2)

Table 6. Day length (hrs) for various time frames and latitudes. Adapted from Forsythe (1954).

Approximate

dec ination

of the sun: -23 27" “15 -10 $ t) +5 +10 +15 +200 423.27"

Approximate

date: Feb Feb 23.0 Mar 8 = Mar 22.0 Apr 3. Apr 16 May 1 May 200 dun 21

Dec 22 Mov3 Oct 19 Oct 6 «= Sep 23 «Sep 10 Aug 28 «= Aug 13 Jul 24

Latitude hours hours. -=—shours. hours. «= hours = shou’ «= hours © hours’ © hours. hours
0 12.12 12.12 12.12 BAZ 12.12 12.10 12.10 12.12
10 11.53 1.75 11.88 12.00 12.12 12:35 12.48 12.72
20 10°92 11:38 163818712. 12-62 12.88 13.35
30 10:20 10.97 11.350 11.73 12.13 12:90 13.32 14.08
40 9.33 10.43 11.02 11.58 12.15 13.27 13.88 15.02
50 8.07 9.72 10.58 = 11.38 12.20 13.78 14.65 16.38
55 17 9.25 10.27 12:23 12.20 14.13 15.18 17.38
60 5.87 8.60 9.88 «= 11.05 12.28 14.58 15.90 18.88
65 3.57 7.70 9.35 10.83 12.28 15.23 16.97 22.05
70 6.23 8.53 10.48 12.35 16.22 18.73
80 3.17 8.77 (12.63



Reckhow, K.H., and S.C. Chapra. 1983, Engineering approaches for lake
Management. Volume 1: data analysis and empirical modeling. Butterworth
Publishers, Boston, MA. 340 pp.

Reckhow, K.H., J.T. Clements, and R. Dodd. 1986. Statistical goodness-of-fit
measures for waste load allocation models. Limnosystems, 2917 Wade Rd, Durham,
NC 27705. 75 pp. [Draft Final Report.]

Reifsnyder, W.E., and H.W. Lull. 1965. Radiant energy in relation to forests.
USDA For. Serv. Tech. Bull. No. 1344. 111 pp.

Richardson, C.W. 1981. Stochastic simulation of daily precipitation,
temperature, and solar radiation. Water Resour. Res. 17(1):182-190,

Ringuest, J.L. 1986. A chi-square statistic for validating simulation generated
responses. Comput. Oper. Res. 13(4):379-385.

Rishel, G.B., J.A. Lynch, and E.S. Corbett. 1982. Seasonal stream temperature
changes following forest harvesting. J. Environ. Quad. 11(1):112-116.

Rochelle, J.M., and C.C Coutant. 1973, Temperature sensitive ultrasonic fish
tag: Q-5099. Oak Ridge National Lab Report ORNL-TM-4438. Available from
NTIS. 29 pp.

Rocky Mountain News. July 30, 1988. Something fishy in drought aid bill.

Ruthven, O.M. 1971. The dispersion of a decaying effluent discharged
continuously into a uniformly flowing stream. Water Res. 5:343-352.

Sanders, W.M. 1985. Field validation. Pages 601-618 in G.M. Rand and S.R.
Petrocelli, eds. Fundamentals of aquatic toxicology. Hemisphere Publishing,
Washington, DC. 666 pp.

Sette, 0.E. 1940. Probable temperatures of surface water to be stored above
Shasta Dam. Pages 165-174 in H.A. Hanson, O.R. Smith, and P.R. Needham, eds.
An investigation of fish-salvage problems in relation to Shasta Dam. USDI
Bureau of Fisheries, Special Scientific Report No. 10, Appendix B.

Shipman, R.D. 1954. Simple aid to instantaneous measurement of Vight intensity.
J. For. 52(8):605-606.

Shuter, B.J., D.A. Schlesinger, and A.P. Zimmerman. 1983. Empirical predictors
of annual surface water temperature cycles in North American lakes. Can, J.
Fish Aquat. Sci. 40:1838-1845.

Smagorinsky, J., Chairman, Climate Research Board. 1982. Carbon dioxide: a
second assessment. National Academy Press, Washington, DC.

Smith, K. 1975. Environn.zntal influences on the temperature of a smal] upland
stream. Oi 9s 26(2):228-236.

116



The simplest, but most error prone, method of measuring travel time is
the floating object method (Hamilton and Bergersen 1984). Its use is limited
to straight and uniform stream segments, with minimum surface waves, on windless
days. Floating object is a misnomer, for appropriate objects actually are
immersed between one fourth of the depth and the bottom, and do not float on the
surface. (In a pinch, a group of oranges may be used.) Establish three to five
transects, far enough apart to actually measure an elapsed time. Intermediate
transects provide double-checks on the estimates obtained. Several trials will
be necessary, with the floats positioned at several locations across the initial
transect, with the final answer being the mean time. Finally, multiply the mean
velocity of a reasonably smooth stream by 0.8 to obtain the average, midcolumn
velocity.

If a concurrent or previous PHABSIM study is available for the study
stream, the detailed output from one of the hydraulic models may be examined
and a mean travel time calculated from the total cross-section area divided by
the discharge at each transect. According to one source (Hubbard et al. 1981),
this method will tend to underestimate the travel time unless a weighted mean
is computed by giving proportional weight to the length of stream represented
by each transect (i.e., habitat mapping approach).

The next most accurate methods are Probably routing studies or colored
dye studies. In a routing study conducted below a controlled-release impound-
ment, an abrupt increase in flow followed by an abrupt decrease to the previous
base flow is made. Staff gages, or stage recorders, located at downstream
transects record the sequential Passage of the release wave. Travel time is
computed from the time of peak stage to peak stage between transects. Different
base flows must be used to develop a travel time vs. flow function (Waddle 1987).
Colored dye studies involve the instantaneous pouring of fluorescein or potassium
permanganate into the stream far enough above the upstream transect to permit
complete lateral dispersion. Dye behaves much the same as water molecules and
moves on the average at the same rate as water. Travel time is computed by
estimating the time when the "center of the color mass" passes the downstream
stations. Considerable judgment is usually required to best gage the time at
which the "best color" is reached. Experimentation is often necessary to achieve
concentrations strong enough to be easily measured, but weak enough to not cause
downstream complaints. See Hamilton and Bergersen (1984) for more details.

The cadillac of methods is the true fluorometric dye study (Hubbard et
al. 1981). The details and equipment are complicated and relatively expensive.
A fluorometer is used to measure the light emitted from a fluorescent dye. The
dye is selected for properties such as detectability, toxicity, solubility, and
cost. The currently recommended dye is rhodamine WT, specifically formulated for
water tracing. Concentration-time plots {Figure 7) may be constructed in a
detailed dispersion study, or more simple peak-to-peak concentration times may
be adequate in a less costly study. Rigorous standards must be met for injecting
these dyes into water bodies that have water withdrawal Points leading to human
consumption. Significant effort is involved in successfully implementing a dye
study of this sort. You should seek assistance from a hydrologist experienced
in this type of study.

18



As the Nation's principal conservation agency. the Department of
the Interior has responsibility for most of our nationally owned
public lands and natural resources. This includes fostering the
wisest use of our land and water resources, protecting our fish and
wildlife, preserving the environmental and cultural values of our
national parks and historical places, and providing for the enjoy-
ment of life through outdoor recreation. The Department assesses
our energy and mineral resources and works to assure that their
development is in the best interests of all our people. The Depart-
ment also has a major responsibility for American Indian reservation
communities and for people who live in island territories under U.S.
administration.

US. DEPARTMENT OF THE INTERIOR
FISH AND WILOLIFE SERVICE
WASHINGTON, D.C. 20240

‘OFFICIAL BUSINESS:
PENALTY FOR PRIVATE USE, $300

POSTAGE AN FEES PRO
UA, DEPARTMENT OF THE INTERIOR
wes

Foor



ERRATA FOR INFORMATION PAPER 13

Bartholow, J.M. 1989. Stream temperature investigations: field and analytic methods.
Instream Flow Information Paper No. 13. U.S. Fish Wildl. Serv. Biol. Rep. 89(17).
139 pp.

The following error was noted after printing. We sincerely apologize for any inconvenience this
may have caused you.

Page 116, last reference Delete reference to Smith, K. 1975. See instead
Smith, K., and M.E. Lavis (1975) as referenced on page
117.



Hewlett, J.D., and J.C. Fortson. 1982. Stream temperature under an inadequate
buffer strip in the Southeast Piedmont. Water Resour. Bull. 18(6) :983-988.

Hirsch, R.M., J.R. Slack, and R.A. Smith, 1982. Techniques of trend analysis
for monthly water quality data. Water Resour. Res. 18(1):107-121.

Hirtzel, C.S., J.E. Quon, and R.B. Corotis. 1982. Mean of autocorrelated air
quality measurements. J. Environ. Eng. 17138, Am. Assoc, Civ. Eng.
108(EE3) : 488-501.

Hogan, C.M., L.C. Patmore, and H. Seidman. 1973. Statistical prediction of
equilibrium temperature from standard meteorological data bases. Environ-
mental protection Technology Series. EPA-660/2-73-003. USEPA, Washington,
DC. 271 pp.

Holling, C.S. 1978. Adaptive environmental assessment and management. John
Wiley and Sons, NY. 377 pp.

Hubbard, E.F., F.A. Kilpatrick, L.A. Martens, and J.F. Wilson, Jr. 1981.
Measurement of time of travel and dispersion in streams by dye tracing. USGS
Techniques of Water-Resources Investigation, TWI 3-A9. 44 pp.

Hughes, D. [1976.] Prediction method for calculating potential water
temperature increases and influence zones. Umpqua National Forest, P.0. Box
1008, Roseburg, OR 97470. 16 pp. Unpubl. MS.

Hydroscience, Inc. 1971. Simplified mathematical modeling of water quality.
Ss. Environmental Protection Agency, Washington, DC. 127 pp. + appendices
and charts.

Jackson, L.W.R., and R.S. Harper. 1955. Relation of light intensity to basal
area of shortleaf pine stands in Georgia. Ecology 36:158-159.

Jackson, R.D., and A.P. Jackman. 1980. Predicting the natural water temperature
profile throughout a river basin. J. Environ. Syst. 9(4):361-381.

Jobson, H.E. 1977, Bed conduction computation for thermal models. Am. Soc.
Civ. Eng. Proc. 103[(H410 No. 13251]:1213-1217.

Jobson, H.E. 1981. Temperature and solute-transport simulation in streamflow
using a Lagrangian reference frame. U.S. Geological Survey Water Resources
Division. Water Resources Investigations 81-2. 175 pp.

Jobson, H.E. 1987. Estimation of dispersion and first-order rate coefficients
by numerical routing. Water Resour. Res. 23(1):169-180.

Jobson, H.J., and T.N. Keefer. 1979. Modeling highly transient flow, mass,

and heat transport in the Chattahoochee River near Atlanta, Georgia. U.S.
Geological Survey Professional Paper 1136. 41 pp.

112



Table 8. One form in which you may find water temperature data from the

STORET data base.

STORET RETRIEVAL DATE 86/11/25

TYPA/AMBNT/STREAM

DATE
FROM
To

82/07/06 1030
82/07/20 1115
78/07/11
78/07/24
79/07/16
84/07/19 1240
83/07/28 1110
80/07/15 1120
79/07/02
84/07/02 1255
80/07/03 1250
97/07/01

MONTH NUMBER
MAXIMUM
MINIMUM
MEAN

97/07/31

78/08/21

81/08/03 0930

81/08/17 0900

81/08/31 0905

80/08/12 0950

79/08/13

79/08/27

80/08/27 0945

mzyoas te 1235

708/08

708/30 1325

82/08/03 1255

83/08/11 1050

83/08/25 1150

84/08/29 1305

84/08/16 1235

86/08/1145

97/08/01

WONTH NUMBER
MAXTHUM
MIMTHUM
WEAN

97/08/31

18.5000
20.0000

11.6471

06701500

39 12 33.0 105 16 02.02

SOUTH PLATTE RIVER BELOW CHEESMAN LAKE, CO
08059 COLORADO JEFFERSON

112yR0 790519 10190002
0000 FEET DEPTH

00011
WATER
TEMP
AHN

67.0999

61



digitizing or other software has built-in methods for correcting the constant
or drift errors mentioned above.

TEMPERATURE REGISTERED BY INSTRUMENT
IN DEGREES CELSIUS

° 3 10 ry 20
TRUE TEMPERATURE, IN DEGREES CELSIUS

Figure 28. The different types of error that can arise in the measurement of
temperature. If well specified, these types of errors can be corrected
(calibrated), Reproduced from Stevens et al. (1975).

The methods of summarizing and presenting water temperature data can vary
from simple to complex and depend largely on the purposes at hand. In general,
graphical techniques are valuable in showing the degree of variation over time
or space (Figure 29).

If daily average temperatures are reported, the maximum and minimum
temperatures should also be recorded if the diurnal variation is greater than
about 2 °C (3.6 °F} (Stevens et al. 1975). Dates and times should always
accompany periodic or nonrecording measurements. Remarks concerning accuracy
of measurements or other special considerations also should be included for a
comprehensive tabulation of temperature measurements.

72



Table 1. Relative sensitivity of maximum and mean water temperatures to various parameters for a
generalized stream. Sensitivity as depicted here is dimensionless. Please see text for an explana-
tion.

When these associated parameters are combined as shown:

To changes in Stream Inflow Width/ Thermal Air Relative Wind Solar Percent

Water temperature is: these variables: flow temp. depth gradient temp. humidity speed radiation shade
Very Sensitive Air Temperature low low high high high
Hoderetely Sensitive Percent Shade low high low high

Relative Humidity low low high high low

Stream Flow Tow Tow high high Tow
Moderately Sensitive Inf low Temperature high high low Tow Tow high
Moderately Sensitive Stream Width low Tow high high Tow
Relatively Insensitive Soler Radiation Tow high low low Tow

Travel Time/Roughness Tow Tow high low
felat wely Insensitive Wind Speed high Tow high high high
Relatively Insensitive Ground Temperature low high high Yow
Insensitive Percent Possible Sun low high high low Tow

Insensitive Thermal Gradient low high high high



APPENDIX B.

Ben Meadows Company
3589 Broad Street
P.0. Box 80589

Atlanta (Chamblee), GA 30366

404-455-0907

Climatronics

140 Wilbur Place
P.O. Box 480
Bohemia, NY 11716
516-567-7300

Cole-Parmer

7425 North Oak Park Ave.
Chicago, IL 60648
312-647-7600

Davis Instruments
513 E. 36th Street
Baltimore, MD 21218
301-243-4301

Hydrolab Corp.
P.O. Box 50116
Austin, TX 78763
512-255-8841

Ikelite Underwater Systems
50 West 33rd Street

P.O. Box 88100
Indianapolis, IN 46208
317-923-4523

Jim-Gem Forestry Suppliers
205 West Rankin St.

P.0. Box 8397

Jackson, MS 39204
601-354-3565

Met One, Inc.

481 California Ave.
Grants Pass, OR 97526
503-479-1248

121

EQUIPMENT VENDORS

General forestry supply

Meteorological supplies

ASTM thermometers

Thermographs

Thermographs

Waterproof thermograph
housings

General forestry supplies

Meteorological supplies



APPENDIX D. ALTERNATIVE DATA FORMATS FROM THE HYDRODATA DATA BASE

Table D-1. Textual output file from Hydrodata (tm U.S. West Corporation) for
mean daily water temperature (°C). Similar tables are often available for
daily maximum, daily minimum, and/or random interval water temperature. There
may also be data on humidity (%), solar radiation (c/cm’), air temperature (°C
or *F), soil temperature (°C), wind speed (mph), as well as a variety of water
quality data. “Remarks” data files may also be available.

08037000 ANGELINA RIVER NR LUFKIN, TEX(DISC)
LOCATION.--Lat 31:27:26, Long 094:43:34, Hydrologic Unit 12020005.
DRAINAGE AREA.-- 1600.00 miz { 4144.00 km2).
GAGE.-- Altitude of gage is 0164.72 ft( 50 m).

TEMPERATURE, WATER {DEG.C], WATER YEAR OCT 1956 TO SEP 1957
STATISTIC CODE 30830

DAY Oct Nov Dec «= dan Feb) Mar Apr Ss May = dun dul, = Aug, Sep
i 22 187.2 10 u 1B 16 19 24 a 23 28
2 22 16 002«742—~*—«O A i 1B 18 22 24 2 3B 28
3 22 i683 88 12 2B 21 22 23 28 28 28
4 23 16 i 12 1 4 18 22 22 28 28 27
5 3 7 14 u 15 15 18 20 23 28 28 27
6 23 v 15 u 16 1B 16 19 23 29 28 27
7 B 18 v H 15 Hl 18 19 24 28 ra 26
8 19 16 16 i 169.4 18 2 26 28 27 24
9 13 12 12 15 i 10 16 2 26 32 8 24
10 18 13 u 1B 7 13 160 = a7 28 27 24
u 18 12 Mo v “ 18 23 2 28 27 23
a2 19 13 12 ist Vv 4 19 24 28 28 28 24

1B 19 B 2B 12 15 “ B 24 2? 30 28 24
4 19 v 2 1B 4 16 18 24 7 30 28 24
8 20 18 4 10 vv uM 16 24 2 29 28 26
16 20 B Mo 8.9 16 “4 v 24 ar 2a 28 2
v7 19 12 2 67 4 16 0 ws 25 27 29 23 24
18 i9 2 14 5 M4 7 2000 a7 3000 oe 25
19 19 12 13 8 13 1s 20 23 a7 23 28 5
20 21 16 137.8 42 16 2h 24 26 23 27 26
2 20 4 12 uM ul 16 ao 22 2 a7 2
22 18 il 13 13 il 15 ai 26 26 28 27 28
23 20 ri 2 83 1B 18 23 26 26 28 28 23
24 18 10 11 8.9 i 15 a 25 24 a 27 26
25 18 100 9.4 8.9 ie) 13 a 26 24 2 2 ral

(Continued)

127


For our purposes, a “major" tributary should not be defined by the standard
10% of the mainstem flow rule [note that Nobel and Jackman (1980) believe that
a 5% discharge rule should apply}, but rather by a temperature change definition.
For example, a tributary that changes the temperature of the mainstem by more
than 5% should be included. The mixing equation may be used to estimate
temperature change. One must think ahead, however, for a tributary may not
presently be changing mainstem temperatures, but it may do so under altered or
postproject conditions.

Beyond these general rules, one can only say the more temperature locations
the better; more provides insurance against inevitable downtime and lost data.
A greater instrument density will also help you isolate troublesome reaches for
which the models seem to perform poorly. However, more monitoring stations also
add to the cost. I can think of no case in which the density of recorders needs
to be greater than every 5 km along a mainstem, all other things being equal;
this should be adequate for small (less than 50 cfs) streams. For larger rivers,
10 km may be adequate.

The exact installation site along a river is usually out of your control;
physical accessibility (e.g., private land) or the existing location of
streamflow gages may essentially dictate the site. When a station is estab-
lished, care must be taken to ensure the site is suitable for measuring
temperature on a more ‘detailed level. For example, water temperature of
reservoir outflow may be measured within the scroll] case of one or more turbines.
These temperatures, however, may be significantly higher than the average for
the total outflow, due to stratification in the forebay, heat generated by
turbulence, and heat conducted through the turbine shaft and dam. Therefore,
it may be best to measure at a distance far enough downstream to ensure complete
mixing. Verify this by taking a temperature profile, both vertically and
horizontally, preferably at low flow. If a particular site requires a monitoring
station, and there is documented horizontal or vertical variation of more than
2 °C (3.6 °F) more than 5% of the time, two stations should be installed. In
cases of highly variable temperatures (Figure 27), it may be necessary to
aay a discharge weighted mean temperature as discussed by Stevens et al.
(1975).

The sensor itself is usually mounted in a perforated pipe directly in the
streamflow, but protected to minimize physical damage. The sensor should not
rest in direct contact with the streambed, nor should it be in direct sunlight,
if possible. Obviously, erroneous measurements will result if the sensor gets
exposed to air at low flows or gets covered with silt or other debris. Location
too near any slough or stilling well can cause erroneous measurements if the
water level fluctuates very much (Stevens et al. 1975).

Calibration

I recommend that any temperature measurements taken have some tie to a
recognized standard, such as that provided by the American Society for Testing
and Materials (ASTM). The use of such a standard will help assure uniformly
high quality data in the courtroom and establish a basis for better comparisons
in the literature (which is woefully inadequate in presenting information
regarding exactly what kinds of temperature measurements were taken).

67



|

winter warming effects of clearcutting alone with clearcutting/s]ashburning,
which decreased winter water temperatures. No conclusions were reached as to
the reasons for the differences. Rishel et al. (1982) found the more typical
decreases of minimum, average, and maximum stream temperatures in winter after
both commercial clearcut and clearcut-herbicide treatments. Although average
diurnal changes in temperatures were large for part of the year (Figure 9), the
temperature changes generally were not statistically significant in December,

January, or February. Lynch et al. (1984) and Swift et al. (1971) reported
similar results.

25>
~
{o) Clearcut-Herbicide
& 20-
Wu
5
= 15 7
<
i
10 4
& Control
Ww
be 64

T T T T T T T T 1
2000 0000 0400 0800 1200 1600 2000 2400 0400

TIME

Figure 9. Effect of one timber management alternative on hourly water
temperatures. Adapted from Rishel et al. (1982).

Finally, in a broad analysis of Oregon streams, Moore (1967) concluded
that stream orientation alone was sufficient to Produce definite categories of
temperature profiles. He found that east-west oriented streams could have
temperatures 2 to 4.5 °C (4-8 *F) warmer than north-south oriented streams.
Approaches similar to this are being used to “fine-tune” timber harvest
restrictions in varied topography.

In a different vein, some investigators have looked at the "direct"
relationship between shade and the biological community, skipping the inter-
mediate temperature step altogether. Platts et al. (1983, p. 58) mentioned

25



methodology that can handle “messy-data" simulation models. Use the root mean
squared error until those better methods are developed. Then, during calibra-
tion, simultaneously attempt to maximize the R? value and minimize the mean
error.

OTHER STREAM TEMPERATURE MODELS

Over the past several decades, many models have been assembled to describe
and predict stream temperatures. These models have taken several forms and have
specialized in different ways. In particular, there appear to be two main
categories of models: empirical and physical-process-oriented models. The
empirical models are themselves of two forms: regression and stochastic/
descriptive. The process-oriented models are harder to classify because they
usually specialize in certain types of processes, such as dynamic flow versus
steady flow, mean daily versus hourly, and specific temperature versus
equilibrium. The following sections will dea? with each of these major types,
but no true evaluation will be made.

hysical-Pri M

Steady-flow models. All physical-process models are a variation on the
energy budget theme. That is, they attempt to explain the changes in water
temperature by calculating the gains and losses in thermal energy from
individually described phenomena such as radiation, convection, conduction, and
evaporation. Early models were necessarily crude due to lack of appropriate
instrumentation; however, application to large rivers was reasonably successful.
In effect, it was hard to be wrong in large rivers due to the large heat storage
capacity of that much water.

Brown (1969) illustrates this point by comparing a stream with a summer
flow of 1 cfs having a diurnal fluctuation of 11.1 °C (20 °F), with a 5,000 cfs
river having a diurnal fluctuation of only 1.1 °C (2 °F). Brown went on to
develop more detail. in his energy budget models to more accurately describe
temperature phenomena in small streams, particularly with respect to the effects
of riparian shading. His work has largely been the basis for further refinement
in the art of temperature modeling (Hughes 1976; Currier and Hughes 1980). Most
of these models, however, should be limited to reaches less than 2,000 feet in
length (Currier and Hughes 1980).

A very widely used and accepted model for stream water quality modeling
is QUAL-IIE. Though intended for up to 15 water quality constituents, QUAL-IIE
could be used exclusively for temperature modeling. The model can handle slowly
varying flow conditions and can simulate diurnal temperature, algal production,
and dissolved oxygen. This model is reputedly inexpensive and easy to apply,
being implemented on microcomputers. The model, user’s manual, and technical
assistance are available from the Environmental Protection Agency (EPA) Center
for Water Quality Modeling in Athens, Georgia.

96



For example, if there is vegetation along 25% of the stream and the average
density of that coverage is 50%, the total vegetative density is .25 times .50,
which equals .125, or 12.5%. The decimal value should always be between 0 and
1. Values for both east and west sides may be independently measured. Though
the continuity factor may be adequately estimated by skilled photo-interpreters,
the shade quality cannot. Both may be estimated by measurements taken in a
sampling along the stream.

To give examples of shade quality, an open pine stand provides about 65%
shade; a closed pine stand provides about 90% shade; a tight spruce/fir stand
provides about 85% shade; areas of extensive, dense emergent vegetation should
be considered 90% efficient for the surface area covered. Some other estimates
are available from Reifsnyder and Lull (1965).

One common method of measuring shade density is to use a concave spherical
densitometer (Platts et al. 1987). This method has been shown to be accurate
in the measurement of forest overstory density (Lemmon 1956), but is not
recommended for our purposes for two reasons. First, it is extremely difficult
to measure the quality of the shade because one can only classify shaded or
unshaded, not the degree of shading. Second, this method does not account for
filtering along the path of the sun.

To correct this "along the path" problem, Brazier and Brown (1973)
pioneered the use of what they called Angular Canopy Density when measuring the
width of clearcut buffer strips. Basically, they showed that what was important
was not the absolute width of the buffer strip, but the actual shade resulting
from that buffer strip. They advocated consideration of the stream’s orientation
with respect to the north-south axis, and measuring the canopy density along the
path of the incoming solar radiation, rather than vertically through the canopy.
They used this technique to visually survey shading. Their reported values range
from 18% to 80% in different width buffers, the vegetation being red alder and
conifers. Though part of their logic is not clear to me when it comes to the
relationship between measured angular canopy density and actual heat blocked,
the results showing the relationship between buffer strip width and shading
density (Figure 14) illustrate a useful concept that has been used to select
"leave trees" in timber management areas (Lafferty 1987).

Brazier and Brown (1973) are quick to point out that the visual measure
of angular canopy density does not adequately account for the true shading due
to the different qualities of shade. The thicker Canopies of the conifers are
more efficient at screening solar radiation than the thin canopies of hardwoods,
even though the measured canopy density may be the same. Other authors have
pointed out that the Brazier and Brown technique needs to be modified for wide,
north-south oriented streams (Pope and Lafferty 1987).

