Temperature data at the sampling location near Anatone are collected hourly by automated USGS
monitoring equipment. Daily temperature data are available beginning in 1959, although there is a gap
in data availability between May 1984 and May 1986. This feature starts with data from 1960.

6.	Derivation

Many aspects of water temperature (e.g., maximum, average, variability) are relevant to salmonid
physiology and therefore relevant to an assessment of habitat quality. This feature focuses on average
August water temperatures, based on an arithmetic mean of all daily mean temperatures during the
month. During early years of the temperature record, only daily maximum and minimum temperatures
are available. For these years, mean daily stream temperatures were calculated as the average of the
daily maxima and minima (Isaak et al., 2012).

7.	Quality Assurance and Quality Control

Temperature data are included in the NWIS database only after extensive quality assurance procedures
have been met. Sensors are regularly inspected, and data are labeled as provisional until they are
reviewed and receive approval. Only approved, final data were used in this feature.

Analysis	

8.	Comparability Over Time and Space

This feature includes data from one study site, precluding any issues of comparability over space. NWIS
quality assurance requirements help to ensure comparability over time.

9.	Data Limitations

Factors that may impact the confidence, application, or conclusions drawn from this feature are as
follows:

1. Many factors other than climate change can lead to warming of rivers. Specific anthropogenic
effects that may result in variable heat loading include:

•	Removal of streamside vegetation through urban development, agriculture, grazing, and
forestry.

•	Changes in stream shape due to bank erosion. Wider and shallower streams increase the
surface area that is subject to solar radiation and atmospheric heat exchange.

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•	Water withdrawals for industrial, municipal, and agricultural uses.

•	Heated water discharges from industrial facilities, wastewater treatment plants, and
irrigation return canals.

•	Reduced groundwater flow due to river channeling, straightening, or diking. Increased
prevalence of impervious surfaces due to urban development also reduces groundwater
flow.

•	Dams and associated reservoirs. Reservoirs may increase maximum temperatures by
limiting water movement in shallow areas, and because of their increased heat capacity,
they may also reduce diurnal temperature variability and seasonal change.

10.	Sources of Uncertainty

Uncertainty has not been quantified for this feature. The uncertainty associated with temperature
measurements taken at individual sites is thought to be minimal, as the data are collected at regular
intervals by electronic instruments that do not depend on human interpretation.

11.	Sources of Variability

Water temperatures can vary substantially from year to year, due in part to the variable nature of
annual weather patterns. Air temperature, rainfall, and snowpack can all vary naturally from one year to
the next, and all of these variables can influence stream temperature. Additionally, climate in the Pacific
Northwest can reflect periodicity in ocean conditions (known as the Pacific Decadal Oscillation, or PDO)
that may have strong effects on temporal variability in stream temperatures (Mantua and Hare, 2002).
Because the length of the dataset includes opposing PDO cycles (Luce and Holden, 2009), however, the
potential magnitude of this source of variability is minimized.

The Snake River at Anatone is downstream of a reservoir that could alter thermal trends, but the
likelihood of meaningful changes to the river's thermal regime due to the reservoir's presence is
minimal. The nearest reservoir is located more than 160 kilometers (km) upstream of the measurement
site. Over this distance, river temperatures should equilibrate to local climatic conditions, as spatial lags
in the correlation of stream temperatures are typically much shorter than 160 km (Isaak et al., 2010).
Additionally, two large, unregulated tributaries enter the Snake River downstream of the reservoir and
upstream of the measurement site. These tributaries, the Salmon and Grande Ronde rivers, double the
Snake River's volume and further dilute any remaining reservoir effects.

12.	Statistical/Trend Analysis

The long-term rate of change in average August water temperatures was computed using the Sen slope
method, which finds the median of all possible pair-wise slopes in a temporal data set (Theil, 1950; Sen,
1968; Helsel and Hirsch, 2002). Autocorrelation within the data was addressed using block bootstrap
simulations, with block length set to n/3, where n is the length of the time series (Kunsch, 1989). These
calculations resulted in a slope of +0.0139°C (+0.0250°F) per year, with a p-value of 0.038, which makes

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the trend significant to a 95-percent level (p < 0.05). The annual rate was multiplied by 55 years to
derive an estimate of total change over the period from 1960 to 2015.

References

Helsel, D.R., and R.M. Hirsch. 2002. Statistical methods in water resources. Techniques of water
resources investigations, Book 4. Chapter A3. U.S. Geological Survey, http://pubs.usgs.gov/twri/twri4a3.

Isaak, D.J., C.H. Luce, B.E. Rieman, D.E. Nagel, E.E. Peterson, D.L. Horan, S. Parkes, and G.L. Chandler.
2010. Effects of climate change and wildfire on stream temperatures and salmonid thermal habitat in a
mountain river network. Ecological Applications 20(5):1350-1371.

Isaak, D.J., S. Wollrab, D. Horan, and G. Chandler. 2012. Climate change effects on stream and river
temperatures across the Northwest U.S. from 1980-2009 and implications for salmonid fishes. Climatic
Change 113:499-524.

Kunsch, H.R. 1989. The jackknife and the bootstrap for general stationary observations. Annals of
Statistics 17(3):1217-1241.

Luce, C.H., and Z.A. Holden. 2009. Declining annual streamflow distributions in the Pacific Northwest
United States, 1948-2006. Geophysical Research Letters 36(16):L16401.

Mantua, N.J., and S.R. Hare. 2002. The Pacific Decadal Oscillation. Journal of Oceanography 58(l):35-44.

Sen, P.K., 1968. Estimates of regression coefficient based on Kendall's tau. Journal of the American
Statistical Association 63(324):1379-1389.

Theil, H. 1950. A rank invariant method of linear and polynomial regression analysis, I, II, III. Proceedings
of the Koninklijke Nederlandse Akademie Wetenschappen, Series A - Mathematical Sciences 53:386-
395, 521-525, 1397-1412.

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