Another method that has proven to give accurate results under tight
canopies, such as conifers or fully leafed deciduous vegetation, is reported in
Platts et al. (1987) and adapted for this context. Measurements are made
visually on randomly selected transects using a white disk 3 inches in diameter
at approximately 100 points throughout the stream segment. ach reading is
Shassified as being one of three states: direct sunlight, filtered sunlight, or
shade.

32



is

Table 7b.

Alternative forms in which

or databases:

you may find water temperature data in USGS publications

daily maximum, mean, and minimum water temperature values.

STATION NUMBER

LATITUDE 330458

wnons nnuoo

Dawns

eecbh Bune

RRBSS BASSR RENEE
ERDAS RSENS RSSSS
Boome O5505 Sano

ouaon

UNITED STATES DEPARTMENT OF INTERIOR - GEOLOGICAL SURVEY

02197370
LONGITUDE

EAN

euscen euusn couse sounoo

nnn

0813554
TEMPERATURE (DEG. C) OF WATER

SUNCH Busou Uowou nonoos

woouo

AREA,

MEAN

(Continued)

SAVANNAH R. BL STEEL CR NR MILLETVILLE, $.C.
DRAINGE DATUM

STREAM

BSRAR ARIEL
Banoo Bonne

muon

PROCESS DATE 1S 09-06-77

SOURCE AGENCY USGS

. WATER YEAR OCTOBER 1974 TO SEPTEMBER 1975

maxX,

Sept
0

MIN

TEMBER
25.0
25.5
25.5
25.5
25.0

STATE 45 COUNTY 005

MEAN



Smee y

YE Headquarters, Research and Development,
Waatington, BC

© Locationa ot Regional Oftces

REGION 1

Regional Director

US. Fish and Wildlife Service

Lloyd Five Hundred Building, Suite 1692
500 N.E. Multnomah Street

Portland, Oregon 97232

REGION 4
Director

US. Fish and Wildlife Service

Richard B. Russell Building

73 Spring Street, $.W.

Aulanta, Georgia 30803

REGION 2
Regional Director
US. Fish and Wildlife Service
P.O. Box 1306

Albuquerque, New Mexico 87108

REGION 5

Regional Director

US. Fish and Wildlife Service

One Gateway Center

Newton Corner, Massachusetts 02158

REGION 7

Regional Director

US. Fish and Wildlife Service
1011 E, Tudor Road
Anchorage, Alaska 99503

REGION 3
Regional Director

US. Fish and Wildlife Service
Federal Building, Fort Snelling
‘Twin Cities, Minnesota 55111

REGION 6

Regional Director

USS. Fish and Wildlife Service
P.O. Box 25486

Denver Federal Center
Denver, Colorado 80225



82

IDEALIZED REPRESENTATION OF HOUR ANGLES

Wha SOLAR NOON

h, - LEVEL PLAIN SUNSET HOUR ANGLE

hg, - LOCAL SUNRISE HOUR ANGLE 1 HOUR - 15° OF HOUR ANGLE
heg- LOCAL SUNSET HOUR ANGLE

Figure 10. Schematic showing the interception of solar radiation by topography and riparian
vegetation. Adapted from Theurer et al. (1984).



(Concluded)

Table 7c.

TEMPERATURE (“C) OF WATER, WATER YEAR OCTOBER 1970 TO SEPTEMBER 1971

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ocr

pay

meee nnnen nano

OTOON YHSNT Yen’!

monnon

60



Another alternative to the SNTEMP category of temperature models is
TEMP-84,° developed and applied by the Forest Service (Ellis et al. 1980; Beschta
and Weatherred 1984). Though still a physical process model, it differs from
the Theurer-type models in several respects. First, it is primarily intended
to simulate potential maximum temperatures resulting from shade removal in small]
mountain streams at low flow. Because this is its single focus, minimal
attention is given to many meteorological parameters, such as cloud cover and
wind speed. Instead, the model contains more resolution in two areas: (1) the
shade components include brush and logs in addition to topographic and riparian
vegetation, and (2) the heat flux with the streambed is simulated through time
in detail. This is important because this model simulates essentially on a 15-
minute time-step; solar radiation is assumed to be absorbed by the substrate if
the depth is less than 20 cm and the particle size is greater than 25 cm.
Radiation is assumed to be absorbed during the day and released at night. The
mode] has not been validated to date. The model, user’s manual, and technical
assistance are available from the Forest Service Watershed Systems Development
Group in Fort Collins, Colorado.

A model called STEADY is an alternative stream temperature and dissolved
oxygen model. STEADY, as you might imagine, is for steady flow situations. It
has strengths in handling complex network structures, e.g., branches and loops,
that the Theurer model will not handle. However, the input data must include
the equilibrium water temperatures, thus implying that at least some other
temperature model must be employed as wel}.

The historical development of physical-process-model development and
application may be found in Edinger et al. (1974) and Carroll] et al. (1983).
Other theoretical mathematical models have been outlined in the literature
(Raphael 1962; Nobel 1979), but in general offer no additional insight into
applied temperature modeling. The major differences seem to be which heat flux
terms are ignored, either explicitly or implicitly, and how coefficients were
derived or estimated. One noteworthy exception is an interesting paper that
develops a graphical technique to estimate the average equilibrium temperatures
about which the instantaneous temperature oscillates (Krajewski et al. 1982).
A worthwhile research endeavor would be to compare alternative temperature model
formulations and empirical coefficients for consistency among modets and to point
out errors of commission or omission. :

Dynamic flow models. Though stream flow is seldom truly steady,
approximations to steady flow greatly simplify the description of all physical
processes involved in temperature and other water quality modeling. As long as
the stream or river has small, monotonic, changes in flow, we can continue to
use steady flow models. If, however, the flow changes are large and erratic,
we must employ models that can handle dynamic conditions. Jobson and Keefer
(1979) present an example of modeling the thermal regime of the Chattahoochee
River near Atlanta, Georgia, in highly transient flow situations. Modeling the
dynamic flow environment was necessary because of hydropulsation (due to power
generation) from 15.4 cms to 215 cms within 10 to 20 minutes.

There is now, I believe, a version for microcomputers called TEMP-86.

97



Viskanta, R., and J.S. Toor. 1972. Radiant energy transfer in waters. Water
Resour. Res. 8(3):595-608.

Waddle, T. 1987. Water temperature data analysis and simulation for the Salmon
River, Oswego County, New York, 1986. Unpublished report for Empire State
Electrical Energy Research Corp., 1271 Ave. of the Americas, New York, NY

‘0.

Ward, J.C. 1963. Annual variation of stream water temperature. J. San. Eng.
Div., Proc. Am. Soc. Civ. Eng. 89(SA6):1-16.

Webb, B.W., and D.E. Walling. 1988. Modification of temperature behavior
ghrough regulation of a British river system. Regul. Rivers: Res. Manage.

Weiss, L.L. 1964. Sequences of wet or dry days described by a Markov chain
probability model. Monthly Weather Rev. 92(4):169-176,

Wellner, C
canopies:

3 pp.

Williams, C.B. 1952. Sequences of wet and dry days considered in relation to
the logarithmic series. Q. J. Royal Meteorol. Soc. 78(335):91-96.

Wunderlich, W.0., and R.A. Elder. 1967. The mechanics of stratified flow in
reservoirs. Pages 56-68 in Reservoir fishery resources symposium. Southern
Div., Am. Fish. Soc., Washington, DC. 569 pp.

- 1979. Estimating light intensity beneath coniferous forest
simple field method. USDA Forest Service Research Note INT-250.

Wunderlich, W.0., and M.C. Shiao. 1984. Long-range reservoir outflow
temperature planning. J. Water Resour. Plan. Manage. 110:285-295.

Zoltai, S.C. 1988. Ecoclimatic provinces of Canada and man-induced climatic
change. Canada Committee on Ecological Land Classification Newsletter 17:12-
15.

119



Figure 16. Field-installed meteorological station. These can be configured
to collect almost any set of data imaginable, at a cost. Courtesy
Climatronics Corporation.

38


Pool Bottom Seap Lateral Seep Lateral Seop
h Deflector

Tributary Mouth Tributary Moutn
Flow Through the Bed with Detlector

Figure 30. Common areas in which to expect temperature-created microhabitat.
Reproduced from Bilby (1984).

In similar work, Ozaki (1988) surveyed 21 km of Redwood Creek, northern
California, for cool pools. These pools, defined as areas that maintain water
temperatures more than 3 ‘C (5.4 °F) lower than adjacent water, represented less
than 9% of the total pool population. All of these pools were at least partially
segregated from the low-flow channel by gravel bars and were maintained at
depressed temperatures by either hillslope groundwater, tributary inflow, or
intergravel flow, with the latter two being most important. The temperatures
recorded were independent of the pool’s depth or volume, and thus felt to be
dependent on inflow quantity and temperature. Pool width-to-depth ratio seemed
to be related to the pool-mainstem temperature differential: the longer and
narrower the pool, the cooler the pool. Most pools exhibited some form of
stratification in the sense that the deeper parts were colder. However, most
pools were not stratified in the sense that they exhibited a distinct thermo-
cline.

Bilby’s and Ozaki’s findings may be contrasted with those of Nee? (1951)
who found ample evidence of true thermal stratification in the small, headwater
streams of Kentucky. Though some pools were found to be spring-fed, others
showed decided stratification (up to 6 ‘F, 3.3 °C) at low flows. The stratifica-
tion Neel found was not the stratification of stagnation, but rather that of
segregation of thermally distinct flowing water. That is, at different times,

80



INTRODUCTION

Water temperature has always been considered one of the most important
factors determining the geographic distribution of fish and other aquatic
organisms. Analysis of water temperature regimes has lately taken on added
importance, primarily for economic reasons. A recent newspaper article (Rocky
Mountain News 1988) discusses the construction of a $5.5 million reinforced-
plastic curtain in northern California’s Shasta Dam to transfer cool water from
the reservoir into the Sacramento River to prevent salmon heat death. Without
this curtain, the Bureau of Reclamation must release water without passing it
through turbines at a cost of $70,000 per day in lost power revenue.

Another study (Croley et al. 1981) has shown that the incremental cost of
reducing thermal discharges to achieve a 3 °F (1.7 *C) reduction along the
Missouri and Mississippi Rivers would be about $211 million per year. This
study measured power losses, but did not attempt to quantify fish and wildlife
gains. In contrast, a paper by Theurer et al. (1985) developed a valuation of
$0.6 million per year for restoring a salmon population in the temperature
degraded Tucannon River, a small Washington State river with a present worth of
$6.9 million. Clearly, these are large numbers, no matter which perspective one
chooses.

Recent climatic changes have caused the earth’s surface to be warmed by
about 0.5 °C (0.9 °F) between 1861 and 1984, along with a decrease in the diurnal
temperature range of about 1 °C (1.8 °F) (Zoltai 1988). A future perspective
on water temperatures is even more interesting. The American Association for
the Advancement of Science (1988) has concluded that scientists do not agree
whether global warming is a reality or not. But it feels that it is at least
prudent to look forward to what changes may be expected if the earth’s atmosphere
were to warm by 1.5-4.5 °C (3-8 °F) (Smagorinsky 1982). In addition to the
flooding of coastal cities and other alarming large-scale problems, we might
expect both a lesser amount of precipitation and higher air (and water)
temperatures, especially in the western United States. If this were to occur,
species now at the margins of their thermally defined geographic range may be
expected to change rather dramatically (Figure 1).

The purposes of this report are many. First, it is intended to serve as
a companion to another report in this series, Instream Flow Information Paper
No. 16--Instream Water Temperature Model (Theurer et al. 1984), in which the
theory and application of the Stream Network Temperature Model (and the SNTEMP
set of computer programs) are described. The information presented here will]
serve to broaden some of the concepts and methodologies outlined in that
publication, especially in the area of field techniques and laboratory analytical
methods. Second, information and advice on other data collection procedures will



SUMMARY

This document provides guidance to the user of the U.S. Fish and Wildlife
Service’s Stream Network Temperature Model (SNTEMP). Planning a temperature
study is discussed in terms of understanding the management objectives and
ensuring that the questions will be accurately answered with the modeling
approach being used.

A sensitivity analysis of SNTEMP is presented to illustrate which input
variables are most important in predicting stream temperatures. This information
helps prioritize data collection activities, highlights the need for quality
control, focuses on which parameters can be estimated rather than measured, and
offers a broader perspective on management options in terms of knowing where the
biggest temperature response will be felt.

All of the major input variables for stream geometry, meteorology, and
hydrology are discussed in detail. ach variable is defined, with guidance
given on how to measure it, what kind of equipment to use, where to obtain it
from another agency, and how to calculate it if the data are in a form other
than that required by SNTEMP. Examples are presented for the various forms in
which water temperature, discharge, and meteorological data are commonly found.
Ranges of values for certain input variables that are difficult to measure or
estimate are given. Particular attention is given to those variables not
commonly understood by field biologists likely to be involved in a stream
temperature study. Pertinent literature is cited for each variable, with
emphasis on how other people have treated particular problems and on results they
ave found.

Model calibration, verification, and validation steps are defined and
outlined, with measures of "goodness-of-fit" given for comparing simulated stream
temperatures with observed values. The question of how good is good enough is
explored, and attention is given to the kinds of simulation and data reduction
errors that one should be alert for.

Some special cases dealing with ice and reservoir temperature are
mentioned. Special attention is given to understanding micro-thermal habitats
that act as important thermal refugia under low flow conditions; their causes,
extent, and management implications are discussed.

Alternative public domain stream and reservoir temperature models are
contrasted with SNTEMP. A distinction is made between steady-flow and
dynamic-flow models and their respective capabilities. Regression models are
offered as an alternative approach for some situations, with appropriate
mathematical formulations suggested.

iii



National Climatic Data Center (see Appendix A)
U.S. Weather Service
U.S. Department of Agriculture
Experiment Stations
Farm Forecast Services
Forest Service Offices
Forest Service Fire Data Center (see Appendix A and
Furman and Brink (1975))
Private Weather or Data Services (see Appendix B)
Environmental Protection Agency
Universities
Utility Districts/Companies
Airports
Military Installations - particularly the Air Force
Coast Guard

Perhaps the best organized data will be from the National Climatic Data Center

This data will be available in photocopy or Magnetic tape form from them (NOAA
1985), but may be found in a more timely fashion at a local land grant university
library. You should look for publications called Local Climatological Data,
which come in two formats. The annual issue contains the monthly data summaries
and normals (means for the previous 30 years); the monthly issue contains the
daily summaries. Any other time period, e.g., weekly, must be assembled from
daily data. A Summary of Hourly Data may be available at some stations, which
may be useful in some situations. You also may be able to contract with a State
climatologist to provide the data on diskettes. In one study that I know of,

the climatologist provided daily values for air temperature, wind speed, dew
point, and solar radiation, 1978-1986, for about $300.

The data contained in the two most. common summaries is not entirely
parallel (Table 4).

Other references that may prove useful, especially for data summaries, are
Climatography of the U.S. (U.S. Weather Bureau 1960), which is good for frequency
of very hot or cold days; Normal Weather Charts for the Northern Hemisphere
(Dept. of Commerce 1952); Weekly Mean Values of Daily Total Solar and Sky
Radiation (Dept. of Commerce 1949); and Sunshine and Cloudiness at Selected
Stations in the United States (Dept. of Commerce 1951). Appendix E illustrates
selected parameters for July conditions for the entire United States. This type
of information is useful for doing "back of the envelope" calculations.

Though several problems arise in using this sort of data, two stand out
as the most serious. The first is the issue of representativeness. The second
is a more specific version of the first, dealing with elevation, Though I will
discuss air temperature specifically, these problems are true to some degree for
all of the meteorological data.

It is crucial that the air temperature data you use adequately represent
your study area. If the readily available weather station data is from a large
city and your study area is in the forested hills above the city, the air
temperatures may not be representative. Similar considerations are ocean (or
other large waterbody) proximity, topographic characteristics (slope), and

35



1 1
A
a c\D Variable Constant a
Temperature
Release
3 oe
«a
J
2 ‘4 3

or
aK
4

Figure 26. Priorities for installation and collection of water temperature
monitoring locations for various stream network configurations. See text for
explantion. After Lifton (pers. conm.).

66



observations and other studies they mention, lend credence to my observation
that it is uncommon for a reservoir to influence temperatures more than 25-30
km downstream. Notable exceptions, however, do exist. Clearly, this will
depend on the volume of release water; the larger the release, the further the
effects will be felt. Cassidy and Holmes rt980) documented "significant"
temperature changes 208 km (129 mi) downstream from Percy Rapids Dam on the
Rogue River, Oregon.

Sylvester (1963) summarized the effects of reservoirs:

The impoundment of water will Produce various temperature
effects on the impounded water temperature and on the downstream
water temperature, depending upon:

Volume of water impounded in relation to mean streamflow.
Surface area of impounded water.

Depth of impounded water.

Orientation with prevailing wind direction.

Shading afforded.

Elevation of impoundment.

Temperature of inflow water in relation to temperature of
impounded water.

Depth of water withdrawal.

Downstream flow rates during critical temperature period,
i.e., an increase or decrease in flow over that occurring
naturally.

©0 NOUEwne

In general, it can be said that large and deep impoundments wil]
decrease downstream water temperatures in the summer and increase
them in the winter, if withdrawal depths are low; that shallow
impoundments with large surface areas will increase downstream water
temperatures in the summer; that water periodically withdrawn from
the surface of a reservoir will increase downstream water tempera-
tures; that a reduction in normal streamflow below an impoundment.
will cause marked temperature increases; and that ‘run-of-the-river’
impoundments, when the surface area has not been markedly increased
over the normal river area, will produce only small increases in
downstream water temperatures.

Ward (1963) employed a simple harmonic model in his investigations of the
influence of reservoirs on Arkansas stream temperatures. He concluded that
(1) the average annual temperature is reduced; (2) the annual variation of
temperature is reduced almost by half; (3) the absolute value of the phase
coefficient of the sine curve is increased. This means that minimum and maximum
temperatures occur later in the year, 38 days in Arkansas; (4) the correlation
index is reduced, and (5) the standard error of the estimate is increased.

Moore (1967) pointed out the similarity between spring-fed streams and
impounded streams, which makes sense. Both are drawing from "reservoirs" of
largely constant-temperature water. His conclusions were generally similar to
Sylvester’s. He did document that temperature changes occurred as much as about
90 km downstream of a newly constructed reservoir; unfortunately, the

75



INSTREAM
FLOW
INFORMATION
PAPER: NO. 13

Biological Report 89(17)
June 1989

TEMPERATURE

STREAM
TEMPERATURE
INVESTIGATIONS:
FIELD AND ANALYTIC
METHODS

AQUATIC SYSTEMS BRANCH



SPECIAL CASES
Predicting Temperature Extremes

Predicting daily temperature extremes is more challenging than predicting
mean daily temperatures. Numerous issues come into play, some quite complicated.

Both the SSTEMP and SNTEMP models suffer from the disadvantage of
untrustworthy maximum temperature simulations and predictions. The thrust of
the SNTEMP model development was mean daily temperatures, and mean daily
temperatures are what it does best and are the only output that has been
"validated." The maximum daily temperature estimation was perhaps something of
an afterthought and suffers from the following problems:

1. The calculations involved are themselves empirical, not theoretical.
It is a matter of getting an essentially instantaneous temperature out of an
otherwise daily average model. Theurer et al. (1984, pages II-30 to II-32),
discuss the derivation of a way to estimate the average afternoon air tempera-
ture, the major component of estimating the maximum daily water temperature.
Regression coefficients were determined for “normal” meteorological conditions
at 16 selected weather stations around the country. Table 11-3 (in Theurer et
al. 1984) shows the R-values, standard deviations, and probable differences for
each of the 16 stations and for all stations combined. Each of these three
statistics is noticeably poorer for all stations combined than for most of the
individual stations. This means that we are not sampling from the same
underlying distribution. This is evident in the tabled regression coefficients
(a0, al, a2, and a3), which are highly variable, often by an order of magnitude,
as well as varying from positive to negative.

This could be improved by performing this same regression for only the
local meteorology at each specific study area. There is a provision to
substitute your own a0 to a3 coefficients in the job control file. (See Theurer
et al. 1984, III-80, record 7, fields 33 to 64.)

2. Correcting the regression coefficients, however, is not likely to
fully correct the maximum daily water temperature calculations in areas within
about six hours travel time from either reservoirs or major tributaries with
markedly different mixing temperatures. The reason is that SNTEMP doesn’t "know"
anything about upstream conditions in predicting maximum temperatures. The
program extends the current reach’s stream geometry "indefinitely" upstream to
simulate the conditions through which the water must travel from solar noon
(assumed mean daily water temperature) to solar sunset (assumed maximum daily
water temperature). This in itself is a major limitation of the model, only
partially corrected in the SSTEMP program. More finely subdividing the reaches
may help correct this problem.

3. The distance the model looks upstream to find the water at solar noon
is a function of flow, width, and Manning’s n, all of which are average values.
Many people have a feel for Manning’s n values only by experience with one of
the National Ecology Research Center’s hydraulic simulation models, IFG4. Such
experience, however, may be misleading because the Manning’s n values in IFG4
are really not hydraulic retardance values at all, but rather act as velocity

101



(Concluded)

C DETERMINE IF THE HOUR IS DURING THE DAY OR NIGHT
BB = 12. - ADY / 2. +C
BE = 12. + ADY / 2.
BT = 4HR
IF (BT .GE. BB .AND. BT .LE. BE) GOTO 3
C CALCULATE TEMPERATURE FOR A NIGHT TIME HOUR
IF (BT .GT. BE) BBD = BT - BE
IF (BT .LT. BB) BBD = (24. - BE) + BT
pDY = ADY-C
TSN = (THX - TMN) * SIN((3.14 * DDY) / (ADV + 2 * A)) + TMN
T= THN + (TSN - THN} * EXP(-B * BBD / ANI)

GOTO 4
c cALcuLaTe TEMPERATURE FOR A DAY TIME HOUR
BBD = BT - BB

T = (TMX - TMN) * SIN((3.14 * BBD) / (ADY + 2 * A)) + TMN
4 CONTINUE

RETURN

END

124


DISCLAIMER

The mention of trade names does not constitute endorsement or recommenda-
tion for use by the Federal Government.

Suggested « itation:

Bartholow, J.M. 1989. Stream temperature investigations: field and analytic
methods. Instream Flow Information Paper No. 13. U.S. Fish Wild). Serv.
Biol. Rep. 89 (17). 139 pp.



adjustment factors--a nice name for a fudge factor. Manning's n values derived
from a water surface profile (WSP) type simulation are likely to be much more
representative. Consultants from Woodward Clyde have told me that measurements
of Manning’s n from hydraulic simulations can be "very inaccurate” compared with
actual measurements from time-of-travel studies. The fact that n or travel time
both vary with discharge, especially at low flows, confounds the situation.

Each of the above reasons taken independently, and certainly combined,
means that one should always treat the maximum daily water temperature
predict fons from SNTEMP with care and should subject the predictions to
validation.

Corrections for the coefficients and Manning’s n should both help.
Neither, however, will eliminate the problem with "looking" upstream. This is
an area for improvement in the programs. Indeed, Woodward Clyde Consultants
have apparently made proprietary improvements to the maximum temperature
algorithms by changing the way the model "remembers" what is upstream. Their
improvements show better correspondence with observations (Voos, pers. comm.).
Even with these changes though, the models leave something to be desired.’

The bottom line is that if maximum temperatures from SNTEMP prove
unsatisfactory with the incorporation of localized a0 to a3 coefficients, the
development of a regression model that includes the mean daily water temperature
and appropriate meteorological parameters in a fashion similar to the approach
outlined in Theurer et al. (1984) is in order. Standard statistical techniques
for inclusion or exclusion of parameters should be done. Occasionally,
innovative approaches will be required, as mentioned in the earlier section on
filling missing water temperature values.

Assessing Probability of Occurrence

A common problem in predicting temperature response arises in what may be
called a worst-case analysis. The problem may be generically characterized as
‘ollows:

You are charged with determining the temperature response of a stream as
a function of reservoir release. Maximum daily temperature for the length of
a specified stream reach is the criterion; the 60-day period from July 15 to
August 15 is known, by inspection, to be the time of worst case conditions. In

7] believe that an empirical (regression) model would perform better than SNTEMP
in predicting maximum daily water temperature, using the existing program’s
Prediction of mean daily water temperature and site-specific meteorological
parameters as independent variables. This must of course be couched in terms
of the assumptions underlying any regression--that the fundamental system
properties have not changed. That is, no changes may be made to the stream
geometry and the hydrologic conditions simulated must be in the bounds of
measured conditions. In addition, the predictions so generated are valid only
at the specific site for which measurements have been made. We have not tried
this approach and therefore cannot attest to its reliability.

102



Law 92-500 (1972). In this legislation, stream temperature increases as a result
of silvacultural practices have been designated as non-point source pollution.
Each State was charged to develop "best Management practices" to control the
temperature increases (Patton 1973; Rishel et al. 1982).

The literature usually takes the form of a report detailing the temperature
changes within study watersheds at different times of the year in different
locations. A thorough reading of these reports typically shows much smaller
changes in water temperature than their abstracts (which invariably describe the
most extreme changes in minimum, average, or maximum stream temperature) would
Jead you to believe. This is not to say that acute lethal temperatures cannot
be reached due to removal of riparian vegetation; it just means that extreme
temperature changes are likely only during specific times of the year, only in
watersheds whose geographic orientation lends itself to direct exposure to the
sun, and usually if the vegetative removal causes other changes, such as an
increase in groundwater temperature.

An unfortunate factor concerning much of the available literature dealing
with shade, especially the earlier literature, is that the authors failed to
clearly document the variables we now know are important, such as stream aspect,
stream discharge, and stream width. In addition, it is deplorable that some
authors did not define whether the temperature changes they measured were for
minimum, average, or maximum stream temperature. An interesting exception is
a paper by Barton and Taylor (1985) that looks at riparian land use in southern
Ontario streams. In their study, the only environmental parameter that clearly
distinguished trout and nontrout streams was weekly maximum temperature. This
temperature, in turn, was largely predictable from the length and width of the
upstream riparian buffer area.

What’s known about shading effects. A brief literature review on shading
effects follows. Meehan (1970) described the temperature changes resulting in
small streams that ran alternately through clearcut or naturally open areas and
sections that were shaded by vegetation in southeastern Alaska. The temperature
differences on overcast days were small compared with sunny days. On clear days,
he found temperature changes as high as +0.21 °C (0.38 °F) per 20 yards of stream
in open areas, and as large as -0.18 °C (-0.32 °F) per 20 yards in shaded
sections. The presentation of results is somewhat misleading, however, because
the rate of change in either direction cannot continue indefinitely downstream.
As water temperature approaches equilibrium, the rate of temperature change will
decrease asymptotically.

Burton and Likens (1973) reported similar results on the Hubbard Brook
Experimental Forest in New Hampshire. They showed rapid heating and cooling of
small, low-discharge streams running through alternately cut and uncut forest
strips on clear, sunny days in duly. They documented rapid heating of 4-5 °C
(7.2-9 °F) in the cut strips followed by similarly rapid cooling in the uncut
strips. Cloudy day temperature changes averaged about 1.5 °C (2.7 °F). Though
no discussion of methods is given, they reported that deciduous forest areas
Provided 50% to 60% shade during winter from the stems and branches alone. They
also speculated that shade removal does not lead to as much nighttime cooling
as expected, due to higher subsurface heat storage (in channel rocks and debris)
in open water during the day. They state that the influx of cool groundwater

23



Corresponding maximum

—_ 32°
——= 36°

WATER TEMPERATURE (°C) air temperatures
20 225 25 275 30
0 T
| |
rye
3 1 t r
g _——| If /
2 x= 6 [ ! oy
s E Lr
wi fa 1
Oo 9 < _ LEGEND
E | --n — Flow 8cfs
= 2 4 n —-— Flow 26 cfs
A H —— Flow 44 cfs
nn \ ——— Flow 83 cfs
15 T 1
16.5 £ y _

Figure 31. Temperature profiles within a pool at various flows. Illustrates the elimination of
stratification at higher flows. Adapted from California Department of Water Resources (1976).



An additional level of difficulty is introduced if the proposed or actual
withdrawal is of the multilevel design such that water may be removed from one
or more vertical tiers within the reservoir. Such a multilevel design may be
used to help control a variety of water quality attributes incl uding temperature,
dissolved oxygen, and turbidity. Though the difficulty of “predicting” release
temperatures is increased in such a situation, the flexibility of meeting
temperature release targets for a specified time is markedly increased. Exact
targets should not be expected; instead, target "windows" provide the needed
water management flexibility (Cassidy and Holmes 1980). Sine curve correlation
may be a potentially useful technique for predicting water temperatures at
different depths in a reservoir (Kothandaraman and Evans 1970), but care must
be used if alternative water management scenarios are likely to change these
profiles very much. It is apparently rare for geographic stratification patterns
to recur on a yearly basis (Wunderlich and Elder 1967).

In reservoirs with multilevel outlets, the temperature of the outflow is
highly dependent on inflow hydrodynamics. In addition, the actual water strata
withdrawn is not as simple as might be expected. Water is "sucked-in" based on
the velocity, the relative density differential of the water layers, and the size
of the intake structure. The higher the velocity, the more likely it is that
water layers above the intake get entrained. Consultation with an experienced
hydrologist-engineer is highly recommended.

In addition to the thermal release characteristics of reservoirs, there
are a variety of water quality concerns, both during the initial filling
(Gunnison et al. 1986) and operation (Terrell et al. 1982), that are beyond the
scope of this paper. However, because reservoir release temperatures are so
often integrally related to the modeling of downstream temperatures, we will list
a variety of models available for performing predictive analysis (largely taken
from EPA 1984, and Corps of Engineers 1987). No attempt will be made to evaluate
these models here.

1. Water Analysis Simulation Program (WASP) - A one- or three-
dimensionat general reservoir water quality, including temperature,
model for planktonic analysis. Model is public domain with user’s
manual and technical assistance available from EPA’s Large Lakes
Research Station in Grosse Isle, Michigan.

2. CLEAN Series - General reservoir modeling package for well-mixed to
stratified conditions in reservoirs, fish ponds, and alpine lakes.
Model is public domain with user’s manual and technical assistance
available from EPA’s Environmental Research Laboratory in Athens,
Georgia.

3. LAKECO - A one-dimensional mode? for calculating temperature,
dissolved oxygen, and nutrient profiles for wasteload allocation.
Model is public domain with user’s manual and technical assistance
available from the Corps of Engineers Hydrologic Engineering Center
in Davis, California.

4, Water Quality for River Reservoir Systems (WQRRS) - Similar to
LAKECO, but can also consider river flow and quality. Model is

77



be presented. Numerous questions always arise as to where and how inputs to any
temperature model can or should be obtained. For each model input, I explain
what it is, what’s known about it, and how to measure or estimate it. Third,
ideas on what constitutes proper calibration/validation for temperature models
are discussed, as there seems to be a lot of confusion over these and similar
terms. I discuss what the terms mean, how to do things the "right" way, and when
to do what. Finally, I give a brief review of alternative temperature models
that may be used in place of the SNTEMP set of models, and a brief review of
reservoir and other water quality models that may be used in conjunction with
stream temperature models.

One reviewer noted that this report will not be a "bestseller"; its
audience is fairly specific, though the material is broad and diverse. It is
directed towards those who have at least a general knowledge of Theurer’s Stream
Network Temperature Model and want to become more proficient in planning field
activities or engaging in simulation/analysis techniques. Others may benefit
from information contained here, but that is not the primary purpose.

PLANNING A TEMPERATURE STUDY

The Aquatic Branch is constantly reminded by users of our models,
especially the more complicated SNTEMP-type models, that we need to stress the
need for careful study design. We assume that this means that there is
difficulty in making sure that you (1) are going to be answering the right
questions, (2) are using the right set of tools, and (3) can trust your answers.

The Aquatic Branch has written volumes on laying out a study plan
(Bartholow and Waddle 1986) and scoping questions to ask (Bovee 1982). These
publications have not seemed to dent the continued insistence that study design
is critical. Therefore, we can only conclude that adequate study plans are not
being assembled. Some have suggested that better prestudy involvement between
all members of the “team" needs to be stressed. That is, planners, field data
collectors, modelers, statisticians, decision makers, regulators, resource
interests, developmental interests, and reviewers all need ACTIVE involvement
to (after Henriksen, 1988):

(1) identify the management problem (goals and objectives). Does this
study deal with water rights or flow reservations? Is it to assess
project impacts, evaluate mitigation, or approve permits? If it is
an impact analysis problem, what is the appropriate baseline period
with which to compare impacts? Are we at the feasibility or
operational stage in the planning process? Is this a single project
or a network of projects? Who are the players; who has the lead?
How "important" is this project; is there a lot of resistance to a
study of this type?

(2) identify the appropriate species/life stages of concern. Is this
a game, sport, or commercial fishery problem? Is it a sensitive or
indicator species problem? Is it an endangered species problem?

5



Figure 21. Effect of a shelter belt on various wind speed classes.

Wind speed (m sec~1)

duced from Geiger (1965).

«

Windward!
wt
“

| Leeward

Percent of wind speed in the open
z

+++ Deciduous woods in winter
—: Loose shelterbelt

—— Medium-dense shelterbelt
+-++Very dense shelterbelt

|

Figure 22. Effect of various shelterbelt heights on wind speed.

from Geiger (1965).

’ ry

1s 20 as 0

Muluples of shelterbelt height

47

Repro-

Reproduced



five stream segments or 30 time periods or scenarios. Automated data filling
and smoothing, coupled with postsimulation statistical evaluation, makes this
truly a high-powered approach. Additional linkages to microhabitat models
(Bartholow and Waddle 1986) add to its flexibility. It is primarily to the
network model that the following sections are devoted.

CALIBRATION/VALIDATION

It is worthwhile to review some basic concepts related to the testing and
usefulness of al] modeling efforts, including temperature modeling. There are
several terms, often used indiscriminately, that variously purport to describe
the process of ensuring that a simulation model “works,” be it purely statis-
tical, purely process-oriented, or (most likely) a combination of the two.
There are both quantitative as well as qualitative measures for determining the
overall usefulness of a model.

It is not my purpose to debate the many definitions that have been used
for these terms in the past, but rather to supply operational definitions useful
to describe the overall processes involved, so that we will know when we have
a_useful model. See the published guidelines by the General Accounting Office
(1979) for more in depth discussion of validation definitions. What we are after
is the answers to operational questions: How good are these models? How well
do they represent natural systems? Can they be used for management and
regulatory decisionmaking? What level of confidence can we place on the results?
(Donigian 1983). (Also see an interesting paper by Mankin et al. [1975].)

The terms used will be better understood in the context of the scientific
method. This begins with a concise statement of the problem and study
objectives. These are intimately tied to the decisions that need to be made.
Next, the problem is analyzed in a process consisting of observation, measure-
ment, sampling, and experimentation. Solution methods are examined, with the
choice of techniques made from a set of criteria. The solution is evaluated
with respect to reliability and sensitivity. If successful, the results or
decisions are implemented (Reckhow and Chapra 1983).

“Calibration," for our purposes, is defined as the process of determining
"proper" values for an existing model’s parameters. Note that I said "existing"
mode] because I do not, at least initially, want to cover model development.
If the structure and function of a mode? have been shown to be satisfactory for
some types of problems in the past, we may only be faced with choosing "proper"
rates for describing how much one thing changes with respect to something else,
or supplying a measurement or estimate for other environmental values.
Calibration should always constrain the values of parameters to be within
"reasonable" limits. If one must deviate from the reasonable, one will be forced
to retreat to reformulation of the model.

“Verification” is used to describe the testing that is done as part of
the calibration process. Verification is the process of testing the model’s
output (spatial and/or temporal patterns) against “real world” measurements,
looking for congruence, or the "best fit," with what we have previously observed.
If we cannot supply parameters that produce output that is congruent with the

89



Water surface:
T

Toe T Hl 16-6 T TOs H 5 T 1257 T 13.0
1 | re) 1 qa I 12.9
1 \ I \ i
10.8 | \ i 1 i
| i i i \
2 | f ' I 4
@ 108 ! 108 ' ! 1
rs i i ‘
e 1 | i
u i H
¥ ws! | !
zat 1 ' i 4
z ' i
4 en
€ 10.8 f i
e 1 1
i |
'
zL © | |
°° | 1
Fa ws | |
re 1 ® }
a «4
Note: Numerals are point temperatures in °C.
tos: delineate lararel limite
a of each numbered subsection.
1 t ® 1 n 4 L 1 L
19, s 10 1s 20 25 30 35 40 45

WIDTH, IN METRES.

Figure 27. Cross-sectional temperatures showing degree of temperature
variation for a given transect at a given flow. Reproduced from Stevens et
al. (1975).

Standard thermometers are calibrated for use by what are known as “total”
immersion, "complete" immersion, or "partial" immersion instruments. The needs
of taking temperature calibration measurements are best met with the total
immersion thermometer (ASTM 1983a). In this case, proper calibration requires
that you either totally immerse the mercury column or use a correction factor
equation. For most water temperature applications, the calibration thermometer
of choice is the ASTM 63C, chosen because of its appropriate range and units (-8
to +32 °C, 17.6 to 89.6 °F), its graduation scale (0.1 °C, 0.18 °F), and its
maximum scale error (0.1 °C, 0.18 °F). If you can get by with a somewhat smaller
scale (0 to 30 °C, 32 to 86 *F), the model 90-C may be a better choice, as it
needs to be immersed to only 76 mn. See ASTM (1983b) for a thorough description
of the options available and ASTM (1983c) for more complete definitions. ASTM
63-C or 90-C thermometers are available from several vendors for about $50 (see
Appendix B). You would be wise to purchase two.

The thermometer used for calibrating other instruments should be lightly
tapped against a resilient surface prior to use to eliminate small separations
in the mercury column. If substantial separations still exist, an ice bath may
be used to draw all of the mercury into the bulb (ASTM 1983a). Unfortunately,
this procedure is not readily adaptable to field conditions, where it may be

68



CONVENTIONS FOR DETERMINING
STREAM AZIMUTH AND EAST/WEST BANK

a) iT b)
H i
+90°{ Ww | E -90° +90° Ht -90°
|
0° 0°
c) d) Tt
“5 -90° +90° {==}. - 90°
0° N 0°
“ —- ‘
8
e) f)
+00° -80° ~Sy) -90°
o° o°
9) h)
+ “(yh ~90° ~(f) -80°

0° o°
Figure 11. Conventions for determining the sign and degree of stream orienta-
west bank designations. Note that the

tion (azimuth) as well as east and
direction of stream flow (single-headed arrow) is unimportant for these
determinations.

29



If there is a significant deviation, Parton and Logan (1981) have developed a
method for determining hourly temperatures from daily maximum and minimums.
Their method hinges on the use of three empirical coefficients known as a, b,
and ¢ (see the program in Appendix C). Ideally, such a model should be
parameterized for these three coefficients, but their paper showed little
sensitivity to changes in them. The a and c coefficients can be easily estimated
for your site; the b coefficient should assume a value of about 2.1 (dimension-
less). Using this routine to calculate the 24 hourly estimates and take their
mean should give a better number for the daily average air temperature if (1) the
maximum temperature occurs before or at sunset, and (2) the minimum occurs during
the early morning hours.

The internationally accepted definition of microclimate refers to that
layer of air from ground level (or water level in our case) to a height of two
meters (Geiger 1965), which is the standard elevation for establishing climate
shelters. More important is the relation between the height of the instrument
and the river. Figure 18 shows the results from one Tennessee river experiment
(Troxler and Thackston 1975). These results tend to justify using somewhat

cooler air temperatures than typical measurements taken at dams or valley tops
would provide.

The time of year associated with the most rapid changes in mean daily
temperature are close to April 21 and October 23, one month later than the
corresponding equinoxes (Blackadar 1984). This would be a time to pay closer
than usual attention to air temperatures, especially for a daily, or smaller,
time-step model.

Rel midi

Relative humidity, like air temperature, can be a very sensitive parameter.
Also, like air temperature, relative humidity can be very different on site than
at a long-established weather station miles away in a concrete jungle. We
recommend that at a minimum, verification humidity measurements be taken twice
a day for some period of time, either at 5 a.m. and 5 p.m. or 11 a.m. and
11 p.m., to be in sync with the NOAA data and allow comparative calculations if
necessary.

A sling psychronometer may be used for spot measurements and for
calibration checks on established recording stations. One can be purchased for

4a



Cowx, I.G., W.0. Young, and J.P. Booth. Thermal characteristics of two regulated
rivers in Mid-Wales, U.K. Regul. Rivers 1:85-91.

Crisp, D.T., and G. Howson. 1982. Effect of air temperature upon mean water
temperature in streams in the North Pennines and English Lake District.
Freshwater Biol. 12:359-367.

Crittenden, R.N. 1978. Sensitivity analysis of a theoretical energy balance
model for water temperatures in small streams. Ecol. Model. 5:207-224,

Croley, T.£., A.R. Giaquinta, and R.A. Woodhouse. 1981. River thermal standards
costs in the upper midwest. Am. Soc. Civ. Eng., J. Energ. Div. Proc.
107(EY1):65-77

Currier, J.B., and D. Hughes. 1980. Temperature. Chapter VII in An approach
to water resources evaluation of non-point silvacultural sources (a procedural
handbook). U.S. EPA Environmental Research Lab. Athens, GA. [EPA-600/
8-80-012] NTIS PB81-119828. v.p. (approx. 780 pp.)

Devries, A.L. 1971. Freezing resistance in fishes. Pages 157-190 in W.S. Hoar
and D.J. Randall, eds. Fish physiology. Academic Press, New York. 559 pp.

Dodge, D.P., G.A. Goodchild, I. MacRitchie, J.C. Tilt, and D.G. Waldriff. 1984.
Aquatic habitat inventory surveys. Ontario Ministry of Natural Resources.

Donigian, A.S., Jr. 1983. Model predictions vs. field observations: the model
validation/testing process. Pages 151-171 in . Swann and A. Eschenroeder,
eds. Fate of chemicals in the environment: compartmental and multimedia
models for predictions. American Chemical Society. ACS Symposium Series 225.
Washington, DC.

Dymond, J.R. 1984. Water temperature change caused by abstraction. J. Hyd.
Eng. 110(7):987-991.

Edinger, J.£., D.K. Brady, and J.C. Geyer. 1974. Heat exchange and transport
in the environment. Cooling water studies for Electric Power Research
Institute. Research Project RP-49, Report No. 14. 125 pp.

Ellis, S.S., S.L. Ponce, and L.£. Siverts. 1980. A temperature model for smal}
mountain streams. Symposium on watershed management. Am. Soc. Civ. Eng.
1:494-503.

Feller, M.C. 1981. Effects of clearcutting and slashburning on stream
temperature in southwestern British Columbia. Water Resour. Bull. 17(5):863-
867.

Ferraro, F.A., A.E. Gaulke, and C.M. Loeffelman. 1978. Pages 125-136 in ASTM
Special Publication #652. American Society for Testing and Materials.
Philadelphia, PA. 184 pp.

Ficke, E., and J.F. Ficke. 1977. Ice on rivers and lakes - a bibliographic
essay. USGS Water-Resources Investigations 77-95. 173 pp.

110



either missing or in error at night. Since percent possible sun is used as a
surrogate for cloud cover, those measurements that are taken may not be good
estimates for nighttime conditions, especially in areas with marked diurnal
weather patterns.

None of these measurements really get at the "quality" of the cloud cover.
Cirrus and nimbus clouds provide markedly different types of solar radiation
attenuation and atmospheric reradiation. In short, percent possible sun
estimates may be a good candidate for model calibration. In other words, if you
have poor estimates, treat them with the uncertainty they are due.

Wind

Though wind direction is not important in the stream temperature models,
speed is. Wind is perhaps most important in convective and evaporative heat
flux. Wind is the meteorological parameter that one would least like to
translate from off-site; the effects of topography on wind are too varied and
complex (Geiger 1965). An exception might be for biweekly or monthly time steps.
On shorter time steps, if you cannot measure wind speed, use it as a calibration
parameter. In other words, you may vary wind speed in the models within some
reasonable bounds to effect a better match between observed and simulated water
temperatures. Some water temperature models use wind speed almost exclusively
as a calibration parameter (Laenen and Hansen 1985).

A variety of devices are available for measuring wind speed. Unlike
standard meteorological measurements, however, we are not interested in
anemometer measurements from a 12 or 20 foot tower. Wind speed should be
measured near the water’s surface, subject to the typical constraints (soil
banks, riparian vegetation) at that level. Because of air turbulence near the
water’s surface, the wind speed there is seldom less than 1 m/s (Krajewski et
al. 1982). See also Figures 21 and 22, which show the effects of shelter belts
(i.e., riparian vegetation) on wind speed. These figures may be useful if timber
harvest is a management action,

Available wind speed data may be from a standard 10 m (30 ft) anemometer

tower. Under certain circumstances it may be desirable to try to correct for
the observation height. Linsley et al. (1975) presents a formula for this:

Vh . In{(zh/z0) + 1
Vm ~ In[(zm/z0) + 1]
where Vh = velocity at desired height, cm/s
Vm = velocity at measured height, cm/s
zh = height desired, suggest 10 cm
z0 = roughness length, suggest 10 cm

zm = height of measurement, cm

46



Table D-4. Textual output file from Hydrodata (tm U.S. West Corporation) for
streamflow (cfs). There may be many variations on streamflow statistics, such
as reservoir storage (af, mg, kaf, mcf, or cfs-d) or level (ft), and river
stage (ft).

08066100 WHITE ROCK CREEK NR TRINITY, TEX.
LOCATION.--Lat 31:03:06, Long 095:22:40, Hydrologic Unit 12030202.
DRAINAGE AREA.-- 222,00 mi2 (874,98 km2).
GAGE.-- Altitude of gage is 0124.30 Ft{ 38 m).

STREAMFLOW {CFS}, WATER YEAR OCT 1984 TO SEP 1985
MEAN

DAY et Nov Dec Jan Feb Mar Apr May Jun dul Aug Sep
i o i 3 900 38 894 43 38 9.7 4h il t]
2 0 146 26 928 33 1660 36 29 7.8 38 6 Q
3 0 194 2k 399 30 494 2g 27 6.9 35 48 0
4 0 us i8 474 31 158 25 18 6.0 33 34 i]
5 tf] 76 1g 212 a7 98 22 15 5.4 31 23 0
6 tf] 60 201 $1 56 65 19 12 4.8 27 A? o
7 o BL 152 58 SL 49 18 H 42 3.7 10 a
8 6.9 45 48 43 ci 41 Vv 9.8 37 6.7 08 0
9 3.0 38 32 35 3 37 1b Fee 3.2 6.8 06 a
10 iia al 26 43 248 32 18 87 4.0 6.8 +05 Q
nh 36 39 23 206 1660 30 Ug 9.5 4.5 §.3 204 o
w 84 35 2i 83 2400 28 14 8.4 3.5 5.3 +02 9
13 5.2 33 203 44 534 26 14 32 2.8 3.4 010 Q
4 10 33 1020 35 127 26 2B 3250 2.4 48 G o
15 5.3 32 1280 RK 83 7 Ex] 2350 2.2 42 9 o
16 2.3 7 650 406 61 31 12 416 2.0 z.4 9 0
? 2.0 30 900 1590 4g 2g 2 88 1.8 3.9 Q i
18 9.81 63 356 2160 42 25 i so 1.6 6.3 0 Q
ig 55 78 112 572 36 22 10 33 14 10 a] Q
20 934 70 63 141 32 4i2 $.9 27 13 23 Q 8
pal 2720 a? 50 81 1290 Og §3 LL 302 0 0
22 2680 35 39 57 1030 9.9 79 1.0 97 0
23 1380 30 32 48 164 il 54 90 25 0 0
24 1250 28 27 46 83 398 32 80 1§ cu 0
25 2330 30 24 43 57 2130 24 72 10 Q 0
26 1380 36 19 38 45 $88 20 65 7 g 0
27 388 312 ai 36 ae 202 1B 58 5.8 9 9
28 847 $13 ai BL 53 ASL Vv 53 4.6 0 o
29 4100 110 ai 88 65 66 is 14g 3.5 0 o
30 2100 a7 ral 37 44 49 ¢4 45 2.7 Q o
SL 319 = 186 43 oot 3g ~— 33 1.8 g ~
Total 20331 2535, 5671 9041 9098 7073 3976 7086 87 570 3.4 t
Hean 656 84 183 292 325. 228 133 228 2.9 18 lL 0
Max 4100 513 1280 2160 2400 1646 2130 3250 9.7 302 LL 0
Min ) 28 18 33 30 22 9,8 B84 45 27 0 0
Ac-Ft 40326 5028 11248 «17833 18046 = 14029 7886 = 14014 172 1130 6.7 0
WIR YR 1985 = TOTAL 65450 MEAN 179 MAX 4100) MIN Q  AC-FT 129817

131


about $50 from a forestry supply house, or one can be easily constructed.? The
psychrometer results in two measurements, the wet and dry bulb temperatures.

9 T T T T T
———-—--—— ELEVATION: 80 FEET ABOVE CANEY FORK RIVER
Fee ee ELEVATION: § FEET ABOVE CANEY FORK RIVER 4

24 12 24 12 24 2 24 2
Monoay | = TUESDAY =| WEDNESDAY | ‘THURSDAY | FRIDAY

Figure 18. Variation of air temperature above the Caney Fork River, TN. This
figure shows that near-water air temperatures are buffered from, and on
average less than, air temperatures more representative of valley-top condi-
tions. Reproduced from Troxler and Thackston (1975).

Convert the two temperatures to relative humidity with the program in
Appendix C, which should run with little or no modification on any computer with
BASIC. Simple hand calculations following the algorithms in the program would
be sufficient also. As an example, if the wet-bulb temperature was 65.0 °F, the
dry-bulb temperature was 59.5 ‘F, and the elevation was 1,000 feet above sea
level, the relative humidity would be 73%.

If relative humidity is taken as part of an established recording weather
station, you may not need to make forced air ventilation for the wet/dry bulb

"Glue two identical thermometers to a thin wooden paddle with the bulbs
projecting from one end, one slightly more than the other. Drill a hole in the
other end of the paddle and loop a sturdy piece of nylon twine through it such
that the paddle can be twirled rapidly. Wrap a few layers of clean cloth or
cotton string around the thermometer bulb that is mounted lowest on the paddle
and affix with a rubber band. Dip this cloth in water and whirl the paddle
around until the temperatures do not change. Read the temperatures quickly and
accurately, to tenths of a degree if possible. The two readings are known as
the wet-bulb and dry-bulb temperatures for obvious reasons (Blackadar 1983).

42



sketchy information for the Yakima River basin, which is not meant to be
representative of other locales. To paraphrase:

Irrigation return flow temperatures may be warm or cool depending
on the situation. The storage of irrigation water in deep reser-
voirs generally provides a cooler source to river points above major
diversions. Below these diversions, water is diverted resulting in
about 20% loss due to spillage and over-irrigation. The rest is
applied to irrigated land where it evapotranspires (roughly 40% of
the diverted water) resulting in a cooling of the soil. About 45%
of the water enters the groundwater where one half of this returns
to the parent stream during the irrigation season and one half
returns from bank storage during the non-irrigation season. In
either event, part returns to the stream in open drainage channels
or subsurface drains. Open drains resulted in temperature rises of
about 3.3 °F (1.8 °C). Sub-surface drains display a drop of about
5.3 °F (2.90 °C). Thus the proportion of water returning by each
method may be important.

For this reason, the SNTEMP model allows for proportioning this lateral flow.
For SNTEMP, Theurer (1984) recommends estimating the open drain return
temperature as equilibrium temperature; the remainder may be estimated as
ground-water temperature.

Data Correction and Reduction

There are a number of problems that arise in reducing temperature data.
If your data is obtained from an external source, it may be wise to apply the
so-called "maximum-minimum test" (Moore 1967). This comparison follows the
logic that the maximum water temperature on any day (or really any time period)
cannot be less than the minimum temperature on the preceding or following days
because the temperature cannot change instantaneously. Since rounding errors
may be present, only discrepancies of more than the minimum resolution should
be scrutinized. Always ask for and test data in the original units to minimize
rounding and reporting errors.

The sources for error in your own data reduction, particularly in analog
recording devices, such as strip chart recorders, are many and varied
(Figure 28). The constant error is the most common and can be corrected by
adding or subtracting the bias. Nonuniform errors require at least two-point
calibration. Both types of errors may be compounded if it is known that drift
over time has occurred. If so it is usually assumed that the drift rate has
been constant. I recommend that you keep a log of calibration checks so that
these errors may be corrected. Cold water temperatures are known to affect some
mechanical recorders. These errors should be considered uncorrectable unless
some correlation with a properly functioning recorder is possible.

Most field temperature recorders now have some automatic or semi-automatic
method of generating a computer readable digital file of temperature measure-
ments. At worst, a strip or circular chart must be sent to a lab for digitiz-
ing. At best, there is software available for summarizing already digitally
recorded data. However, you should check beforehand to see whether the

71



one might find warmer water flowing across the top of the pool, or cooler water
flowing along the bottom (or even at intermediate depths).

Neel found that water surface elevations in riffles were fairly reliable
indicators of thermal stratification in downstream pools, even very shallow
ones. If the water surface elevation fell (slowly) below a specified level,
thermal stratification would be found. However, this was not true if water
levels were fluctuating frequently. These fluctuations would combat the
formation of thermal layers. This principle is nicely shown in Figure 31, which
depicts the maximum water temperature as a function of depth and flow for the
Eel River. For example, an 8 cfs flow creates a thermocline at a depth of 4-5
feet. Increasing the flow to 44 cfs lowers the thermocline to 10-12 feet. At
83 cfs there is no thermal stratification. This figure also illustrates that
stratification maintains a marginal habitat even on very hot days. Thus, under
conditions of very limited water supply, it may be preferable to actually reduce
flows in order to protect cool-water refuges, such as these California pools used
by juvenile anadromous fish. At a minimum, care should be taken not to increase
flows in the afternoon, thus disturbing the thermally segregated water.

Andersen and Miyajima (1975) found that they could lower stream tempera-
tures in constructed pools by I to 4 °F (0.6 to 2.2 °C), and that peak
temperature (over 72 °F, 22.2 °C) duration could be shortened from 12 hours in
riffles above the pools to 1-2 hours in the pools (Figure 32).

Cool-water refuges in or near tributary mouths present more of a problem.
We know that thermal segregation occurs some distance downstream from tributaries
(or other point sources) even though the models assume instantaneous and thorough
mixing. At times it may be desirable to estimate this longitudinal mixing
distance. One approximation for rivers and streams in which the depth is less
than one tenth of the width is (Milhous, pers. comm., adapted from Ruthven 1971):

. 2,088.4
(8/8) +

where L = mixing length (feet) that assures the variation in temperature
across a section arising from a point source interjection does not
exceed 10%
W = average stream width (feet)
D = average stream depth (feet)
n = average Manning’s n (roughness)

For example, suppose you had a river 100 feet wide, 5 feet deep, with an n value
of 0.06. The length to near complete mixing would be:

2
0.085 100 850
1 = 505/68) 4 9 og 3082 * 0.06 ~ 3708 feet

81


thermal inversions. Keep these issues in mind if you perform any sort of
correlation between off-site data and spot on-site measurements, as recommended
by some authors (Raphael 1962).

Table 4. Availability of useful data elements from two forms of Local
Climatology Data Summaries.

Annual Monthly
Item (monthly data) (daily data)

Maximum temperature
Minimum temperature
Average temperature
Normals

Extreme temperature
Wind speed

Percent possible sun
Relative humidity
Dew Point

Station location

>< KE DE De De OK OE >
>< eK >< >< ><

The second major problem involves the use of a single air temperature to
represent all of the elevation zones in your study area. It has been common
for streams being modeled to originate in the headwaters, perhaps with a
reservoir in those headwaters, and flow through a large elevation change. It
has proven inadequate to compute, or allow the model to compute, a single,
translated air temperature. Lapse rates (the decrease in air temperature with
increasing elevation) will themselves vary for the same reasons mentioned in
the preceding paragraph, and at different times of the year.

For example, Leffler (1981) found variations in Tapse rates of as much as
1.8 °C (3.2 °F) per kilometer between winter and spring. Figure 15 shows the
frequency distribution of lapse rates measured in England over a 3-year period
{cited by Geiger 1965). The adiabatic lapse rate is considered the standard and
is the rate at which temperature changes with elevation such that the heat
content remains the same. The figure shows that the median (50%) lapse rate
measured is slightly smaller than the adiabatic rate. This begs the question
to some extent, however, because the lapse rate we are interested in is that rate
still at ground level with changing elevation, not going from ground level up
into the air, the way it is most commonly measured.

36



Omnidata

P.0. Box 3489
Logan, UT 84321
801-753-7760

Pioneer & Company
216 Haddon Ave.
Westmont, NJ 08108
609-854-2424

Ryan Instruments

P.0. Box 599

Redmond, WA 98073-0599
206-883-7926

Thomas Scientific

Vine Street at Third

P.0. Box 779

Philadelphia, PA 19105-0779
215-574-4500

Weathertronics
Instrumentation

Box 41039

Sacramento, CA 95841

122

All instrumentation

Waterproof thermograph

housings

Thermographs

ASTM thermometers

Meteorological supplies



To end on a positive note, it has been shown that analysis of data for a
mere two-year time-span leads to the same general distribution of equilibrium
water temperatures as does a ten-year time-span (Hogan et al. 1973). Presumably,
this may mean that we could consider a two-season data collection effort
comprehensive in the sense of giving us an appropriate mean and standard
deviation for water temperatures. Knowing the standard deviation, one could
easily compute the n-th exceedence value,

106



Nobel, R.D. 1979. Analytical prediction of natural temperatures in rivers.
Am. Soc. Civ. Eng. Proc. [EE 5 no. 14870}:1014-1018.

Nobel, R.D., and A.P. Jackman. 1980. Predicting the natural water temperature
profile throughout a river basin. J. Environ. Syst. 9(4):361-381.

Osterkamp, T.£. 1979. Mercury-in-glass thermometers for precise temperature
measurements near OC. J. Glaciol. 22(87):385-388.

Ozaki, V.L. 1988. Geomorphic and hydrologic conditions for cold pool formation
on Redwood Creek, California. Redwood National Park Research and Development
Technical Report 24. Redwood National Park, Arcata, CA. 57 Pp. + appendices.

Parton, W.J., and J.A. Logan. 1981. A model for diurnal variation in soil and
air temperature. Agric. Meteorol. 23:205-216 and errata p. 219.

Patton, D.R. 1973. A literature review of timber harvesting effects on stream
temperature: research needs for the Southwest. USDA Forest Service Research
Note RM-249. 4 pp.

Pauszek, F.H. 1972. Water-temperature data acquisition activities in the United
States. | Water-Resources Investigations 2-72. U.S. Geological Survey,
Washington, DC. 54 pp.

Platts, W.S. 1981. Stream inventory garbage in--reliable analysis out: only
in fairy tales. Pages 75-84 in N.B. Armantrout, ed. Acquisition and
utilization of aquatic habitat inventory information: proceedings of a
symposium, 1981 October 28-30, Portland, OR. Western Division, American
Fisheries Society.

Platts, W.S., W.F. Megahan, and G.W. Minshall. 1983. Methods for evaluating
stream, riparian and biotic conditions. Gen. Tech. Rep. INT-138. Ogden, UT.
Intermountain Forest and Range Experiment Station. 70 pp.

Platts, W.S., C. Armour, G.D. Booth, M. Bryant, J.L. Bufford, P. Cuplin, 5S.
Jensen, G.W. Lienkaemper, G.W. Minshall, S.B. Monsen, R.L. Nelson, J.R.
Sedei], and J.S. Tuhy. 1987. Methods for evaluating riparian habitats with
applications to management. General Technical Report INT-221. Ogden, UT: U.S.
Dept. of Agric., Forest Service, Intermountain Research Station. 177 pp.

Pope, D., and B. Lafferty. 1987. Selecting leave trees in a streamside
management zone to minimize increases in stream temperature - Canton Creek.

Pages 67-69 in Managing Oregon’s riparian zone for timber, fish, and wildlife.
NCASI Technical Bulletin No. 514. 96 pp.

Quigley, T.M. 1981. Estimating contribution of overstory vegetation to stream
surface shade. Wildl. Soc. Bull. 9(1):22-27.

Raphael, J.M. 1962. Prediction of temperature in rivers and reservoirs. J.
Power Div., Proc. Am. Soc. Civ. Eng. 88:157-181.

nS



APPENDIX A. DATA SOURCES

Hydrodata/Climatedata

US West Knowledge Engineering, Inc.
4380 South Syracuse Street

Denver, CO 80237

303-694-4200

National Climatic Data Center
Federal Building

Asheville, NC 28801-2696
704-259-0682 or FTS 672-0682

U.S. Forest Service Fire Data Center
3905 Vista Ave.

Boise, ID 83705

208-334-9458 or FTS 554-9458.

WATSTORE

NAWDEX Chief of User Services
U.S. Geological Survey

421 Nationa? Center

Reston, VA 22092
703-648-5664 or FTS 648-5664

120

Meteorological and
hydrologic data

Meteorological data

Meteorological data

Meteorological and
hydrologic data



Biological Report 89(17)
June 1989

STREAM TEMPERATURE INVESTIGATIONS:
FIELD AND ANALYTIC METHODS

Instream Flow Information Paper No. 13

by

John M. Bartholow
U.S. Fish and Wildlife Service
National Ecology Research Center
2627 Redwing Road
Fort Collins, CO 80526-2899

U.S. Department of the Interior
Fish and Wildlife Service
Research and Development

Washington, DC 20240



radiation, vi, vii, viii, 10, 12,
20, 21, 24, 27, 28, 32, 34, 35,
39, 43, 44-46, 48, 50, 55, 86,
88, 94, 96-98, 103, 105, 113,
114, 116, 118, 127, 134

random, 14, 27, 87, 94, 127

reconnaissance, 70

reflectivity, viii, 48, 50, 51

regression techniques, iii, 14, 16,
24, 39, 65, 85-88, 90, 93-96,
99, 101-103, 126,

reservoir, iii, 1, 5, 7, 36, 43, 53,
54, 65, 67, 71, 74-78, 85-87,
92, 95, 98, 100, 101, 102, 103,
107, 109, 111, 115, 119, 131

residuals, error, 93, 111

resolution, 63, 71, 97, 104

retrieval, data, 56, 61

rhodamine dye, 18

riparian vegetation, vi, 7, 13, 2l,
23, 24, 26-28, 31, 46, 92, 96,
97, 108, 113, 115, 118

RMS error, 20, 98

roughness, streambed, 8, 10, 14, 16,
46, 48, 81, 83, 98

routing, 18, 98, 112

Ruthven equation, 81, 116

samples, 27, 32, 59, 62, 63, 86, 87,
89, 93, 100, 101, 108, 111

saturation, 20, 33, 125

scoping, 5

screening, 21, 30, 32

seeps and springs, 79

segment, stream, 7, 10, 13, 14, 18,
26. 27, 32, 65, 88, 89, 98, 107,

SELECT model, 32, 53, 69, 74, 78

sensitivity analysis, iii, v, vi,
viii, 6-8, 10-12, 13, 20, 21,
26, 39, 41, 54, 65, 84, 85, 88,
89, 90, 103, 110, 116

shade, riparian, vi, 7, 8, 10, 21-
27, 30-34, 40, 69, 70, 75, 85,
88, 96-98, 100, 103, 107, 114,
M5, 117, 126

shadow, 26, 34, 43, 44, 69

Sine wave, 40, 75, 77, 87

sinuosity, stream, 13

site selection, 65

138

SNTEMP model, i4i, vi, viii, 1, 5.
7, 10, 11, 13, 14, 16, 19, 20,
26, 43, 45, 50, 51, 53, 54, 72,
its 78, 86, 88, 93, 95, 97, 101,
10:

soil, 46, 48, 51, 71, 109, 113, 115,
117, 123, 127

solar, vi, vii, viii, 10, 12, 20,
21, 24, 28, 28, 32, 43-46, 48,
50, 55, 86, 88, 94, 97, 98, 101,
103, 105, 107-109, 114, 116,
118, 127, 134

solar radiation, vi, vii, viii, 10,
12, 20, 21, 24, 28, 32, 35, 43-
46, 48, 50, 55, 86, 88, 94, 97,
98, 103, 105, 114, 116, 127, 134

solarimeter, 34

springs and seeps, 36, 48, 54, 56,
70, 75, 76, 80, 84, 105

SSSHADE program, 24, 26, 88, 107

SSSOLAR program, 24, 43, 44, 50, 88,
107, 108

SSTEMP program, 88, 95, 101, 108

staff gage, ix, 18, 53

statistical methods, 37, 88-90, 92,
$3, 95, 99, 100, 102, 108, 112,
1

STEADY model, 114, 16, 78, 96-99
stochastic models, 96, 116, 117
STORET data base, viii, 55, 61
stratification, temperature, 65, 67,
70, 76, 77, 80-82, 84, 119
streambed, 14, 16, 19-22, 67, 79, 97
substrate, 12, 24, 97
summer, 12, 51, 69, 74-76, 96, 100
sun, vii, 8, 10, 21, 23, 26, 27, 32-
34, 36, 43-46, 52, 86, 103, 133
sunlight, 32-34, 45, 67
sunshine, 34, 35, 45, 118

theft, equipment, 63

thermal gradient, 8, 10, 19-21]

thermistor, 62, 84

thermocline, 80, 81

thermograph, vii, viii, 39, 62, 63,
64, 70, 121, 122

thermometer, 39, 41, 68-70, 84, 107,
115, 121, 122



are available from the Corps of Engineers Waterways Experiment Station,
Vicksburg, Mississippi (U.S. Corps of Engineers 1987).

In addition, it is worth noting that a model capable of simulating dynamic
flow situations can be used equally well to simulate steady flow events. An
excellent paper by Laenen and Hansen (1985) describes just such an application
of Jobson’s dynamic flow/temperature model. It is also an application noteworthy
for producing fair simulation results with a paucity of input data, as well as
an unusually good problem statement and description of objectives. It is
unfortunate that these dynamic flow studies typically cover only a few days time,
due largely to the quantity of input data required (i.e., expense). It has been
reported that complete dynamic-flow, dynamic-meteorology models are highly
accurate, but require equally complete input data sets (Jobson 1981). An example
of such a model is the Branched Lagrangian Transport Model (BLTM) developed by
Jobson (1987).

A comparative study of a variety of solutions for dynamic heat flux and
transport models may be found in Gosink (1986).

Statistical Models

We covered regression models earlier, primarily emphasizing the filling
of missing data values. These empirical models do not attempt to explain heat
flux or heat transport, but rather describe (1) the relationship between one or
more easily measured meteorological variables and water temperature or (2) the
relationship between water temperature and time of the year.

The first method has been tried with varying degrees of success. Water
temperatures generally mimic air temperatures, but with a delay. The delay is
termed a “phase shift." Several authors have studied this phenomena. Moore
(1967) found disappointing results in attempting to correlate air temperature

and discharge with water temperature in Oregon streams. Smith (1981) produced
acceptable results with standard errors of up to 2.65 °C (4.8 °F).

The second method is often called harmonic analysis. Such relationships
may be developed for any measurement of temperature: monthly, daily, maximum,
mean, etc. For example, several authors (Collins 1969; Steele 1978, 1983; Smith
1981) used a variation on the single model:

Tmax = a [ sin (bx +c) ] +T
where Tmax = maximum temperature for time x
a = amplitude, °C
b = constant, 0.0172 radians (0.987 degrees) per day
c¢ = phase angle, radians
x = Julian day

T = mean annual maximum temperature, °C

99



6S

Table 7c. Alternative forms in which you may find water temperature data in USGS publications or
databases: daily mean water temperatures.

PLATTE RIVER BASIN
0676400 SOUTH PLATTE RIVER AT JULESBURG, COLO.--Cont inued

EXTREMES. 1970-71
Water temperature: Maximum, 21.0°C aug. 22, 23; minimum, freezing point on many days during December to March.
Period of record.--Specific conductance: Maximum daily, 3,270 micromhos Jan. 12, 1971; minimum daily, 348 microhoms Aug. 15, 1968.
Water temperature (1946-49, 1950-71): Maximu, 34°C July 28, Aug. 1, 1953, July 7, 18, 1983; minimum, freezing point on many
days during winter period.

‘ont inued

REMARKS .--Samples for specific conductance and temperature collected from channe] no. 2 (06783990). For monthly chemical analyses
considered applicable to this site, see record for South Platte River near Julesburg, Colo. (sta. 06764200).

TEMPERATURE (“C) OF WATER, WATER YEAR OCTOBER 1970 TO SEPTEMBER 1971

oAY ocr Nov DEC AN FEB MAR APR may ua JUL AUG see
1 14.5 4.5 4.5 1.0 1.0 3.5 6.5 10.0 8.0 11.0 10.0 11.0
2 12.0 3.5 2.0 0.0 0.0 2.0 12.0 45 9.0 14.5 14.5 12.0
3 14.5 2.0 3.5 0.0 1.0 1.0 10.0 5.5 10.0 10.0 9.0 15.5
4 13.5 3.5 1.0 0.0 0.0 45 6.5 5.5 9.0 13.5 8.0 10.0
5 13.5 3.5 1.0 0.0 0.0 45 5.5 4.5 8.0 11.0 8.0 15.5
6 13.5 45 4.5 0.0 45 12.0 0 10.0 11.0 8.0 20.0
7 9.0 © 10.0 3.5 0.0 0-0 : : 6.5 12.0 6.5 14.5
8 4 5.5 2.0 0.0 0.0 14.5 45 9.0 8.0 8.0 5.5
9 . 45 35 1.0 8.0 15.5 45 8.0 10.0 8.0 1.0
10 45 1.0 4.0 8.0 11.0 45 8.0 9.0 14.5 5.5
uw 9.0 1.0 0.0 3.5 6.5 10.0 1.0 8.0 15.5 9.0 8.0
12 6.5 1.0 0.0 2.0 45 12.0 2.0 10.0 11.0 10.0 10.0
13 8.0 0.0 0.0 4.0 8.0 15.5 4.5 9.0 10.0 10.0 45
“ 45 0.0 0.0 5.5 2.0 11.0 45 8.0 10.0 18.0 45
15 2.0 2.0 0.0 5.5 3.5 18.0 45 5.5 13.5 9.0 45

(Continued)



thus be 22% of 300, or 66. Records indicate 75% possible sun; so 75% of the
value from 66 to 300 would be approximately 240 kilojoules/square meter/second.

FORT COLLINS TOTAL HEMISPHERIC
RADIATION

MJ / METER SQUARED / DAY
©

2 ——O— Measured value
Average clear day value

1 6 W 16 21 26 31
NOVEMBER 1988

Figure 20. Total hemispheric radiation taken at Fort Collins, CO, during
December 1988. Note the days in which radiation exceeds the clear day value.
From Colorado Climate Center (1988).

Percent Possible Sun

Percent possible sun is used in the SNTEMP models as a surrogate for cloud
cover. This measurement is taken at more weather stations than is solar
radiation, but it is probably subject to more error. Technically, percent
possible sun is measured as the number of minutes of direct sunlight divided by
the number of minutes possible for that latitude and time of year. Obvious
problems arise in determining the threshold of cloud cover at which the sun
"ceases to shine." The technical specification calls for a limit of 200
watts/square meter, but like the radiation measurements, dust, rain, snow, and
other factors literally "cloud" the instrument at times.

There is a relatively new instrument on the market called a Sunshine
Recorder, which costs about $1,100. It works by burning a trace on a chart that
is at the focus of a hemispherical lens.

When not measured by an instrument, percent possible sun is periodically
estimated by a weather observer. Estimates of cloud cover are likely to be

45



Temporal Error
@ 2s Upper Colorado River, Monthly Study b. ‘emporal Error

Te
Cache Le Poudre River, Dally Study

Error (C)

°
+05
a

‘Oct Nov Dec Jan Fob Mar Apr May Jun Jul Aug Sep
= Mean Error = Maximum Error dun 15 aT us, Aug) Aug 15,
| Mean Error — Regression Line

¢. Longitudinal Error d "Parameter" Error
ny Gunnison River, Br-Weekly Study . 2 Poudre River, Dally Study

2 .
1 ne
°

oa
EB
“3
4] © Meanérror + Probable Error “ts . .
6 -2
0 40" 80" 120" 160 " 200 40” 60 "120 160 200 240” 280 320360
KM Upstream from End Point Observed Solar Radiation (j/m2/sec)
t Relative Error
oe . Cache Le Poudre Riva, Dally Study
4 te] : .
08 1.04 . ve 4
06 . q
04 Zr02}, .
§ 024s a g 1 TM~
B-o2 4. 8008
a Seed * _ .
goad" oo . Boss
sos . Goss .
1 0.92:
“12 é .
“14 . 09
118 . . ons .
12 14 76 18 20 2 1s 16 18 20
Observed Mean Day Temperature (C) Observed Temperature (C)

Figure 34. A spectrum of perspectives on possible causes of model error.

{A) Temporal error showing nonrandom bias with month of the year. (B) Lack of
overa?l temporal error in a daily simulation. Regression indicates a mild,
but insignificant trend. However, there is a hint of a periodic (perhaps
weekly) trend. (C) Longitudinal error showing an increase in the model's bias
in an upstream direction. (D) Parameter-by-parameter error. In this case,
the model’s bias was weakly (R? = 0.1) associated with solar radiation.

{E) "Temperature" error plot to determine if the model’s bias is associated
with the absolute temperature observation (or prediction). (F) Relative error
(predicted/observed) is another way to assess a simulation’s quality.

94



Site n Site n+1

Ton

Concentration

Time

Te,

Figure 7. Schematic of dye concentration versus time showing dispersion

during time-of-travel study. The X-axis also may be interpreted as proceeding

downstream from left to right. Thus the magnitude of the concentration

becomes attenuated through time (and space). Travel time is measured from

peak soncentration to peak concentration. Reproduced from Hubbard et al.
1981).

SNTEMP works with either a constant Manning’s n or a constant travel time,
both of which are truly dynamic with changes in discharge. If large variations
are possible, high and low flow models should be constructed.

Thermal Gradient

The thermal gradient determines the rate of heat lost or gained from the
streambed to the water. The thermal gradient may be thought of as the reciprocal
of the more commonly known "r" value used in home insulation. The r value is

19



POUDRE RIVER WIDTH-FLOW RELATIONSHIP

English Computation

44

437) Width = age

42 = 20.42 Q0.21
44 Ree

LN (Width) (ft)

35 = Observed
34 — Predicted
33
32
aa

o 1 2 38 4 5 6 7
LN (Discharge) (cfs)

POUDRE RIVER WIDTH-FLOW RELATIONSHIP

Metric Computation
a2
St Width = aa?
3.0 = 13,24Q021
29 R? = 0.98

LN (Width) (m)

LN (Discharge) (cms)

Figure 5. Development of width versus flow relationships showing no effect of
units of measurement on slope, but definite effect on intercept.

15



The first challenge in such a situation is to model the routing of dynamic
flow. Such a model must accurately describe the changes in velocity throughout
the length of the segment (and thus travel time) that occur with cyclic flows.
Travel times varied from 23.31 to 7.7 hours in the 27.9 km Chattahoochee reach
modeled. This case was also complicated due to large interchanges in water from
the main channel into or out of several tributaries as the stage changed in the
main channel. Reverse flows were often observed duri ng rising stage conditions.
They found good correlation between measured and predicted stage after
implementing a routine to vary several subreach roughness values linearly as a
function of stage, though data collection requirements were extensive. In
addition, they found that the roughness values seemed to vary with time at
certain locations.

Modeling of temperature was accomplished next using input data much the
same as any other physical-process-oriented model. The only difference was in
the form of the heat transport equation and the time-step involved. Signifi-
cantly more complicated descriptions of the solar radiation {to account for
hourly changes in stream shading) were necessary. Dynamic temperature
predictions were good (RMS errors of 0.32 °C and 0.20 °C, 0.6 °F and 0.4 “F)
for the two months Studied). They demonstrated that hydropeaking could increase
the river temperature immediately below the dam by as much as 3 °C (5.4 °F)
almost instantaneously, due to the greater release of epilimnionic water at high
flows. Other water quality attributes, such as dissolved oxygen), were expected
to change dramatically also, but they did not measure this.

Twenty-eight kilometers downstream, these same effects were attenuated
and to some extent reversed. That is, large flushing flows were capable of
reducing stream temperatures instead of raising them prior to the arrival of
the warmer epilimnionic waters. The interpretation of the thermal effects of
hydropeaking are complicated because they depend on the time of day and prior
release schedules.°

That these same conclusions can be reached more simply through observation
rather than dynamic modeling has been shown by Waddle (1987). Waddle limited
the view to a specific time of year, as did Jobson, but was able to discern the
general pattern of timing lags for temperature releases from a shallow reservoir
at several points downstream and make limited recommendations for the timi ng and
magnitude of flow releases necessary to mitigate consequences on a downstream
fish hatchery. Once again, much additional data must be collected to quantita-
tively support the conclusions.

CE-QUAL-RIV1 is a model specializing in time-varying, highly unsteady flow
and water quality assessment. Data input requirements are more extensive than
steady-state counterparts. Both models, user’s manuals, and technical assistance

Se

®It is also interesting to note that what was described as a "light rain"

apparently resulted in an increase of 1.2 °C (2.2 °F) during a period of
otherwise steady flow.

98



shade are merely added to get the total shade. However, one should think of
topographic shade as always being dominant in the sense that topography always
intercepts radiation first, then the vegetation intercepts what is left
(Figure 10).

To use the preferred method of calculating shade, one must estimate or
Measure latitude, stream azimuth, topographic altitudes, and riparian shade
Parameters. Latitude, of course, may be taken off of a standard USGS topographic
map. The other parameters require more explanation and will be detailed below.
In addition, keep in mind that we are talking about “averages” for a stream
segment in measuring what often is a very irregular or patchy variable. Here
are some tips on how to make these measurements.

to due south, and controls which sides are called east and west, by convention.
Measure towards the west for positive degrees and towards the east for negative,
regardless of the direction of flow. Refer to the following figures for
guidance: for example, if the stream were flowing in a generally northwest-
southeast direction as in parts e or f of Figure 4, the azimuth would be
approximately -45 degrees.

Once the azimuth is determined, usually from a topographic map, the east
and west sides are fixed by convention (Figure 11). When the angle is small,
east and west are obvious. As the angle approaches plus or minus 90 degrees,
it is less clear. Imagine an azimuth of Plus 90 degrees; the actual north shore
is termed west. Similarly, if the azimuth is -90 degrees, the north shore is
termed east as far as this Program is concerned. This convention is easy to
understand if you visualize varying the azimuth from zero degrees and note that
west and east always stay on the same side of the stream.

The remaining parameters may all be estimated, with correspondingly more
accuracy for direct field measurements. Random samples would undoubtedly provide
the most accurate and robust figures. Reifsnyder and Lull] (1965) recommend 20
to 40 samples in bright sun, fewer if cloudy. See Platts et al. (1987) for
information about designing a random sampling procedure.

The topographic altitude is a measure of the average line-of-sight angle
to the horizon from approximately the middle of the stream, measured in degrees.
Both east and west sides will require altitude measurements. The altitude may
be measured precisely with a clinometer or fairly accurately with a protractor
(Figure 12). If a clinometer is used, you will find that one with a degree scale
is preferable. Topographic maps may be used with caution, but in mountainous
terrain, it will be difficult to estimate topographic altitudes from a topo map,
as it becomes virtually impossible to tell where the horizon, as seen from the
stream, actually is. In flatter country, the stream bank itself may be the
overriding topographic horizon.

The vegetation height (Vh in Figure 13) is the average height for the

existing or proposed shade-producing strata of vegetation along the stream from
the water’s surface. Note that this should not be the height of the vegetation

27



Bartholow, J.M. [1988a.] Stream segment shade model (SSSHADE). Temperature
model technical note no. 3. Computer program and documentation for IBM-PC.
U.S. Fish and Wildlife Service, Fort Collins, CO. 8 pp. Unpubl. MS.

Bartholow, J.M. [1988b.] Stream segment solar model (SSSOLAR). Temperature
model technical note no. 4. Computer program and documentation for IBM-PC.
U.S. Fish and Wildlife Service, Fort Collins, CO. 8 pp. Unpubl. MS.

Bartholow, J.M. [1988c.} Stream segment temperature model (SSTEMP) .
Temperature model technical note no. 2. Computer program and documentation
for IBM-PC. U.S. Fish and Wildlife Service, Fort Collins, CO. 12 pp.
Unpub]. MS

Bartholow, J.M., and T.J. Waddle. 1986. Introduction to stream network habitat
analysis. Instream Flow Information Paper 22. U.S. Fish Wild]. Serv. Biol.
Rep. 86(8). 242 pp.

Barton, D.R., and W.D. Taylor. 1985. Dimensions of riparian buffer strips
required to maintain trout habitat in southern Ontario streams. North Am.
Jd. Fish. Manage. 5:364-378.

Benham, D.G., and 0.G. George. 1981. A portable system for measuring water
temperature, conductivity, dissolved oxygen, light attenuation and depth of
sampling. Freshwater Biol. 11:459-471,

Berger, J.0., and D.A. Berry. 1988. Statistical analysis and the illusion of
objectivity. Am. Sci. 76:159-165.

Beschta, R.L., and J. Weatherred. 1984, TEMP-84, a computer model for
Predicting stream temperatures resulting from the management of streamside
vegetation. USDA Watershed Systems Development Group. WSDG Report WSDG-
AD-00009. 76 pp.

Beschta, R.L., and R,L. Taylor. 1988. Stream temperature increases and land
use in a forested Oregon watershed. Water Resour. Bull. 24(1):19-25,

Bilby, R.€. 1984. Characteristics and frequency of cool-water areas in a
western Washington stream. J. Freshwater Ecol. 2(6):593-602.

Blackadar, A.K. 1983. Using home computers to study the weather. Weatherwise
36(4):193-195.

Blackadar, A.K. 1984. A computer almanac. Weatherwise 37(5):257-260.

Blackburn, T. 1983. A practical method of correcting monthly average
temperature biases resulting from differing times of observation. J. Clim.
App. Meteorol. 22(2):328-330.

Boning, C.W. 1974. Generalization of stream travel rates and dispersion

characteristics from time-of-travel measurements. J. Res. U.S. Geol. Surv.
2(4) 2495-499,

108



Table D-2. Textual output file from Climatedata (tm U.S. West Corporation)
for maximum daily air temperature (°C or °F). Minimum daily air temperature
is usually also available.

Station GORE PASS RANCH Parameter TMax
County JEFFERSON Record Cat 7

State 0 Coverage % 83

Id Miss ing/M g/ 0
Latitude Elevation 7600.00 Begin Date & (1987
Longitude Obser Time 16000 hrs End Bate 12 /1963

Daily Maximum Temperature, in degrees Centigrade

1 9 2 1s 7 24 29 2a 20 24 8 7

2 6 3 10 v7 19 29 24 22 a R 3

3 g a4 5 20 20 27 2 a 23 w 2

4 4 0 12 22 24 26 20 -- 24 cc 3

5 8 ~2 12 22 19 a7 22 7 23 & 5

& 8 i 16 23 23 30 23 aa ai 13 3

7 6 9 18 24 24 30 26 oe at 13 >

8 i 3 14 23 23 29 24 ral 23 10 ao

g 6 6 14 19 16 22 _ 24 21 it 3
10 Q 5 iL 22 2i ai -- - 23 14 wk
iW 5 3 2 22 23 23 24 “ 22 “ “6
12 nh 1 v7 V7 25 ai 23 = 2h = 7
i3 “tL 0 19 19 28 26 26 26 7 il 5
4 “13 1 5 20 20 28 27 a 19 18 16 ol
15 “4 2 6 16 a. 23 26 28 23 19 14 v1
16 -2 i o g 19 16 29 23 23 2i es “4
v7 2 4 6 4 ral 19 29 v7 23 2i “i “4
igs “14 3 4 10 ai 16 3h 25 26 al Q i
1g “6 ? 4 5 Vv 22 BW er 25 18 2 aie
2 -- 7 8 10 20 25 32 23 rei 15 3 ied
2i 6 5 g 14 oo 28 29 19 a 16 3 -
22 o 4 1 i 22 26 24 23 23 18 2 -
23 L 6 4 10 18 2? 30 is a4 16 3 oa
24 4 g 2 i 18 26 3 26 22 12 2 4
25 -1 4 g 16 18 26 31 26 24 18 5 &
26 ~6 8 ed 18 19 a 7 24 7 ig 4 L
27 -6 i 16 v -- = a7 25 -- 18 + 9
28 1 2 13 16 - 28 2g 2 -- 18 -- --
2 Q oe u 14 rat a7 29 24 oa 18 oo o1
30 5 - 16 Vv a4 28 31 24 aad v 4 -3
31 7 io v aad i “ = ai “= 12 iid aad

129


Table 5. Percent reflectivity for various homogeneous ground cover condi-
tions,

Setting Percent

Meadows and fields 12 to 30
Leaf and needle forest 5 to 20
Dark, extended mixed forest 4to 5
Heath 10

Flat ground, grass covered 15 to 33
Flat ground, rock 12 to 15
Light cultivated soil 15 to 30
Dark cultivated soil 7 to 10
Sand 10 to 20
Sandy soil 15 to 40
Light sand dunes, surf 30 to 60
Vegetation, early summer 19

Vegetation, Tate summer 29

Fresh snow 80 to 90
Old snow 60 to 80
Melting snow 40 to 60
Clean glacier granular snow 50 to 65
Dirty glacier granular snow 20 to 50
Ice 40 to 50
Clean glacier ice 30 to 46
Dirty glacier ice 20 to 30
Water, lakes 5 to 15
Water, sea 3 to 10
Densely urban areas 15 to 25

Day Length

Day length is such a well-known measurement that it hardly bears
mentioning. The SNTEMP programs will calculate day length automatically, or
Table 6 may be used for a good estimate.

HYDROLOGY COMPONENTS

I will only touch on collecting hydrologic data, since this subject is
covered adequately in other available publications (Bovee and Milhous 1978;
Bovee 1982; Hamilton and Bergersen 1984). This is not to minimize the need for
accurate flow measurements, both surface and groundwater; though not typically
the most significant variables, they can play a major role in affecting stream
temperatures.

51



The latter approach can then be used to answer the first question; namely,
what is the probability of exceeding a certain water temperature? To do so, work
your way along the air temperature exceedence curve, computing the associated
relative humidities, holding the discharge constant, or for a projected release,
and computing the water temperature by using your model. for example, suppose
you find that the 7% exceedence air temperature (air temperatures this high or
higher are found only 7% of the time) produces the threshold water temperature.
Then it is reasonable to believe that the water temperatures themselves wil] be
exceeded 7% of the time.

Note that this approach risks overlooking some combinations of meteorol ogi-
cal parameters that could produce more extreme water temperatures. It also
ignores the cross-correlation between the meteorological parameters for which
we used averages. However, I feel that this heuristic approach is satisfactory
in the face of the more detailed and costly simulation of long time-periods or
Monte Carlo approaches.

The best example of investigating temperature recurrence intervals, of
which I am aware, may be found in Moore’s (1967) analysis of Oregon streams.
He developed a rating scale, from one to six, to characterize the maximum July
and August water temperatures based on the deviation of air temperature from
normal for that time of year. For example, a scale value of one was given to
a month when its air temperature departure from the long-term average for that
month was greater than -4 °F; a two for -2 to -4 °F; a three for -2 to 0 °F; a
four for 0 to +2 °F; a five for +2 to +4 °F; and a six for greater than +4 °F,
These determinations were subject to slight modification on the basis of that
month’s discharge being above or below average. Moore felt that this scale
could be used to deduce the recurrence interval for peak water temperatures
based almost solely on air temperature. Other, more rigorous, analysis
techniques substantiated this rudimentary approach, except in spring-fed streams.

A related issue involves not the probability of exceeding a specified
temperature threshold for a single occurrence (day), but rather the probability
of exceeding a threshold for a sequence of days. This has been done for
rainfall, but to my knowledge, has not been done for maximum daily temperatures,
either air or water. It has often been noted that "the longer the [dry] spet]
has lasted, the more likely it is to last another day” (Williams 1952). The
ambitious practitioner is referred to a paper by Weiss (1964) that outlines an
approach to developing a Markov-chain probability model to compute the cumulative
probability of a sequence of events with a specified duration. One application,
though actually related to toxic chemicals, that looks at the interrelated
duration probabilities of river flows, chemical discharges, and toxic kinetics
may be found in Hamelink (1979). We will need to be able to do these kinds of
persistence analyses to proceed with more rigorous biological assessments and
predictions. Some tools are being developed that may support forecasting of this
sort. Though preliminary in nature, the Agricultural Research Service is
developing a computer program called CLIMATE to predict, on a site-specific
basis, the weather sequences of Precipitation, maximum and minimum air
temperatures, and solar radiation (D.A. Woolhiser, U.S. Department of Agricul-
ture, Agricultural Research Service, pers. comm. ).

105



better to carefully heat the bulb to totally fill the capillary tube and then
allow gradual cooling. For special problems in using these thermometers in
arctic conditions see Osterkamp (1979).

The frequency of calibration will vary with the instruments used and must
be determined by the experience of the operators. Stevens et al. (1975) outlined
the methods for instream and air temperature measurement and calibration. His
method is reported here with minor modifications to be slightly more contemporary
(technology continues to improve these devices).

1.

Measure air temperature in the shade using a dry thermometer to
minimize the risk of obtaining an erroneously low air-temperature
reading due to evaporation.

Select a site in the stream where the water is moving and where the
influence of tributaries is diminished because of mixing. Studies
have shown that a temperature taken in the main flow of the stream
is usually representative of the entire water mass. During the
summer, when discharges are low, it may be necessary to wade into
the center of the stream, or as far as possible in deep streams, to
obtain the temperature. If sufficient mixing has not occurred,
temperature observations must be obtained at several locations so
that a discharge-weighted mean temperature can be computed.

Stand so that a shadow is cast upon the site chosen for collecting
the temperature.

Make certain the liquid column in the thermometer is not separated.
Hold the thermometer by its top, and totally immerse it in the water
in the shadow area. Position the thermometer so that the scale can
be read, and hold the thermometer in the water until the liquid
column no longer moves (no less than 60 seconds).

Without removing the thermometer from the water (to avoid wet-bulb
cooling), read the temperature to the nearest 0.1 °C (0.18 ‘F), and
record it in the field notes. [Temperatures measured by the
calibration thermometer should be read in a fashion to eliminate
parallax (ASTM 1983a).] If the water is too rough or too turbid to
allow a reading in the stream, the temperature may be taken by
filling a container with the water, immersing the thermometer in
the container, and then reading the temperature. The container must.
be large enough to allow total immersion of the thermometer, and the
walls of the container must be brought to the same temperature as
the stream before it is filled with water for temperature
determination. In addition, it must provide sufficient thermal mass
to insure that the temperature of the water in the container does
not change while the temperature is being recorded. A volume of at
least a pint should be withdrawn for temperature measurement.

The observed water temperature is considered to be the true stream
temperature and will be designated as TST. The next step is to
repeat the above procedure (steps 3 through 5) in the water near the

69



effects of solar radiation are evident in the removal of night-forming anchor
ice during the day (Ficke and Ficke 1977).

Comer and Grenney (1977) criticized many previous modeling efforts for
not including heat transfer at the streambed. They concluded that diel variation
of heat flux at the water-ground interface may approximately equal that at the
air-water interface at night in clear, small mountain streams. Part of this heat
balance was attributed to solar radiation reaching the streambed. In addition,
they presented data on the magnitude of the diurnal variation of ground
temperature at different depths and times of the day collected with a unique
temperature probe. Brown (1969) also noted that conduction into the streambed
was important in shallow streams having a bedrock bottom, but stated that gravel
bottoms appeared to be insignificant as energy sinks. The color of the rocks
have been cited as an influence here also (Currier and Hughes 1980). My
conclusion from all of this is that hourly temperature simulations should pay
attention to the thermal gradient and absorptive properties of the streambed,
but daily simulations can safely ignore these elements. Daily simulations,
however, should recognize that seasonal variation of ground temperatures may be
a source of error if the models do not account for such change. See the section
on ground temperature below.

Stream Shade

As shown in Table 2 and Figures 3 and 4, water temperature can be very
sensitive to stream shading, especially for low flow, high width streams in
midsummer. Shade, as considered here, comes in two forms, riparian vegetative
shade and topographic shade from valley walls, cliffs, and even streambanks.
Both forms result in the interception of the daily solar radiation from the
water’s surface. Though instream shade caused by fallen logs and brush often
should be included, in practice it rarely is.

Shading affects stream temperatures in three primary ways. First, it
screens the water’s surface from the direct rays of the sun. Solar radiation
May account for over 95% of the heat input during the midday period during
midsummer (Brown 1970). Thus, it is one of the dominant factors affecting
maximum daily water temperature, often more so than air temperature. Second,
shade reduces the amount of the water’s back radiation at night, tending to
moderate the minimum stream temperatures. Third, shade produces its own Jong
wave (thermal) radiation, which also tends to raise minimum temperatures at
night. However, results of shade removal on minimum stream temperatures have
not always been as expected, nor are they typically the same magnitude as changes
in maximum temperature (Figure 8).

There are, of course, other direct and indirect effects of shade, or lack
thereof, on the physical and chemical nature of streams. Shade removal allows
increased light, which may result in increased algal production (Burton and
Likens 1973), and also may influence migration or other movement activity, even
due to bright moonlight (Lynch et al. 1984), Vegetative alteration also has the
attendant problems of streambank stability and sedimentation. Coupled with peak
flow events, streamside timber management practices may lead to long-term,
cumulative temperature effects (Beschta and Taylor 1988). Shade-producing
vegetation is closely related to the amount of instream cover produced by fallen

rat



handle outliers that may have resulted from grab-sample data collection methods,
lack of quality control, or malfunctioning equipment. As with any regression
approaches, there cannot be any change to the system. If a reservoir has been
constructed during the period of record used to fill or smooth data, for example,
two regressions would need to be developed, one to describe the predam conditions
and one for the postdam conditions.

There are also other methods for estimating missing water temperature
values. One promising methodology is the use of “harmonic” analysis. Though
there are several variations on this theme, they all work by fitting one or more
sine waves to the known data. One technique that shows promise for predicting
daily water temperatures from temperatures taken on a less than daily time step
(periodic or irregular) is contained in a paper by Gilroy and Steele (1972); also
see Ward (1963). The method used need not be constrained to any Particular time
step. The basic form of the sine wave relationship is:

Ty - M+ A sin(bj + C) + e,
where Ty = the stream temperature on julian day j (°C)
M = the overall mean temperature for the time period of interest (°C)
A = the amplitude of the sine wave (°C)
C = the phase angle (degrees)

b= Shey fundamental period of the sine wave; equal to (2 pi)/365 (or
3.

&y = the random error term (°C)
You may use this relationship and determine the best fit coefficients M, A, and

C through standard least squares (or other) regression analysis. To do so,
however, the formulation must be changed to:

ty = M+ Al sin(bj) + A2 cos(bj)
where the parameters from above are then:

C = arctan(A2/Al)
A = Al/cos(C)

For simplicity, however, just leave the equation in the second form containing
both the sin and cos functions.

It may often be profitable to use these formulations in conjunction with
one another. For example, it is usually easy to obtain at least air temperature
data near the stream of interest; flow data must be obtained or estimated. All
other meteorological data may then be lumped, in effect, into the sine wave
equation. Thus, the formulation would become:

Ty; = AO + Al Tay + A2 1n(Q5) + A3 sin(bj) + A4 cos(bj)
87



CURRENT "TEMPERATURE-LIMITED" TROUT HABITAT

Figure la. Approximate "temperature-1imited" geographic range of rainbow trout (Oncorhynchus
mykiss, formerly Salmo gairdneri) under three scenarios: current conditions. Range defined by
upper limit of 23.8 °C (75 °F) mean monthly surface water temperature. Figures derived from
Hydroscience (1971).



sensor. This is not the sensor temperature but the temperature of
the water mass surrounding the sensor, and it wil? be designated as
TNS (temperature near sensor). After recording this temperature
the observer should check the thermograph recorder and note the
indicated temperature. The recorder temperature is designated as
TRC. The three temperatures should all be recorded in the field
notes and also on the temperature chart (if there is one), along with
the date and time. Differences between TST and TNS will generally
be diurnal or seasonal in nature. The recorder should, hence, be
set to read TNS, and corrections should be made during the analysis
of the record to account for differences between TNS and TST. This
recording procedure will provide a clear record of Problems at a
given site, and it permits the recording of accurate temperatures
at the higher flows, when TNS is likely to be representative of the
true stream temperature (TST). Usually, changes in a recorder
setting of less than 0.5 °C (0.9 °F) should not be made unless the
apparent error is verified by two or more field inspections.

After the observer has obtained and recorded the three reference
temperatures, she should check and correct, if necessary, the
recorder-chart time and the zero and span settings. Sensors should
be checked, cleaned, and replaced if necessary. Sensor-recorder
measuring systems should be recalibrated at least twice each year
(more often if problems are observed).

Groundwater Temperature

Many streams receive substantial Portions of their flow for all or part
of a year from groundwater. Obviously, it is important to have a good estimate
on the temperature of this advective thermal contribution. In many cases, the
diurnal temperature fluctuation may be almost completely damped out in spring-
fed streams, especially in large or heavily shaded streams (Moore 1967).
Localized influx of cool groundwater may account for temperature reductions of
ts. °C (7.2 to 9 °F) over a distance of 300 m in small streams (Smith and Lavis
975).

Stevens et al. (1975) recommends Using a maximum-minimum thermometer for
measuring ground-water temperatures at a reconnaissance level. This seems quite
practical, since in most cases groundwater would not be expected to fluctuate
in temperature very much. Other measures would need to be taken should variation
be observed. These temperatures may be taken in unused wells, pumping
(irrigation) wells, discharging springs, mines, or holes bored in the streambank.
Wells present special problems if there is much stratification.

In the absence of on-site groundwater temperature measurements, Theurer
(1984) and Currier and Hughes (1980) recommend the use of mean annual air
temperature. Moore (1967) confirms this approximation with the exception of
thermal areas.

There seems to be an unfortunate lack of information about the temperature
of irrigation return flow. One source (Sylvester 1963) reports the following

70



corresponding changes in discharge were not mentioned. Speaking of springs, Webb
and Walling (1988) reported substantial increases in ground-water discharge
downstream from a newly constructed reservoir in England. This springflow tended
to moderate the temperatures of the release water, warming the winter releases
and cooling the summer releases from this largely unstratified impoundment.

Experiments I conducted (Bartholow 1985) have shown that nonstratified,
run-of-the-river impoundments may actually decrease the maximum temperatures if
the increase in depth is not offset by an increase in width, such as would be
the case in a confined canyon area.

Many if not most applications of a stream temperature model involve
reservoir construction or altered release evaluations. If it is not expected
that changes in flow release patterns will themselves influence the release
temperature, then the analysis is greatly simplified. However, if the flow
alterations may be expected to change the release temperature, then those new
release temperatures must be estimated or predicted based on some sort of a
model of response. New construction can always be expected to modify the thermal
regime.

As with stream temperature modeling, there are a variety of techniques that
one could draw on for help. Perhaps the simplest is for the case in which a new
reservoir is to be constructed, if there are similar impoundments in the region
of development. Similarity may be evaluated on the basis of such parameters as
elevation, depth, and fetch (the longest horizontal distance exposed to
prevailing winds). Empirical equations have been developed (Shuter et al. 1983)
that attempt to predict annual surface water temperature cycles during ice-free
periods based on parameters such as these for stratified and nonstratified lakes.
Unfortunately, I found their harmonic analysis difficult to interpret.

Other techniques relate air temperatures and surface water temperatures
(Sette 1940; McCombie 1959). Different time periods are necessary for good
fits. Dividing the year into two periods, one in which water temperatures are
rising and one in which they are falling, improves the correlations. If enough
data is available, a correlation for each month is in order. If surface
temperatures are available, one could argue that they should be used directly.
The correlation with air temperature, however, may be useful in frequency
analysis when air temperature data is more readily available. In any case, these
calibrations require that data be available for representative reservoirs,
usually nearby.

The literature contains examples of methods to contro] temperatures
downstream from reservoirs without a multilevel release facility by varying
gross release schedules for different seasons of the year (Wunderlich and Shiao
1984). This method utilizes a "cold water index," which is the ratio of the sum
of reservoir releases within a planning period to the cold water volume in the
reservoir at the start of the period. The index is reservoir specific and can
be used in operational-constraint type models when multiple objectives, such as
power generation and flood control, are involved.

76



FIGURES (Concluded)

Number Page

25 In situ and remotely configured thermographs ..
26 Priorities for installation and collection of wi

monitoring locations for various stream network configurations 66
27 Cross-sectional temperatures showing degree of temperature

variation for a given transect at a given flow ................ 68
28 The different types of error that can arise in the measurement

of temperature
29 Temperature versus distance versus time display .
30 Common areas in which to expect temperature- created microhabitat 80

31 Temperature profiles within a pool at various flows . 82
32 Comparison of water temperatures in pools and upstream 83
33 Full "split set" calibration/validation scheme 91
34 A spectrum of perspectives on possible causes of model error 94

35 Duration plot for 30 years of daily air temperature from
June 15 to August 15 ...... cee cece eee eee e eee e eee eeeseerenees 104
36 Duration plot for 30 years of mean daily air temperature from

June 15 to August 15 104
E-1 Normal daily average temperature for July 132
E-2 Mean relative humidity for July ........ 133
E-3 Mean percentage of possible sun for July . 133
E-4 Mean daily solar radiation for July .. 134
E-5 Mean wind speed for July 134

vii



Temperature Duration For Poudre River
30 Years of Daily Data

Temperature (C)
we
8

t) 20 40 60 80 100
Percent Exceedence

Figure 35. Duration plot for 30 years of daily air temperature from June 15
to August 15. Thin lines show the maximum and minimum temperatures, the thick
line traces the mean air temperature. Each duration curve has been computed
separately, i.e., any given mean temperature is not associated with its

corresponding maximum and minimum temperatures. Curves are jagged due to the
resolution of the original data.

Temperature Duration For Poudre River
40 30 Years of Daily Data

35
30
25
20
15
10

Temperature (C)

of

20 40 60 80 100
Percent Exceedence

Figure 36. Duration plot for 30 years of mean daily air temperature from
June 15 to August 15. The thick line traces the mean air temperature exceed-
ance curve. The thin lines show the maximum and minimum temperatures given
the corresponding mean. In other words, a variety of maximum and minimum
conditions may each produce the same mean temperature. Curves are jagged due
to the resolution of the original data.

104



improve one’s faith in this method, which is similar to that Proposed by Nobel
and Jackman (1980).

SPECIAL CASES
nditions

It is to be expected that temperature models such as SNTEMP will perform
poorly at water temperatures near freezing. The thermal properties of liquid
water alter below about 4 °C (7.2 °F). Water temperature variation is
profoundly suppressed in the vicinity of freezing due to the latent heat of
fusion which, in effect, means that the variation in temperature becomes more
strongly influenced by the temperature of the water than by the air temperature
(Song and Leung 1978). In addition, any degree of ice cover significantly
affects heat flux at the water-air interface.

There are many questions one would like to ask of water temperature models
in such cold conditions. For example, what is the length of open water below
a controlled-release reservoir? This may be important in estimating crowding
conditions for fish and waterfowl] (and concomitant waterfowl disease rates) or
determining ice passage conditions for terrestrial wildlife (Gosink 1986). In
addition, an estimate of when ice conditions may occur could provide better
stream discharge estimates because they indicate when altered stage-discharge
relationships should be used (Moore 1967).

There are many research opportunities in the area of river ice related to
how differing hydrodynamic and meteorologic conditions aid or hinder the
formation of various rates and kinds of ice. Until More questions are answered,
one may refer to an extensive bibliography compiled by Ficke and Ficke (1977),
which includes sections on the freezing process, climatic effects and predic-
tion, regulation and control, melting and breakup, ice jams, flow under ice, and
ice interactions with structures. Other sources of help are the Arctic
Environmental Information Data Center in Anchorage, Alaska, and the Army Corps
of Engineers Cold Regions Institute in Hanover, New Hampshire.

It is clear that cold water and ice affect the mortality as well as the
behavioral and microhabitat preferences of many aquatic organisms. However,
studies of the effects of winter conditions in streams have been largely
neglected (Needham and Jones 1959). Fish will select microhabitat sites to
minimize the danger of freezing, even though it is rare that actual freezing
will occur, since the freezing point of freshwater fish is usually in the range
of -0.50 to -0.65 *C (31.14 to 30.87 °F) (Devries 1971).

Reservoirs

Cowx et al. (1987) observed temperatures downstream from a reservoir all
year long. As expected, summer temperatures were uniformly depressed, while
winter temperatures were somewhat elevated. The daily temperature variations
were also dampened. The interesting thing was that the time of year when
absolute maximum temperatures were reached depended more on the reservoir
release schedule than on ambient meteorological conditions. In addition, their

74



Results of these investigations are predictable: (1) the water temperature
data is usually assumed to be normally distributed; (2) the models invariably
explain about 85% of the variance by using only the first harmonic; (3) standard
deviations increase in the summer, peaking about the time of summer solstice,
and decrease in the winter near the time of winter solstice; (4) limited data
sets provide about the same statistical "explanatory" power as complete data
sets, thus some data collection efforts may be reduced; and (5) care must be
taken if periods of frozen water are included.

Though statistical models may be useful in general stream characterization
studies, determining suitability for aquatic life (evaluating success for a
potential introduction or predicting presence/absence of a species), they are
generally of limited utility in determining the incremental impact on tempera-
tures due to any change in the water system (e.g., flows, shade, reservoir, or
construction) because any change in the stream geometry or hydrology (or
meteorology) cannot be a part of the model. At best, they can identify that some
changes have occurred in a basin (Steele 1983}. But once man or nature changes
the environment of the stream, the purely empirical models no longer describe
the results. In addition, even if one does apply such a model, it is only
representative of the single geographic Tocation for which it was built; no
translations are possible. For this reason Theurer’s confined use for these
statistical models to data filling and smoothing is very appealing.

The ambitious reader may be interested in extensions to these models:
some append Monte Carlo routines to deal with the remaining 15% of the variance
that the single harmonic models do not explain (Song and Chien 1977); some
attempt to offer greater “predictive” ability using Kalman Filters (Chiu and Isu
1978); and some are interested in trend analysis, given the statistical problems
of nonnormal distributions, missing values, and serial correlation (Hirsch et
al. 1982). This last reference quotes another source (p. 117) for something we
need to be cognizant of

No . . . obvious indication advises the experimenter that a
parametric assumption has been violated. Of course he may apply
time-consuming tests for normality or homogeneity to the obtained
data, but such tests are rather unsatisfactory. They are unlikely
to detect any but the most extreme violations when samples are
small, and they are almost certain to detect the most trivially
slight violations when samples are large.

In summary, the purely statistical models lend themselves well to
temperature prediction when the stream geometry and hydrologic conditions are
not expected to change dramatically and long periods of record are available.
Their development time is relatively rapid. The process-ortented models are
better suited to exploration of system changes and alternative water management
solutions, but at the cost of more intensive data collection, data entry, and

manual calibration, See Marceau et al. (1986) for a more detailed discussion
of these issues.

100


SIMULATION AND ANALYSIS TECHNIQUES

ALTERNATIVE PREDICTIVE TECHNIQUES

In any given study, there may be reasons to choose among alternative
temperature models due to the objectives of the investigation as well as the
constraints involved. Among those alternatives are (1) monitor temperature
response to a range of flow and meteorological conditions, (2) use regression-
type models, (3) use process-oriented models, or (4) use some combination of
the above.

rement

It is always worth mentioning that certain temperature management problems
may lend themselves to measurement only. Existing data may be sufficient in the
simplest cases to demonstrate that there is or is not a temperature problem.
In more complicated cases for which the opportunity to experiment exists
(alternative flows for example), reliable temperature estimations may be obtained
from interpolation or limited extrapolation. Thus, explicitly avoiding formal
modeling is always an alternative. If sufficient flexibility exists in water
management that flows may be manipulated through the range of feasible
alternatives, and the variability in meteorological conditions is expected to
be fully realized, a carefully executed temperature monitoring study should be
possible. (Note that a special case may be represented by relying on existing
historical water temperature data.) The system's response could then be
carefully interpolated from the measured data.

The dangers of such an approach, however, should be apparent in the above
wording. This assumes that it is only flow, or possibly reservoir release
temperature also, that are the management alternatives. Care should be taken
to not overly constrain the range of alternatives evaluated. Though it may be
reasonable to extrapolate above the range of measured flows, it would not be wise
to extrapolate below that range because the rate at which water approaches
equilibrium is highly nonlinear at low flows. It is hot often that the full
range of meteorological conditions can be expected to occur. An analysis of the
recurrence interval of daily maximum air temperatures, for example, may
illustrate the expectancy of such Conditions. Because these opportunities are
felt to be limited, because changes in shade or stream geometry cannot be easily
manipulated, and because the range of meteorological conditions may be
incomplete, other techniques will often need to be employed.

Regression Models

Regression-type models are very attractive in their simplicity and
understandability. Data requirements are often minimal, and the relative
sensitivity of parameters can be easily derived. Do not, however, fall prey to
reducing the number of terms to just water temperature and flow. Always examine
other terms (see below). The models as outlined by Theurer (1984) are far more

85



Discharge

The discharge for many rivers may be obtained through the U.S. Geological
Survey, which maintains a network of gaging stations throughout the United
States. Normally, gages are located on the larger streams and rivers and may
not be available in smaller watersheds. Typically, discharge measurements wil]
reflect mean daily or mean monthly flows; further manipulations would be required
for any other time-step. Other sources for discharge measurements include the
HYDRODATA database (see Appendix D), Soil Conservation Service, NAWDEX, water
conservation or irrigation districts, national parks and forests, utilities, and
some city water departments. The HYDRODATA source may also include reservoir
stage and reservoir storage, though these are much less frequently found.

Users of SNTEMP have advised that studies in which either reservoirs or
powerhouses are involved will usually necessitate the evaluation or reduction
of historical operations records, as well as the identification of possible
future operations. This can result in a significant expenditure of time and
manpower, usually complicated by incomplete and inconsistent data formats (Dave
Gilbert, Pacific Gas and Electric, pers. comm. ).

If historical discharge measurements are not available, some form of field
measurements must be made. Since discharge is greatly influenced by the
Characteristics of the channel, it is advisable to carefully select transects
for homogeneity. Be sure to establish a staff gage in accordance with
recommended procedures (Bovee and Milhous 1978) if a temperature study is coupled
with a PHABSIM analysis. Taking enough discharge measurements will allow
correlation with discharges in well-measured watersheds. Thus, the historica?
record may be extended back in time (Bovee 1982). Consult an experienced
hydrologist if in doubt.

Most network-type temperature models will require that flow data be
supplied for internal geographic locations for which you have no measurements.
In cases for which you can assume no changes in groundwater or tributary inflow,
or a stream that is losing flow, the discharge for an arbitrary point between
two other points of known discharge is:

- - (Dx - Dt)
Ox = Ql + (Q2 - Ql) * (D2-- D1)
where Qx = unknown discharge at point x, between I and 2
Ql = known discharge at location 1
Q2 = known discharge at location 2
Dx = distance measurement at location x
01 = distance measurement at location 1

D2 = distance measurement at location 2

53



6b

DEPTH (FT)

FORT COLLINS SOIL TEMPERATURES (°F)
MAIN CAMPUS, AVERAGE OF 7 A.M. AND 7 P.M.
VALUES, 1977-1984

MAY NOV CS
0 0
==? <i CTY
5

[—— 35 70 \}e
24

44 N 4

6+ 6

| Nw \ \ ;

10 : ; : : 10

JAN FEB OCT NOV DEC

MONTHS

Figure 23. Annual variation of ground temperatures at Fort Collins, CO. Adapted from Colorado
Climate Center, colorado State University.



SENSITIVITY OF MEAN DAILY WATER TEMPERATURE Hl

AIR TEMPERATURE eS ee el
RELATIVE HUMIDITY — 4)
PERCENT SHADE coo}
STREAM FLOW — s+
INFLOW TEMPERATURE —_not
«0 feo

o

sous naouron | aps y
oy

wm aves tems we ee
noun rewsenarune | geen

PERCENT POSSIBLE SUN fox

THERMAL GRADIENT

ROUGHNESS

Cr rr i i
RELATIVE CHANGE IN MEAN DAILY WATER TEMPERATURE

Figure 3. Sensitivity of the SNTEMP model’s predictions of mean daily water
emperature to changes in various input parameters.

SENSITIVITY OF MAXIMUM DAILY WATER TEMPERATURE

o + 2 8 #4 5 6 7 wk
AIR TEMPERATURE a
PERCENT SHADE ——__—_—.

RELATIVE HUMIDITY —

STREAM FLOW a ee

FLOW Temperature | +—{—[T——} +

SOLAR RADIATION =o s ”
sovomess a LEG EL
no sve ls (ft ff

—oI
rienunt omens

RELATIVE CHANGE IN MAXIMUM DAILY WATER TEMPERATURE

Figure 4. Sensitivity of the SNTEMP model's predictions of maximum daily
water temperature to changes in various input parameters.

nN



Usable

Perfect
x=
- 30
3g :
20 r =

= : <
~
o 10
>
~ ot : :

JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN JUL AUG

1950 1951
MONTHS

Figure 17. Variation of amount of usable data from self-installed meteorolog-
ical stations. The shaded portion represents the number of days of usable
data; the dashed portion represents the number of days of perfect data
capture. Adapted from Anderson (1955).

One source of air temperature data, both maximum and minimum, is a database
called CLIMATEDATA from U.S. West (see Appendices B and D). Unfortunately, this
database does not contain other meteorological data needed by most temperature
models. However, the sheer number of stations reported in this data base makes
finding representative air temperature data a fairly easy task.

By convention, the primary weather stations record daily maximum and
minimum air temperatures at midnight for the previous day, but not all stations
adhere to this convertion. It is prudent to check the source of your data for
recording time. Rather serious biases, especially for mean monthly values, may
occur otherwise. Any data problems may be compounded if you are trying to
average values between stations or are taking some values from one station and
some from another, If you suspect problems of this sort, see Blackburn (1983)
for a method of correcting for observation time. You should be cognizant of
things like Daylight Savings Time.

The mean annual temperature of cities averages about 1 *C above surrounding
rural temperatures (Linsley et al. 1975); therefore, some additional correction
may be in order. Using rural air temperatures may be more appropriate.

If you take or have available only daily maximum and minimum temperatures,
it may be acceptable to simply take their average to determine the mean daily
air temperature. This averaged value is usually less than a degree above the
true daily average (Linsley et al. 1975). This is because the temperature
variation is asymmetric; during the day it can be approximated by a truncated
sine wave, but during the night it is more of a rapid exponential decay. You
should always test your specific locale for deviation between the two means.

40



areas. There are many potential problems with these units. Some problems unique
to the pyranograph are things (snow, rain, leaves, dust) partially obscuring the
window. Before ordering any such unit, pay careful attention to the units of
measurement (e.g., gm cal. per sq. cm. or Langleys per min.), the method of
obtaining the measurement (e.g., digital readout or pl animetering a strip chart),
and recommendations for calibration. For the "more than you wanted to know"
report, refer to the comprehensive technical manual by Latimer (1972).

Las
a

RELATIVE HUMIDITY, %

+ FLEVATION : 80 FEET ABOVE CANEY FORK RIVER

-e--2e-e ELEVATION: 5 FEET ABOVE CANEY FORK RIVER
1 A 1 1 1 n 4
OF 24 12 24 2 24 2 24 i2
MONDAY | TUESDAY = || WEDNESDAY. «=| THURSDAY. =| FRIDAY

Figure 19. Variation of relative humidity above the Caney Fork River, TN.

This figure shows that near-water humidity measurements are generally higher

than (by almost 20%), and on average greater than, measurements more repre-

ciate of valley-top conditions. Reproduced from Troxler and Thackston
75).

Interestingly, you may find that some days will result in more solar
radiation than is theoretically possible under clear sky conditions. This can
occur if the instrument is receiving direct solar radiation as well as indirect
solar radiation reflected from clouds that are not casting a shadow (Figure 20).

In the absence of solar radiation data, you may be able to estimate it by
first calculating a "clear sky" value using the SSSOLAR program. Then use the
percent possible sun measurements, if available, to scale the total radiation.
Finally, scale these measurements once more, realizing that even at 0% possible
sun, roughly 22% radiation still gets through. For example, suppose you
calculate a clear sky radiation value for the time of year and latitude to be
300 kilojoules/square meter/second. The “completely clouded sky” value would

44



|
;
,
in

TEMPERATURE. IN. OEGREES CELSIUS

SS SSS

1

oF

“DISTANCE, IN MILES, ASOVE HEAD OF TIDEWATER
SWAN RIVER

Figure 29. Temperature versus distance versus time display. Reproduced from
Stevens et al. (1975).

Estimating Water Temperatures

Often one must supply estimates of water temperatures on tributaries where
no water temperature data has been collected. The rule of thumb is that
tributaries that change the mainstem temperature by 5% should be considered in
the analysis. However, these may be either too numerous or too similar to
warrant costly data collection on each one. Therefore, the typical technique
used in estimating incoming temperatures is to model the tributary(s) by
extending them to an arbitrarily defined "zero flow" location. That is, for
model purposes, modeling the tributary from a point at which it can safely be
assumed that all flow is essentially groundwater, which itself is usually
assumed to be entering the stream at the mean annual air temperature. This
water and subsequent accretion will be subject to the full range of meteorolog-
ical effects such that, by the time it mixes with the mainstem, the predicted
water temperatures should be approximately correct. Estimates so derived will
become better and better the longer and more homogeneous the tributary is with
respect to stream geometry conditions. Spot verifications will, of course,

73



accretion rate, 73

accuracy of models, 6, 26, 27, 34,
48, 62, 63, 72, 90, 114

acute temperatures, 23

ADDBEND program, 14

adiabatic lapse rate, 36

advective heat flux, 70

air, vi, vii, viii, 1, 8, 10, 12,
13, 20, 21, 24, 35-37, 39-43,
46, 48, 50, 55, 65, 67, 69, 70,
73, 74, 76, 85-87, 99, 101, 103-
105, 110, 112, 114, 115, 123,
127, 129

anemometer, 46

AVDEPTH, 14

azimuth of stream, 27, 29

bank, stream, vi, 27, 29, 30, 46, 71
baseline conditions, 5
BASIC programs, 42, 87, 89, 92, 123
battery power, 39, 62, 63
bends, stream, 14, 83
bias in models, 13, 40, 62, 71, 93-
biweekly time step,
bottom of stream, i a, 20, 21,

79, 81, 102
braided channel, 14

calibration of models, iii, v, vii,
5, 39, 41, 44, 46, 50, 62, 65,
67-69, 71, 72, 89, 90, 91-93,
96, 100, 103, 107

clearcutting trees, vi, 22-25, 32,
107, 109, 110, 114

cliffs, 21

Climate, 1, 35, 41, 45, 49, 74, 105,
109, 111, 114, 116, 118-120

CLIMATEDATA, viii, 40, 120, 129

INDEX

138

clinometer, vi,
clouds, 23, 24, 27,
65, 93, 95,
conduction, 20,
convection, 12, 46,
correlation, 7, 26, 36, 37, 53, 71,
75-77, 88, 90, 98, 100, 105, 114
curvilinear relations, 16

27, 30

35, 44-46, 50,
118

112

96

96,

x

daily time step, vi, vii, viii, 6,
10-12, 14, 21, 24, 35, 36, 40,
41, 43, 53, 55, 56, 57, 59, 65.
72, 74, 85-87, 90, 94-96, 99,
101, 102, 104, 105, 111, 116-
118, 127, 129, 132, 134

dams, 1, 7, 41, 55, 56, 67, 75, 98,
111, 113, 116

day length, viii, 7, 26, 51,

daylight, 26, 40

debris in stream, 23, 62, 67

deciduous vegetation, 23, 26, 30, 32

declination, 52

densitometer, 32, 113

density of vegetation, vi, 26, 30,
32, 33, 67, 77, 113

depth, viii, 10, 12, 13, 18, 20, 21,
48, 56, 61, 75, 76, 77, 79-81,
83, 89, 97, 108

dew, 35, 36, 43, 114, 123, 125

diel, vi, 21, 22

digital, 39, 44, 62, 63, 71

discharge, iii, vi, 1, 14, 16-19,
23, 24, 26, 48, 53, 54, 67, 69,
70, 74, 76, 79, 86, 99, 102, 105

distances, 12, 13,

diurnal temperatures, 1, 20, 21, 25,
46, 70, 72, 79, 96, 115, 123

duration analysis, vii, 37, 81, 104,
0:

dust coefficient, 44, 45, 50

dye, vi, 18, 19, 112

52, 123



Table D-1. (Concluded)

DAY Oct Nov Dec dan Mar Apr May
28 18 3 oo 8.9 14 22 25
a4 16 8.3 oe aa 2B 22 24
28 16 72 8.9 9.4 13 21 24
2g 16 7.2 8.9 12 13 20 24
30 19 72 7.8 10 15 20 24
3h Sate - 8.3 10 16 aaa 24

Total 585 392 336 292 380 437 544 650
Mean 19 13 12 10 4 14 9 23

Max 23 1 v 1s 7 1 23 26
Min 16 7.2 7.2 5 Ww 9.4 B ed
WIR YR 1957 TOTAL 6935 «MEAN 19 MAX 32 MIN

128


Is it a "guild" of species or a planned introduction? Are we tal king
about a naturally sustaining population, supplemental stocking, or
a put and take fishery? Do we have adequate life history information
for periodicity, microhabitat preferences, and water quality?

(3) identify the relevant variables to be measured/predicted. Is
minimum, mean, maximum temperatures, or some combination the issue?
Is a daily, weekly, or monthly averaging period appropriate? What
is the spatial extent of your study area?

(4) identify the appropriate criteria to employ. Are we talking about
growth, mortality, trigger temperatures, temperature change rates,
"minimum" flows, available fish habitat, population size, dollars,
or commercial or recreational fishing effect? Do not Proceed until
criteria have been formulated and agreed to by all parties.

(5) identify the quantitative measures for decision making (miles of
suitable stream, temperature-conditioned microhabitat, hatching
times, etc.). How concerned must we be about accuracy and/or
Preciston? Do different players need different information to do
their job?

(6) identify and evaluate the feasible solution methods. Is adequate
information already available to make the decisions at hand? If
not, what techniques will best address the questions? Is there a
favored method which has been used by local agencies? How much
time, money, and manpower can (or should) be devoted to the problem
and solution analysis? What is the time frame for decisions to be
made? Can field studies be scheduled? What are realistic management
options?

We hope that by getting all of the participants to reach a consensus on the
above points, you will have come a Tong way toward resolving the impediments
that sneak up on otherwise well planned and executed studies. The remainder of
this document is devoted to helping you perform at least the temperature analysis
effectively and efficiently.

UNDERSTANDING WATER TEMPERATURE THROUGH SENSITIVITY ANALYSIS

Prior to any extensive water temperature modeling or analysis activity,
it is wise to understand the influences that various stream geometry, meteoro-
logical, and hydrological components have on determining water temperature. Such
an understanding will better enable you to (1) prioritize data collection
activities, (2) know the degree to which you should be concerned with quality
control errors, (3) know which Parameters can be safely estimated, and
(4) broaden your perspective of potential management strategies. To further
this understanding, we propose an initial consultation with a sensitivity
analysis tool.



Table D-3. Part of a Lotus 1-2-3 (tm) worksheet created by Hydrodata Optical
Disk Database.

ABBREVIATED EXAMPLE OF HYDRODATA DATABASE PRINTOUT

31 a4 31 Ru at aL
uses USGS uses usGS USES uses
06768000 06768000 06768000 06768000 06768000 08768000

3 31 31 31 3 31

047 O47 047 047 047 O47

PLATTE R PLATTE R PLATTE & PLATTE & PLATTE R PLATTE RIVER NEAR OVERTON,
SW Sw Su SW SW SW

404057 404057 404057 404057 404057 404057

0993224 0993224 0993224 0993224 0993224 0993224
00 00 00 00 00 00

gggs99 6 ggessg §=6ggag9e g9s99s 999999 999999
g99899 6999999 999999 999999 99a9ss 999999
gesgo9 6 gggsee §=ggg999 999999 999998 999909
57700 57700 57700 57700 57700 57700
52300 82900» 52900) 52900 © $2900 = 82900
~geg99 =-99999 -G99G9 ~gssg9 § ~-G9999 99909
2298.830 2298.830 2298.830 2298.830 2298.830 2298.830
1978 1979 1980 1981 1982 1983

i i 1 1 1 1

i 1 1 1 1 i
10 10 10 10 10 10
10 10 10 10 10 Ww
10200101 10200101 10200101 10200101 10200101 10200101

34,50 25.00 30.00 31.80 21.10
31.50 26.50 30.50 31.50 20.80
30.50 28.00 30,00 33,50 23.30
32.00 25.00 31.00 34.50 26,10
33.00 24.00 28.00 30.50 26.70
31.50 20.50 31,50 27.00 24,40
34.00 20.00 29.50 30.00 23.30
36.50 18.50 23.00 26.00 26.70
33.50 20.50 31,00 25.00 28.90
33.00 20,00 33.00 28.50 27.80
33.50 20.00 32.00 30.50 26.70
28.00 19.00 33.00 30.50 24.40
33,00 19,50 32.00 30.00 25.50
34.00 20.00 31.00 28.50 26.70
28.00 20.00 30.50 30.50 27.80
29.00 20.50 23.50 31.50 25.50
28.50 21.00 34.50 33.50 26.70
35.50 21.00 31.00 34.00 27.20
31.50 23.50 33.50 33.50 27.80
32.00 24.50 34.50 32.00 27.20
34.50 23.00 33,50 34,50 27.80
31.00 22.00 33.00 34.00 28.90
35.00 22.00 33.00 30.50 26.70
29.50 22,00 33.00 30.00 27.80
29.50 22.00 31.00 31.50 30.00
31.50 22.00 33.50 32.00 30.50
27.00 22.00 32.00 29.00 30.50
24.00 23.50 33.00 29.00 29.40
26,00 23.50 33.50 32.00 = 29.40
28.00 24.50 31.50 26.00 30.50
29.50 23.50 30,50 30,00 31,70

130


It has been theorized that wind speed affects solar reflectivity by
altering the water surface roughness and introducing air bubbles near the
surface. (The same thing has been postulated for roughness caused solely by
stream gradient.) However, Raphael (1962) mentions that the widely cited Lake
Hefner studies failed to find any relationship between wind speed and reflec-
tivity. Also, experimental data has shown insensitivity of surface reflectance
to the water’s purity and turbidity (Viskanta and Toor 1972).

Ground Temperature

Ground temperature typically is an insensitive parameter in water
temperature modeling. Though not strictly speaking a meteorological parameter,
ground temperature is so strongly influenced by the long-term meteorology that
it might as well be. In the absence of other data, we generally assume that
ground temperature is the same as the mean annual air temperature. This
assumption is very nearly true for moderate depths (approximately two feet) in
Fort Collins, Colorado (Figure 23). One can, however, spot seasonal trends that
show the lag time from surface temperature to differing depths. Another study,
in Vienna (cited by Geiger 1965), showed ground temperature at a depth of 1.9
meters to be 1 °C higher than the mean annual air temperature over a period of
33 years.

There may be occasions when you feel that the assumption of mean annual
air temperature is incorrect, such as in a geothermal area, or where the aspect
of local topography thoroughly influences the input of solar radiation to the
ground (Figure 24), or because of local soil/rock characteristics. If so, it
would be good to verify the ground temperature.

Direct measurement of ground temperature will be nearly impossible for
any given study because ground disturbances due to drilling or digging are too
disruptive to the temperature profile. In fact, it can take up to 10 years for
temperature profiles in the ground to stabilize completely after a disturbance.
For this reason, it would be far better to check for sources at weather stations
or agricultural experiment stations than to attempt measurement yourself. Since
we are interested in ground temperature below the stream surface, these reported
temperatures may not be representative. Groundwater temperature from shallow
wells near the stream may be more appropriate. Similarly, temperature of

discharging springs may be a good source for both ground temperature and lateral
inflow temperature.

Ground Reflectivity

The ground reflectivity (percent) is a measure of the amount of shortwave
radiation reflected from the earth into the atmosphere. Representative values
may be taken from Table 5 (Geiger 1962; Gray 1970; Tennessee Valley Authority
1972) without being too concerned with accuracy due to the relative insensitivity
of this parameter.

48



Appendices provide information on State and Federal agencies that are good
data sources, vendors for field instrumentation, and small computer programs
useful in data reduction.

iv



Occasionally, spot measurements may be used in lieu of continuous
recorders. Moore (1967) outlined techniques for reconstructing annual
temperature cycles by regressing spot temperature measurements, if date and time
of observation are known, with continuous measurements on similar streams. He
found that seasonally stratified regressions produced better results than entire-
year regressions as streams became more dissimilar. For example, lumping May,
June, September, and October in one group, November to April in another, and July
and August in a third produced good results.

Moore also reviewed previous investigators’ methods for obtaining mean
daily water temperatures, for example, averaging the 9 a.m. and 9 p.m, versus
8 a.m. and 4 p.m. temperature measurements. He reported that averaging the daily
maximum and minimum temperatures gave an acceptable mean, with a probable error
of only 0.3 °C (.54 °F). In the context of our previous discussion of averaging
max and min air temperatures, averaged water temperatures should be checked at
your site for deviations from true 24-hour means.

Site Selection

There are several considerations that go into decisions on how many and
where temperature recording instruments should be placed. The "how many" is
often based on cost. Figure 26 illustrates the relative priorities for
establishing stations in different system configurations. Obviously, the first
priority is to accurately measure stream temperatures within the river reach(es)
of biological importance (Figure 26a). This (single) location will also suffice
for calibration purposes. Beyond this first priority, the picture becomes
cloudy, with lots of intervening variables; nevertheless, we can make some
generalizations. In general, the next priority must be assigned to reservoir
release temperatures (Figure 26b), since all temperature models require these
starting water temperatures. In some cases, if it is known that reservoir
release temperatures are relatively constant, at least through the season of
concern, actually measuring that temperature through time may not be as
important. Further, as the distance from important biological sites to a
reservoir becomes large (greater than 30 km), the need for release temperature
measurements decreases. In such cases, equilibrium release temperatures may
suffice. This is not to say that you should not measure the release temperature;
however, if time, money, and manpower are limiting, this may be an area where
data can be sacrificed. Use the segment temperature model to test system
sensitivity.

In a situation where there is no reservoir, headwaters are the logical
candidate. It could be argued, however, that headwaters sufficiently far
upstream (greater than 30 km) can just as easily be approximated by using the
"zero flow headwater" approach. If there is a reservoir and one or more other
"major" tributaries (Figure 26c), it could be argued that knowing the temperature
immediately above the junction may be more important than knowing the reservoir
release temperature, again considering the relative distances involved. That
is, if there is no area of biological concern above the junction, we do not care
as much about the temperature profile in the upper reaches. If, however, the
release temperature fluctuates dramatically, or more importantly, release
temperature is a management action to be evaluated, placing a recorder at that
location should dominate (Figure 26d).

65



the resistance to heat Toss; the thermal gradient is a measure of the conductance
of heat. The larger the difference between the ground temperature and the water
temperature, the greater the potential heat transfer. Though determined to be
small relative to other parameters (Figures 3 and 4), some authors have
determined the thermal gradient to be reasonably sensitive in predicting diurnal
temperature variations in small, shallow streams (Jobson 1977). Comparing the
results of models run with and without the consideration of a thermal gradient,
Jobson determined temperature differences averaging about 0.25 °C (0.45 °F).

Comer and Grenney (1977) document a method for assessing the thermal
gradient in shallow, sand- and gravel-bed streams. Measurement without
disturbance is difficult, but possible. They concluded that for streams with
significant interchange of water in the saturated zone below a river, the net
heat flux from the "ground" into the stream at night may equal the outgoing flux
at the air-water interface. They also suggest that solar radiation may indeed
be directly absorbed by the streambed in clear, shallow streams.

Very clear streams with black or dark rock bottoms may display different
diurnal temperature variation than would be explained by the SNTEMP family of
temperature models. Table 3 from Geiger (1965) shows the percentage of incident
solar radiation reaching various depths in clear water. Note the significant
decline in mid-wavelength radiation between 10 cm and 1m. Thus, bottom
conditions may not matter in streams deeper than 10 cm to 1 m.

Table 3. Percentage of incident solar radiation reaching various depths in
water.

Wavelength (u) Imm lcm = 10cm Im 10m 100m

0.2-0.6 100.0 100.0 99.7 96.8 72.6 5.9
0.6-0.9 99.8 98.2 84.8 35.8 2.6 0.0
0.9-3.0 65.3 34.7 2.0 0.0 0.0 0.0

In general, my sensitivity analysis demonstrated little influence exerted
by the ground temperature. However, Jobson and Keefer (1979) showed that
including the heat transfer at the streambed decreased the mean error in their
dynamic temperature simulation, but increased the RMS error. They concluded that
streambed conduction acts as a “damper” to temperature computation, which,
overall, improved their modeling results.

Crittenden’s (1978) sensitivity analysis showed that the diffusivity of
the streambed was significant in an equilibrium temperature model developed for
smalt, shallow, low gradient streams subject to intense solar radiation. The

20



Table 9.

Example thermograph specifications.

Requirements

Suggested criteria

Temperature range
Resolution

Accuracy

Recording method
Interface

Battery life
Measurement capacity

Measurement interva
Deployment

Size
Weight

At least 0 to +50 °C (32 to 122 °F)

At least 0.1 °C (0.18 °F)

At least +- 0.3 °C (0.54 °F)

Digital with visible readout

RS-232 compatible with IBM microcomputer

At least 1 year

At least 4380 (6 months of hourly samples)
recordings

Selectable, at least 30 minutes and 1 hour

Both submersible (waterproof) for in situ
recording and by remote cable (water
resistant)

Minimal

Minimal

Lifton (Woodward-Clyde, pers. comm.) listed problems with the units he

Theft
Vandalism
Leakage

Battery failure

RAM failure

has used and arrayed them from most to least common:

Chart jam or malfunction
Stylus jam or breakage

Chip pin damage or breakage
Analog to digital converter failure

SOON AMEYONe

Tape or film failure

All of these problems may be mitigated to some degree by how often you visit the
instrument. Once every two weeks to begin with, and then once a month, may be
reasonable, but this is highly dependent on local conditions, desired redundancy
in your data, and cost.

More complex studies may make it necessary to collect other water quality
data at the same time as water temperature data. Refer to other sources such
as Bark et al. (1986), Benham and George (1981), Hamilton (1984) and Appendix
B for more information and references.

63



time, vi, vii, viii, 6, 8, 10, 12,
13, 16-19, 22, 26, 30, 34, 35
39-41, 43-46, 48, 50, 52, 53,
55, 56, 62, 63, 65, 67, 70-74,
76-79, 84, 86-90, 92, 93, 95,
97-102, 105, 106, 108, 112, 114,
123, 124, 129

topography, vi, 13, 21, 24-28, 30,
34, 35, 43, 46, 48, 50, 97

toxic, 18, 105

transect, vii, 18, 32, 53, 68

transport, 13, 86, 98, 99, 110, 112

travel time, vi, 8, 10, 13, 16-19,
98, 101, 102, 108, 112

tributary, 53, 55, 67, 73, 79-81,

6

4,

undergravel flow, 79, 84

units of measure, vi, 10, 14-16, 39,
44, 62, 63, 68, 71, 86

USGS, viii, 27, 54-57, 59, 107, 109,
110, 112, 117, 130

validation of models, iii, v, vii,
5, 62, 89-93, 95, 102, 110, 114,
116, 117

vandalism of equipment, 62, 63

variance of results, 7, 39, 86, 93,
95, 100

vegetation, vi, 13, 21, 23, 24, 26-
28, 30-32, 34, 37, 43, 46, 51,
92, 97, 108, 115, 118

verification of models, iii, 37, 41,
43, 48, 62, 67, 73, 84, 89, 90,
92, 107

WASP model, 77

wavelength, 20

weekly time step, 6, 23, 35, 63, 90,
94, 118

WESTEX model, 78

width, vi, 7, 8, 10, 13-15, 21, 23,
26, 30, 32, 33, 76, 80, 81, 83,
86, 92, 101, 103, 123, 126

139

wind, vi, vii, 8, 10, 12, 34-36, 43,
46-48, 55, 75, 76, 86, 97, 103,
127, 134

winter conditions, 23-25, 36, 39,
43, 59, 74-76, 84, 88, 100

WQRRS program, 77, 78

WSP program, 16, 102

year, vi, vii, 1, 23, 25, 26, 30,
35, 36, 41, 43-45, 48, 50, 55-
60, 63, 65, 70, 74, 75, 76, 90,
94, 98, 99, 103-106, 113, 118,
127, 131



9g

Table 7a. Alternative forms in which you may find water temperature data in USGS publications or
databases: a daily values summary showing availability of temperature data through space and time.

DAILY VALUES SUMMARY
RETRIEVAL NUMBER 1.00
PARM STAT BEGIN END tt: MISSING

STATION NUMBER LOCATION DEPTH CODE CODE YEAR MO YEAR MO DAYS DAYS YEARS MAXIMUM —NIWINUM MEAN
BOISE RIVER NR TWIN SPRINGS ID aceacy USGS STATE 16 DISTRICT 16 COUNTY O15 SITE SW DR AREA = 830.00 SQ KI
13185000 00010 00001 31987 9 661 az 3 22.00 51
13185000 00010 00002 re 6 1979 9 701 29 3 25.70 221
13185000 00010 00002 1955 3 1957 9 661 42 3 19.50 58
13185000 00010 00002 1977 6 1979 9 701 29 3 20.40 80
13185000 00010 00003 1977 6 1979 9 701 29 3 22.90 03
13185000 00010 00011 1958 10 1959 9 365 1 24.5 59
S FK BOISE RIVER NR FEATHERVILLE 1D Asevct UsGS STATE 16 DISTRICT 16 COUNTY 039 SITE SW DR AREA = SQ
13186000 00010 00001 1962 12 1965 9 973 3 25.59 45
13186000 00010 00001 1977 5 1977 9 226 19 2 24.00 22
13186000 00010 00001 1978 3 1979 9 574 36 2 23.19 -06
13186000 00010 00002 1962 12 1965 9 973 6 3 18.30 54
13186000 00010 00002 1977 5 1977 9 226 19 2 18.30 .
13186000 00010 00002 1978 3 1979 9 574 36 2 17.60

13186000 00010 00003 1977 5 1977 9 228 18 H 20.90

13166000 90010 00003 1978 3 1979 9 19.80

S_FK BOISE RIVER AT ANDERSON RANCH OAM ID AGENCY USGS STATE ry bismich 16 coy 039 sim

13190500 00010 00001 1977 5 1979 9 B97 7.1!

13190500 00010 00002 1977 5 1979 9 $37 5 ‘ ie.10

13190500 00010 00003 1977 5 1979 9 v7 4 16.40

SPR BOISE R AT NEAL BRIDGE WR ARROMROCK DAM TO ASENCY USGS. STATE 16 DISTRICT 16 COUNTY 039 SITE

13192200 00010 00001 1977 $ 1979 9 895 19 4 19.

13192200 00010 00002 1977 5 1979 9 835 8 4 15.40

13192200 00010 00003 1977 5 1979 9 4 17.60

WHORES CREEK AB ROBIE CREEK NR ARROWROCK DAM ID ASENCY | Uses STATE ry oistich 16 COUNTY O15 SITE

13200000 00010 00001 1964 1: 1 1.69

13200000 woo1o oo0ol ise “3 ise7 3 e231 2 27.

13200000 00010 00001 1968 12 1969 8 235 39 1 26.

13200000 00010 C0001 1970 1 1972 9 908 35 3 25.

13200000 00010 oO002 1964 12 196412 14 7 1 1.03

13200000 00010 o0002 1965 3 1967 9 829 115 2 23.89

13200000 0010 00002 1968 12 1969 7 213 30 1 20.99

13200000 00010 00002 1970 1 1972 9 908 35 3 21.09

13200000 00010 00003 196710 1968 9 412 46 2 22.00

13200000 00010 00003 1969 9 1969 9 28 2 0 19.39

13200000 00010 00011 196910 1970 9 438 19 2 20.00



temperatures and relative humidities to points of known elevation. Though any
of these major processes may be of great importance, none are individually
sensitive to small errors in elevation. Thus, elevations may be taken from
readily available topographic maps even though contour intervals on some maps
may be 40 feet and low relief terrain may not have easily discernable elevations.
The most difficult task may be identifying where some station or node actually
is on the map. For example, field work may have indicated significant changes
in the distribution of riparian vegetation not apparent on the map. Be wary of
trying to model very steep gradient, almost waterfall, situations; SNTEMP may
dramatically overestimate heat flux due to friction if the stream width is too
narrow.

Distances

Stream distances are important in calculation of heat transport. Distances
basically translate to travel time and thus exposure time to all of the heat flux
conditions. Aside from river mile indices that may be available, maps or aerial
photos of known scale provide the easiest way to estimate distances. Distances
can be a source of model bias if consistently over- or underestimated. Streams,
being sinuous, can be tricky to measure reliably using a map and a map wheel,
especially if the map has been protected with an acetate cover. It is best to
measure the segments repeatedly, using a paper map, and take an average. In
cases where you are aware that a schematic map does not convey the true
sinuosity, it may be advisable to multiply the measurement by a "fudge factor”
to account for the difference. It would be better to use aerial photos in this
situation.

If a temperature analysis is being conducted in conjunction with a Physical
Habitat Simulation Model (PHABSIM) analysis (Milhous et al. 1989), actual
surveyed or paced stream segment distances may be available, especially if the
detailed “habitat mapping” approach (Morhardt et al. 1983) is being used. It
is always preferable to measure the distance the water is actually flowing.
Also, distance may actually change as a function of flow. If large changes are
anticipated, adjustments to model distances must be made in a fashion analogous
to PHABSIM high-flow and low-flow models. That is, one set of data is used to
describe the high flow conditions, one set the low flow.

Stream Width

Stream width can be a very sensitive parameter (recall Figures 3 and 4)
in modeling water temperatures. All of the heat flux activities take place at
either the air-water interface or the water-ground interface, both of which are
as wide as the wetted stream width. At least one paper (Dymond 1984) attempts
to develop a simple nonenergy balance model that predicts change in temperature
based solely on changing the flow and hence width (depth) and time-of-travel.

The SNTEMP series of models employs a width as a function of flow
relationship in the form of

Wea a?
where W = width (m)

13



}+— soon straw

0

Figure 12. The "Bovee" home-made clinometer using a protractor, fishing
weight, and soda straw.

itself, i.e., there is no need to correct for the height of the stream bank.
Also note that all shading is important; understory trees, brush, and shrubs may
be more significant than commercial grade timber in many cases (Currier and
Hughes 1980). Both east and west sides may be measured independently. The
height may be calculated by the formula H = D * TAN(A), where H is the height,
D the distance from the observer (in the water) to the vegetation, and A the
angle from the water surface to the tops of the vegetation. The simple
protractor or a clinometer may be used to estimate the angle. Some clinometers
also have a built-in rangefinder so that the distance to the vegetation may be
measured simultaneously with the topographic altitudes. You should also correct
for the height of the observer. Most clinometers come with instructions for
making these calculations.

The vegetation crown. (Vc) is the average maximum crown diameter for the
existing or proposed shade-producing strata of vegetation along the stream.
Values for both east and west side may be independently measured. Direct
measurement or estimation from aerial photos may be used. Even if vegetation
is continuous, the diameter is important in the model’s calculation of overhang.

The vegetation offset (Vo) is the average offset of the trunks of the
existing or proposed shade-producing strata of vegetation from the water’s edge.
You may need to vary this if you vary the stream width. Values for both east
and west side may be independently measured.

The vegetation density (Vd) is the average screening factor (0 to 100%)
of the existing or proposed shade-producing strata of vegetation along the
stream. It is actually composed of two parts: the continuity of the vegetative
coverage along the stream (quantity), and the percent of light filtered by the
vegetation’s leaves and trunks (quality). This percent of light may need to be
adjusted for the time of year if you are dealing with deciduous vegetation. The
Stand-Alone Shade Model (Theurer 1984) provides for such variation with time of
year.

30



Figure 24. Departure from the mean ground (70 cm) temperature (°F) according
to direction of slope and time of year. This illustrates the fact that
different topographic conditions may result in markedly different subsurface
(groundwater) temperatures. Reproduced from Geiger (1965).

Dust Coefficient

This parameter is one that probably should have been left out of the SNTEMP
family of models. There is no good direct way to measure this index to the
scattering effect that dust and other small particles have on incoming solar
radiation. Theurer et al. (1984) show how to perform a solar radiation
calibration to arrive at reasonable values for both ground reflectivity and the
dust coefficient. A similar, but more straightforward approach would require
good estimates for cloud cover, air temperature, and ground-level solar radiation
and a best guess for ground reflectivity. The SSSOLAR program then may be used
to estimate what the dust coefficient must be to produce the ground-level solar
radiation.

50



changing flow, even though SNTEMP considers it a constant. Though there are
guidelines an experienced hydrologist can use in determining roughness, the
confidence interval surrounding such estimates is likely to be large (Platts
1981). Use of a step-backwater hydraulic simulation model, such as the Water
Surface Profile (WSP) model (Milhous et al. 1988), may be a better method to
estimate n. The use of a regression-type hydraulic model, such as IFG4 (Milhous
et al. 1989), however, is not recommended; the "n values" used in this type of
model are really “conveyance factors" and not true estimates of channel
roughness.

Iravel Time

Travel time is an alternative to Manning’s n. Travel time is the inverse
of velocity. If velocity is measured in units of length per time, then travel
time is measured in units of time per length, such as seconds per kilometer in
the SNTEMP model. Stream velocity, and therefore time of travel, vary with
discharge. The relationship takes the form:

Travel Time = a ?
where a and b = empirically derived coefficients
Q = discharge

Note that the exponent b may itself vary as the flow-control varies with
discharge. For example, the stream may change from a fundamental pool-riffle
contro] to a channel control as the discharge increases. Consequently, three
or more time-of-travel measurements may be necessary, depending on the range of
flows of interest. If no control change takes place, a travel time vs. discharge
plot may be constructed (Figure 6). If a control change is evident, such a plot
would itself be curvilinear (Hubbard et al. 1981). Travel time may be either
estimated or measured for steady or gradually varied flow conditions.

Often, travel time estimates are available from power/water companies.
If travel time must be estimated from very limited data, the following empirical
relationships, adapted from Boning (1974), may be used. These relationships were
developed from 873 independent measurements throughout the United States. Note,
however, the large standard errors involved.

Pool and Riffle Reaches (standard error = 40%)

Tr=1/ (0.38 9°49) « 5-20
Channel-Controlled Reaches (standard error = 26%)
0.26 0.28

Tr =1/ (2.699°°*°) *§
where TT = travel time (s/ft)
Q = flow (cu.ft./s)
S$ = slope of streambed (ft/ft)
16



Lagrangian methods, 99, 112

lapse rate, vi, 36, 37

LARM model, 78, 111

latent heat, 74

lateral flow, 7, 18, 48, 55, 71, 79,

86

latitudes, viii, 24, 26, 27, 44, 45
52, 57, 123, 129

leaf, 51

lethal temperatures, 23

level plane, 26, 36, 39, 41-43, 46
50, 67, 70, 77, 78, 81, 89, 103,
125, 131

light, 18, 21, 26, 30, 34, 51, 98,
108, 112, 116, 119

local conditions, viii, 6, 26, 35,
36, 43, 48, 63, 101, 114

log, 14, 71, 86, 125

logarithmic, 34, 119

logs in stream, 21, 22, 97

longitude, 57, 79, 81, 94, 129

losing stream, 53, 54

Lotus 1-2-3, viii, 14, 123, 126, 130

Manning’s n, 14, 16, 19, 81, 83,
101, 102

map, 13, 18, 27, 114

maximum temperature, vi, viii, 6-8,
10-12, 14, 21, 23-25, 30, 36,
40, 41, 43, 56, 57, 59, 61, 65,
68, 70-72, 74- 76, 81, 85, 86,
90, 93, 97, 99, jo1- ios, 123,
127, 129

mean temperature, vi, vii, viii, 2,
5-8, 10-12, 18, 20, 24, 33, 35,
39-41, 43, 48, 50, 53, 56, 57,
59, 61, 65, 67, 69, 70, 73, 75,
86, 87, 92, 93, 95, 96, 99, 101-
104, 106, 110, 112, 118, 127-
129, 131, 133, 134

median conditions, 10, 36

meteorology, iii, v, vi, 12, 34, 35,
37-40, 43, 46, 48, 73, 74, 85,
87, 88, 97, 99-103, 105, 112,
113, 118, 120-123, 132

microclimate, 41

microhabitat, vii, 6, 74, 80, 84, 89

microthermal habitats, 54, 79, 84

minimum temperature, 6, 10, 18, 21,
23, 25, 36, 39-41, 56, 57,

137

61, 65, 70-72, 75, 81, 86, 88,
95, 104, 105, 123, 127, 129
missing values, 46, 56, 86-88, 99,
00, 102
mixing equation, 55, 67, 69, 79, 81,
> 84, 101
monitor, vii, 7, 66, 67, 85, 107
Monte Carlo method, 100, 103, 105
month, 2, 6, 35, 36, 39, 40, 41, 46,
53, 58, 59, 61, 63, 76, 94, 95
98, 99, 105, 108, 112, 118, 119

120
24, 35, 36,
103, 105, 118,

NAWDEX data base, 53,

normal conditions, vii,
75, 90, 95, 101,
132

optimization methods, 78
oxygen, 77, 96-98, 107, 108

parameter, iii, vi, viii, 6-8, 10-
12, 13, 20, 23, 26, 27, 31, 35,
37, 39, 41, 43, 46, 48, 50, 76,
85, 86-90, 92, 94, 97, 102, 103,
105, 129

percent possible sun, 8, 10, 36, 44-
46, 86, 103

permanganate, 18

PHABSIM method, 13, 14, 18, 53, 114,
126

planimetering, 44

pool, vii, 14, 16, 79-82, 83, 84,
107, 115

priority, iii, vii, 6, 65, 66

probability, 65, 101, 102, 103, 105,

profile, temperature, vii, 16, 25,
48, 65, 67, 77, 82, 102, 112,

psychrometer, 42
psychronometer, 41
pyranograph, 43, 44
pyranometer, 43
pyrgeometer, 43
pyrheliometer, 34, 43
pyrradiometer, 43



Riffle ———
Pool -==

0,
(°c) 8:30 PM
Es 244 12 Midnight
224 Ly
= ao A L\ f L\ ay
i \ “\ nN oN N\
mTM \.
5 184 AN ran oo Ne NE
V 7 / O01
F 16 1 ; 7 a +, T :
28 29 30 31 1 2 3
July August
DAYS

Figure 32. Comparison of water temperatures in pools and upstream riffles.
The volume of pools buffers water temperature in low-flow situations. Adapted
from Anderson and Miyajima (1975).

It is very important to remember that the roughness value is a function of flow
and can increase dramatically at low flows. Also, any channel bends or other
features will tend to shorten the mixing length. Consult an experienced
hydrologist when applying this formula.

An alternative method of estimating the length of channel, from a side
channel, necessary for complete mixing may be found in Hubbard et al. (1981).
It does not involve the use of Manning’s n, and has been adapted as shown here:

2
t= 9.062-y* w
d*s
where v = velocity (ft/sec)
w= width (ft)
d= depth (ft)

S = slope of water surface (ft/ft)

83



-0.5 c/100 m is average in
J free atmosphere

400,
% 6 - 47 3
47-45 @
sot 45-4 2
3]
60— g
&
ali 2
40- “4-2
Superadiabatic os ;
lapse rates Ea inversions
Be
Sa
°
- 2s
° ae* 1 1 1 1 j
* a 7 2 ry 6 $40

Temperature lapse rate (deg/100 m)

Figure 15. Frequency distribution of temperature lapse rates at different
altitudes. Reproduced from Geiger (1965).

Both problems can be solved, or at least improved, by some form of on-site
verification of air temperatures. If your study area is relatively homogeneous
with respect to elevation, vegetation, land use, and orientation, a single site
may be established for comparison with the known data set. A record length
suitable for statistical correlation with the established station would need to
be generated. If a large elevation change, or other non-homogeneity, is
recognized, two air temperature stations would be recommended, such that some
form of elevational correction or subarea modeling could be accomplished.

Each of the meteorological parameters will be discussed in detail. Keep
in mind that establishing a “full blown" meteorological station (Figure 16) will
cost $3,500 or more just to install, with variable operating costs depending on
the duration of the study and the frequency of data collection and checking.
Further costs will be incurred for data reduction and quality control.
Therefore, I will discuss each individual parameter so that options for varying
the data collection effort may be evaluated. I will provide more detail for
items that are either more important than others or which may be unfamiliar to
most people.

If you are faced with establishing a more or less permanent, self-recording
meteorological station, the following factors may be considered essential
(Anderson 1955; Tenney 1987):

37



10.

ll.

|

public domain with user’s manual and technical assistance from the
Corps of Engineers Hydrologic Engineering Center in Davis,
California.

CE-QUAL-R1 - An extensive one-dimensional model of reservoir water
quality, descended from WQRRS. This model is reported to work well
in fluctuating-level reservoirs. Model is public domain with user’s
manual and technical assistance from the Corps of Engineers Waterways
Experiment Station in Vicksburg, Mississippi.

CE-QUAL-W2 - A two-dimensional reservoir (and other waterbody) water
quality model similar to CE-QUAL-R1. Model is public domain with
user’s manual and technical assistance from the Corps of Engineers
Waterways Experiment Station in Vicksburg, Mississippi.

CE-THERM-1 - A one-dimensional reservoir model exclusively for
predicting temperatures as controlled from multilevel outlets. This
model is also reported to work well in fluctuating-level reservoirs.
Model is public domain with user’s manual and technical assistance
from the Corps of Engineers Waterways Experiment Station in
Vicksburg, Mississippi.

WESTEX - A one-dimensional reservoir model for temperature and
conservative constituent modeling for optimal design of multilevel
outlets. Model is public domain with draft user’s manual and
technical assistance from the Corps of Engineers Waterways Experiment
Station in Vicksburg, Mississippi.

SELECT - A one-dimensional, steady-state, selective-withdrawal model
for deciding which ports to use to meet downstream water quality
objectives. Model is public domain with draft user’s manual and
technical assistance from the Corps of Engineers Waterways Experiment
Station in Vicksburg, Mississippi.

HEC-5Q - Reservoir system simulation/optimization model for water
supply and water quality planning studies. Model is public domain
with user’s manual and technical assistance available from the Corps
of Engineers Hydrologic Engineering Center in Davis, California.

Laterally Averaged Reservoir Model (LARM) - LARM is a one- or two-
dimensional temperature and reservoir flow model useful in
reservoir/stream networking analysis. It has been used in estimating
release temperatures over time of varying the release outlet location
(Gordon 1981), and it has been used in conjunction with SNTEMP on
complex stream networks in California (Lifton et al. 1985). The two-
dimensional model requires 15-minute time steps to remain stable.
The model is in the public domain and available from the Corps of
Engineers Hydrologic Engineering Center in Davis, California.

78



Bureau of Reclamation

Environmental Protection Agency

Federal Energy Regulatory Commission
Fish and Wildlife Service

Geological Survey

Naval Facilities Engineering Command
Atomic Energy Commission

International Boundary & Water Commission
Tennessee Valley Authority

The State agencies are too numerous to list individually because the names
vary slightly by State. However, a general categorization would be:

Water Resources Departments

Game and Fish Departments

Public Health Departments
Pollution Control Departments
Sanitary Engineering Departments
Water Quality Control Departments
Water Districts

State Geological Surveys
Utilities

Out of 7,500 stations collecting surface water temperature data, about
4,500 were Federal and 3,000 were State. Most (over 4,000) were east of the
Mississippi, and most of these were in the Great Lakes States. Of the stations
in the West, most measurements were in the three coastal States. These
statistics are not so impressive when frequency of measurement is considered.
Continuous water temperature measurements were made at only 731 stations, largely
concentrated in Washington, Oregon, and California. Continuous measurement is
not needed in groundwater temperature measurements, however.

Examples of commonly available formats for obtaining daily water
temperature data from USGS are shown in Tables 7a, 7b, and 7c, and from STORET
in Table 8. Appendix D contains some examples of formats available from the
HYDRODATA database. (This data base also may contain some isolated humidity,
solar radiation, air temperature, and wind speed data.) Make sure that no
changes have occurred in the system that would have affected water temperatures
if you use historical data. Dams, irrigation diversions, or channelization are
examples of things to look for (Hamilton 1984). Historical USGS water
temperature measurements are generally considered accurate within plus or minus
1 °F (.56 °C) 80% of the time, and within 2 °F (1.1 °C) about 95% of the time
according to Moore (1967); this may be better today, but I have no supporting
evidence. Also, temperature gaging stations immediately downstream from a
dissimilar tributary may give erroneous measurements due to incomplete lateral
mixing. This will likely be true at gaging stations that have had temperature
measurement added to existing stage measurement capabilities. Mixing must be
present during all seasons; fluctuating tributary conditions, as found with
snowmelt, can result in incomplete mixing during part of the year.

55



Microthermal Habitats

All of the water temperature models we have employed are one-dimensional
models in that they assume that the water is thoroughly mixed at any point in
time and space, and therefore can be used to predict temperature patterns
Tongitudinally (downstream) only. They contain no facility for looking at
temperature changes horizontally (across the channel) or vertically (from top
to bottom). At moderate to high flows, this assumption should not be a major
difficulty. But at low flows, when most temperature extremes are reached, you
need to examine this assumption more closely.

Bilby (1984) classified four distinct types of cool-water areas in a fifth-
order Washington stream (Table 10). He termed them lateral seeps, pool bottom
seeps, cold tributary mouths, and flow through the streambed. Temperatures in
these cool-water areas averaged 4.7 °C (8.5 °F) lower than ambient water
conditions on warm afternoons. A total of 39 coo} areas were found within a 3.5
km reach, and collectively accounted for 1.6% of the surface area and 2.9% of
the water volume of this stream.

Table 10. Average size, depth, and temperature depression of the cool-water
areas found by Bilby (1984) in a Washington stream (discharge not reported).

Average Average Average temperature

size (m*) depth (cm) depression (°C)
Lateral seeps 1.3 15 4.7
Tributary mouths 6.0 23 5.3
Pool-bottom seeps 7.3 43 4.9
Undergravel flow 4.6 38 3.9

The areal extent of cool-water areas formed by lateral seeps and tributary
mouths was influenced by the characteristics of the stream channel upstream from
the point of cool-water entry. Anything that served to deflect warm, flowing
(ier fo) mixing with the cool water formed protected cool-water areas

gure 30).

In general, the seeps had a greater degree of thermal constancy and a
greater temperature depression than did the undergravel flow areas, which
represented the majority of the cool-water area. This was attributed to seepage
being actual ground-water effluent, whereas undergravel flow had originated as
instream water and was only cooled by its passage through the streambed. The
undergravel flow therefore showed some diurnal temperature variation.

719



Lynch, J.A., G.B. Rishel, and E.S. Corbet. 1984. Thermal alteration of streams
draining clearcut watersheds: quantification and biological implications.
Hydrobiologica 111:161-169.

Mankin, J.B., R.V. O’Nei11, H.H. Shugart, and B.W. Rust. 1975. The ‘importance
of validation in ecosystem analysis. Pages 63-71 in G.S. Innis, ed. New
directions in the analysis of ecological systems, Part 1. Simulation Councils
Proceedings Series 5(1).

Marceau, P., D. Cluis, and G. Morin. 1986, Comparisones des performances
relatives a un modele deterministe et a un modele stochastique de temperature
de 1’eau en riviere. Can. J. Civ. Eng. 13(3):352-364.

McCombie, A.M. 1959. Some relations between air temperatures and the surface
water temperatures of lakes. Limnol. Oceanogr. 4(3):252-258.

Meehan, W.R. 1970. Some effects of shade cover on stream temperature in
southeast Alaska. USDA For. Serv. Res. Note PNW-113. Pacific Northwest
Forest and Range Experiment Station. 9 pp.

Milhous, R.T., M. Updike, and D. Schneider. (1989). Physical Habitat Simulation
System (PHABSIM) - Version II. National Ecology Research Center Report. Fort
Collins, CO. In press.

Millard, S.P., J.R. Yearsley, and D.P. Lettenmaier. 1985. Space-time
correlation and its effects on methods for detecting aquatic ecological
change. Can. J. Fish. Aquat. Sci. 42:1391-1400.

Moore, A.M. 1967. Correlation and analysis of water-temperature data for Oregon
streams. U.S. Geological Survey Water-Supply Paper 1819-K. 53 pp.

Morhardt, J.£., D.F. Hanson, and P.J. Coulston. 1983. Instream flow: increased
accuracy using habitat mapping. Pages 1294-1304 in Waterpower "83". An
international conference on: hydropower. Conference Proceedings vol. 3:
environmental impacts. Tennessee Valley Authority, Knoxville, TN.

National Climatic Data Center. Periodic. Local climatological data, Asheville,
NC.

National Weather Service. 1973. Relative humidity and dew Point table. U.S.
Dept. of Commerce. National Oceanic and Atmospheric Administration. WS TA
B-0-5A through E. 4 pp. each. [Suffix A is for elevations between 610] and
8500 feet; B is for 3901 to 6100 feet; C is for 1900 to 3900 feet; D is for
501 to 1900 feet; E is for 0 to 500 feet.]

Needham, P.R., and A.C. Jones. 1959. Flow, temperature, solar radiation, and

ice in relation to activities of fishes in Sagehen Creek, California. Ecology
40:465-474,

Neel, J.K. 1951. Interrelations of certain physical and chemical features in
a headwater. limestone stream. Ecology 32(3):368-39].

n4



Smith, K. 1981, The prediction of river water temperature. Hydrol. Sci.
26(1):19-32.

Smith, K., and M.E. Lavis. 1975. Environmental influences on the temperature
of a small upland stream. Oikos 26(2):228-236.

Song, C.C.S., and C. Chien. 1977. Stochastic properties of daily temperature
in rivers. J. Environ. Eng. Div., Am. Soc. Civ. Eng. 103{EE 2 Paper 12858)
Apr: 217-231,

Song, C.C.S., and K.S. Leung. 1978. River temperature variation with freezing
and storage. Am. Soc. Civ. Eng. Proc. [EE5 no. 14054] 104:879-888.

Steele, T.D. 1978. A simple-harmonic model for depicting the annual cycle of
seasonal stream temperatures. USGS Open File Report 78-155. 16 pp.

Steele, T.0. 1983. Harmonic analyses of stream temperatures in the Upper
Colorado River Basin. USGS Water Resources Investigations Report 84-4290.
57 pp.

Stevens, H.H., dr., J.F. Ficke, and G.F. Smoot. 1975. Water temperature,
influential factors, field measurement and data Presentation. Techniques of
water-resources investigations of the USGS. Book 1, collection of water data
by direct measurement, chapter DI. 65 pp.

Swift, L.W., and J.B. Messer. 1971. Forest cuttings raise temperature of smal]
streams in the Southern Appalachians. J. Soil Water Conserv. 26(3):111-116.

Sylvester, R.O. 1963. Effects of water uses and impoundments on water
temperature, 12th Pacific Northwest symposium on water pollution research.
U.S. Public Health Service, Corvallis, OR. pp. 6-28.

Tennessee Valley Authority. 1972. Heat and mass transfer between a water
surface and the atmosphere. Water Resour. Lab. Rep. 14. Norris, TN. 166 pp.

Tenney, A.S. 1987. How do you know how hot it really is? Research/Development
29(2) 2122-126.

Terrell, J.W., T.E. McMahon, P.D. Inskip, R.F. Raleigh, and K.L. Williamson.
1982, Habitat suitability index models: Appendix A. Guidelines for riverine
and lacustrine applications for fish HSI models with the Habitat Evaluation
Procedures. U.S. Fish Wildl. Serv. FWS/OBS-82/10.A. 54 pp.

Theurer, F.D. [1984.] Stand-alone shade model. Computer program and internal
documentation. U.S. Fish and Wild]. Serv., Fort Collins, CO. Unpubl. MS.

Theurer, F.D., and K.A. Voos. 1982. IFG’s Instream water temperature model
validation. Pages 315-318 in Conference on water and energy: technical and
policy issues. ASCE proceedings of the hydraulics conference, Pittsburgh,
PA, and Fort Collins, CO, May 23-26 and June 23-27, 1982.

7



and contact with a cooler channel substrate may be responsible for the rapid
cooling in the shaded areas. However, simple calculations suggested that these
effects would be slight in total, and they could not explain all the phenomena
they observed.

Brown (1970) developed relatively uncomplicated formulae that, used in
combination with some associated tables and nomographs, are meant to assist in
estimating the maximum temperature change (not the change in maximum temperature)
due to clearcutting. He logically points out that since maximum temperatures
will "undoubtedly" occur on cloudless days, "pure" solar radiation as predicted
by simple models (such as SSSOLAR) eliminates the need for collecting detailed
solar and cloud cover measurements. Further, Brown states that topographic
shading can be ignored, since it is largely insignificant during the midsummer
period. (Tests of Brown’s hypothesis using the SSSHADE model indeed showed that
even with east and west topographic altitudes of 25 degrees, midsummer shading
from topography alone could only reach 11% at 40 degrees north latitude using
a stream aspect of zero degrees. As the stream aspect deviated from zero
degrees, the percent shade rapidly approached zero shade.)

Brown’s formulae are simple, containing terms for only the surface area
of the stream, rate of heat input, and stream discharge. He used this technique
to predict temperature changes, following clearcutting, of 16 °C (28.8 °F) within
1 °C (1.8 °F). Even so, he is quick to point out limitations in his approach.
Briefly, the method will not work well in streams with tributaries or with large
daily changes in discharge, and it gives no allowance for only partial shade
removal. Therefore, his technique typically results in overprediction of maximum
temperature changes. This was not the case in results reported by Hewlett and
Fortson (1982) in their experiments with Brown’s model. They found an
unacceptable underprediction of temperature changes, up to 20 °F (11.1 °C), using
Brown’s model, which led them to develop some simple regression techniques for
predicting water temperature changes. Those regressions, however, produced high
standard errors (5 °F, 2.8 °C) and low coefficients of determination.

Though neither detailed nor precise, Hewlett and Fortson’s paper is
interesting in several ways. They present evidence that leaving partial (35-
to 50-foot) buffer strips along the riparian corridor may not always moderate
the stream temperature effects resulting from clearcutting. They suggest that
forest cover reductions in areas of gentle land relief may elevate the
temperature of shallow groundwater moving into the stream. Therefore, shade
should not be considered in isolation from other relevant inputs to whatever
temperature model is chosen for a particular study. They show that although
ground-water temperature measurements in deep wells remain within a few degrees
of mean annual air temperature, the "effluent" ground-water temperature
apparently varied from 43 °F (6.1 °C) in January to 70 *F (21.1 °C) in July.
They suggest that results of other foresters’ demonstrations of near-normal
stream temperatures with retention of 25- to 50-foot buffers on each side of the
stream (Swift and Messer 1971) have been due to steep terrain in which effluent
groundwater is from a deeper origin.

Feller (1981) reported one example of how clearcutting of the shading
vegetation miy increase winter strean. temperatures rather than decrease them,
as would usually be expected. His report is intriguing in that it compares

24



ACKNOWLEDGMENTS

Many people are due credit for this paper. Kay Lindgren and her staff
spent many hours finding and obtaining documents for me. Terry Waddle reviewed
an early draft and helped to firm up the contents. Other reviewers included
Carl Armour, Del] Simmons, Dave Gilbert, Fred Theurer, and Ken Voos.

Special thanks are due to both John Homa and Wayne Lifton for extra-
detailed review and comments. Kenny Dinan and Bill Slauson served to bring a
real-world tone to the manuscript. Jenny Shoemaker and Mary Sanz did a fine job
of making or obtaining the figures, and Trish Gillis made the manuscript
presentable. Jim Zuboy provided a much-needed editorial review. Finally, thanks
to Bob Milhous for pressuring me to do this job in the first place.



APPENDIX E. SELECTED METEOROLOGICAL DATA FOR JULY

The following figures are taken from U.S. Department of Commerce (1968).

Figure E-1. Normal daily average temperature for July.

Shae

132


1027210)
REPORT DOCUMENTATION | 1. REPORT NO. 2 ‘3. Recipient's Accession Wo.
PAGE Biological Report 89(17)

© Title and Subtitle

© fever Date
Stream Temperature Investigations: Field and Analytic Methods | dune 1989
Instream Flow Information Paper No. 13 .

7. Author(s) © Performing Organization Rept. No.
John M. Bartholow

% Performing Organization Name and Address National Ecology Research Center 10, Project/Tesk/Work Unit Na.
U.S. Fish and Wildlife Service
2627 Redwing Road 1 ComtrmentG) oF Grank{a) Me,
Fort Collins, CO 80526-2899 ©
@
12, Sponering Orgnzston Name nd ASSO ty Department of the Interior 13. Type of Report & Period Gavered

Fish and Wildlife Service
Research and Development
Washington, DC 20240 “

18 Supplementary Notes

16 Abstract (Limit: 200 words)

This document provides guidance to the user of the U.S. Fish and Wildlife Service's
Stream Network Temperature Model (SNTEMP). Planning, executing, and using the results
from a stream temperature modeling study are discussed. Details of field data gathering,
instrumentation, and data collection priorities are given for the range of stream geometry
meteorology, and hydrology components necessary for the model's application. Each input
variable is defined, and its relative data collection effort is approached from the
perspective of sensitivity in predicting stream temperatures. Where measurements may not
be required, techniques for estimation are given. Ranges of values for certain input
variables that are difficult to measure or estimate are given. Pertinent literature is
cited for each variable. Model calibration, verification, and validation steps are
defined and outlined, with measures of "goodness-of-fit" given for comparing simulated
stream temperatures with observed values. Alternative public domain stream and reservoir
temperature models and techniques are contrasted with SNTEMP.

Appendices provide information on State and Federal agencies that are good data sources,
vendors for field instrumentation, and small computer programs useful in data reduction.

37. Document Analysis 8.
Mathematical models
Water resources

b. Identiters (Open-Ended Terms

Temperature models, thermal habitats, field techniques, meteorology, validation,
sensitivity analysis, calibration, regression, thermography.

©. CORATI Field/Group

UR Avaitabltty Statement 19. Securky Class (Thia Repord 2 No. of Pages
Release unlimited Unclassified 139
20. Security Clase (This Page) 7 Price
Unclassified
(See ANSIE9.1) Tee instructions on Reverse ‘OPTIONAL FORM 272 (6-77)

90.8, COVENOGOT PRIWTING OFFICE: 1995-676-063/25093 Department of Commerce



1. The data must be recorded as accurately as possible, with a range
covering the anticipated ranges encountered.

2. The equipment must maintain its calibration over extended time
periods.

3. It must operate unattended for a long time, e.g., one month.

4. It must use a minimum of electrical power. Long-life batteries
provide much more mobility.

5. All other things being equal, the smaller the size the better, so
you can install (hide) it in more places, and the more rugged the
better.

6. Al] other things being equal, data collected should be in the target
units, with a visual display for checking.

In any such effort, a number of problems can be expected. Though old by current
equipment standards, Anderson (1955) presents a figure from the then famous Lake
Hefner Studies that illustrates many results (Figure 17). In this figure,
"usable" refers to a level of hourly data collection that was deemed acceptable
for this particular study. You will need to make your own standard for what is
"usable." The "perfect" refers to no equipment malfunctions for an entire 24-
hour day. This figure shows that data collection will likely improve with time
and experience with the instrumentation. It also shows that data collection in
the winter months is more likely to fail and will need increased frequency of
attention to the equipment.

Appendix B lists some vendors from which permanent meteorological stations
may be obtained.

Air Temperature

Crisp and Howson (1982) found that they could explain 86%-96% of the
variance in water temperature by regressions containing solely mean air
temperatures measured as far as 50 km away, as long as the air temperature was
above freezing. Other authors (Smith and Lavis 1975; Song and Leung 1978) have
noted the same profound linkage. Since air temperature is the single most
important (sensitive) parameter in the absence of other thermal inputs, it
deserves special attention and effort in getting data that is truly representa-
tive of the entire area being studied.

Air temperature is important because it plays a part in most of the heat
flux components, especially atmospheric radiation, evaporation, and convection.
As with all parameters needed to calibrate any water temperature model, air
temperature data may come from a variety of sources. Air temperature may of
course be measured in a manner much like that for water temperature. Thermom-
eters ranging from simple mercury to digital thermographs are available at a wide
range of prices.

39



SCO OD NM Hm eB w

-

D-1
0-2
D-3
D-4

TABLES

Relative sensitivity of maximum and mean water temperatures to
various parameters for a generalized stream ...................
Range of input values used to determine relative sensitivity
OF SNTEMP o 1... eee cece eeceeeeueevereens Peet eee cece eens

ita elements from two forms of Local

Availability

Climatological Data Summaries .......... Penance ee eececneeeees
Percent reflectivity for various homogeneous ground cover
CONGILIONS 20... 00. e cece eee eeee sees

you may find water temperature data
in USGS publications or data bases .........c.cccccceecescccece
One form in which you may find water temperature data from a
STORET data base ...............,
Example thermograph specifications
Average size, depth, and temperature depressions of the
water areas found by Bilby in a Washington stream ..
Textural output file from Hydrodata for mean daily w.
temperature ...........cccceeeeeeeeee
Textual output file from Climatedata fo
temperature ..............005

y Hydrodata Optical

Disk Database ..
Textual output file

for streamflow ..

viii

10
20
36

51
52

56

61
63

79
127
129

130
131



1005

80 ~+

60 4 °

404

PERCENT SHADE
°
°

20-

id T T T T 1
o 20 40 60 80 100

BUFFER STRIP WIDTH (ft)
Figure 14. Influence of buffer strip width on shade density. As the buffer

gets wider, the shade value reaches a "saturation" density. Adapted from
Lafferty (1987).

The average shading would be determined by the formula:

= A(x) + Bly) + C(z)
Mean Shade % 100

where A = percentage of "full sun” observations
B = percentage of "filtered sunlight" observations
C = percentage of "shaded" observations

x = 100 (full sun)

y
z2 = 7 (shaded)

50 (filtered)

For example, tf 10% of the total observations were direct sun, 25% were filtered,
and 65% were complete shade, the mean shade would be:

Mean Shade % = 10(190) + 25(50) + 65(7) = 2105, a7

100

33



Jowett, I.P., and M.P. Mosley. 1983. River temperature modeling. In
Streamland, 14. National Water and Soil Conservation Organization, Water and
soi Division, Ministry of Works and Development. Wellington North, New
Zealand. 4 pp.

Kaya, C.M., L.R. Kaeding, and D.£. Burkhalter. 1971. Use of a cold-water refuge
Pe and brown trout in a geothermally heated stream. Prog. Fish-Cult.
39(1):37-39.

Kothandaraman, V., and R.L. Evans. 1970. Annual temperature variations in an
impoundment in Central Ilinois. J. Water Works Assoc. 62:639-642.

Krajewski, W.F., A.K. Kraszewski, and W.J. Grenney. 1982. A graphical technique
for river water temperature predictions. Ecol. Model. 17:209-224.

Laenen, A., and R.P. Hansen. 1985. Preliminary study of the water temperature
regime of the North Santiam River downstream from Detroit and Big Cliff Dams,
Oregon. U.S. Geological Survey. Water-Resources Investigations Report
84-4105. 45 pp.

Lafferty, B. 1987. A procedure for evaluating buffer strips for stream
temperature protection under the forest practices act. Pages 70-77 jin
Managing Oregon’s riparian zone for timber, fish, and wildlife. NCASI Tech.
Bull. No. 514. 96 pp.

Latimer, J.R. 1972. Radiation measurement. International Field Year for the
Great Lakes Technical Manual Series No. 2. Atmospheric Environment Service,
Environment Canada. The Secretariat, Canadian National Committee for the
International Hydrological Decade, No. 8 Building, Carling Avenue, Ottawa,
Canada. 53 pp.

Leffler, R.J. 1981. Estimating average temperatures on Appalachian summits.
J. App. Meteorol. 20(6):637-642.

Leggett, R.W., and L.R. Williams. 1981. A reliability index for models. Ecol.
Model. 13:303-312.

Lemmon, P.E. 1956. A spherical densitometer for estimating forest overstory
density. For. Sci. 2(1):314-320.

Lifton, W.S., K.A. Voos, and D.A. Gilbert. 1985. The pit 3, 4 and 5 hydro-
electric project using the USFWS instream temperature model. Presented at the
Waterpower ‘85 International Conference on Hydropower. Am. Soc. Civ. Eng. ,
Las Vegas, Nevada. 11 pp.

Linsley, R.K., Jr., M.A. Kohler, and J.L.H. Paulhus. 1975, Hydrology for
engineers, 2nd ed. McGraw-Hill Book Co., New York. 482 pp.

List, R.J. 1951. Smithsonian meteorological tables. Smithsonian Miscellaneous
Collections Volume 114. Publication 4014. Washington, DC

113



Previous authors suggest that factors other than flow largely dictate
water temperatures (Jowett and Mosley 1983; Laenen and Hansen 1985). Few authors
have dealt with the sensitivity of water temperature models to a variety of
parameters’ (Moore 1967; Crittenden 1978). Crittenden’s (1978) sensitivity
analysis differs from my example and from other authors’ in several respects.
First, he varied only a single parameter at a time, and second, the model he used
was developed solely for predicting equilibrium temperatures in small, unshaded,
low gradient streams with little groundwater inflow. His results indicate that
wind speed and the thermal properties of the substrate are the two most sensitive
parameters. I find these conclusions suspect because these two parameters were
varied over two orders of magnitude in the case of wind and one order of
magnitude for thermal diffusivity. I do not believe these are reasonable
variations for "real world" applications.

In summary, I strongly advise that a sensitivity analysis, even if crude,
be performed prior to any field work or other data collection to determine which
Parameters deserve special attention. Do not take the examples given here as
necessarily indicative of your situation.

DATA GATHERING AND FIELD TECHNIQUES

Armed with a general knowledge of which parameters are most likely
controlling water temperature, we can proceed to the discussion of individual
model parameters--what they mean and how to estimate them, Our discussion will
be divided into three major groups: stream geometry, meteorology, and hydrology.

STREAM GEOMETRY COMPONENTS

Elevations, distances, and stream widths are fundamental stream geometry
measurements. These get respectively more sensitive and also more difficult to
calculate accurately.

Elevations

Elevations are important in temperature modeling for (1) calculating the
slope resulting in heat from friction, (2) calculating the atmospheric pressure,
an important element in heat convection, (3) calculating the depth of the
atmosphere through which solar radiation passes, and (4) translating known air

ee

When reviewing their work, it is well to remember that there may not be an
adequate distinction between air temperature and solar radiation. Accordingly,
there may be confusion between proximate and ultimate causes in the sense that
short wave solar radiation warms the air, which in turn emits long wave thermal
radiation. In terms of heat flux, atmospheric radiation dominates most of the
time, especially in the summer. Occasionally, the sensitivity discussion in the

literature must be interpreted as sensitivity of maximum daily water tempera-
tures, not mean daily.

12



Sensitivity analysis of deterministic models is a valuable step in any
model application. There are several specific uses for sensitivity analysis,
some for the model builder and tester, and some for the model practitioner.
Sensitivity analysis may be used to (1) serve as an aid in confirming that the
model is consistent with theory, (2) show the effect that errors in each
parameter have on the dependent variable (water temperature), (3) identify those
parameters that are sensitive to the degree that they warrant very reliable
measurement, and (4) show the relationship between the parameters subject to
Management contro] and the dependent variable (Reckhow and Chapra 1983). For
our purposes, it will be valuable to learn where to concentrate data collection
efforts, and how to display the effect that changes in flow, riparian shade, or
channel characteristics have on stream temperature.

There are many ways of performing a sensitivity analysis on deterministic
models. A common approach is a test in which a single parameter is systemati-
cally varied, while other parameters are held constant, and the response of the
dependent variable is monitored. This allows us to say, for example, "A unit
change in X produces a 2% change in stream temperature.” A disadvantage of this
technique is that it does not allow the practitioner to say what portion of the
variance is attributable to a single parameter if the other parameters are also
changing.

Table 1 illustrates the relative sensitivity of the key parameters used
as input to most temperature models. This analysis was performed for the SNTEMP
model, many major components of which are illustrated in Figure 2, but the
results would be expected to be similar across other deterministic stream
temperature models. This table was generated by systematically varying the input
parameters and noting the conditions associated with maximum changes in both mean
and maximum water temperatures. This method gives a more robust picture of true
sensitivities than varying a single parameter for only one set of other
variables. It does not, however, explicitly consider the cross-correlation
between parameters.

The parameters in Table 1 are ordered down the page from most to least
sensitive for the generalized stream being simulated. Other streams will behave
differently, but the general pattern should remain relatively stable. There are
some obvious exceptions, however, such as the case of water temperatures
immediately downstream of a reservoir, where the primary influence on temperature
is the release temperature itself.

Parameters were varied for a generalized stream segment for an idealized
July condition. The high and low values chosen to characterize this stream are
shown in the Table 2. Items not shown in Table 2 were held constant; these
values are: lateral flow, zero; upstream elevation, 100 feet; downstream
elevation, zero feet; segment length, 10 miles; width’s B value, .2; day length,
14.5 hours; and dam at inflow, true. Clearly, a headwater stream or a large
river’s parameters would be different.



CONTENTS

SUMMARY ...
FIGURES .
TABLES ....
ACKNOWLEDGMENTS .

INTRODUCTION ...... secesereseees 1
PLANNING A TEMPERATURE STUDY . 5
UNDERSTANDING WATER TEMPERATURE THROUGH SENSITIVITY ANALYSIS 6
DATA GATHERING AND FIELD TECHNIQUES 12
Stream Geometry Components . 12
Meteorological Components 34
Hydrology Components ... 51
Water Temperature Componet 54
Special Cases ............0005 74
SIMULATION AND ANALYSIS “TECHNTQU 85
Alternative Predictive Techniques” 85
Calibration/Validation ......... 89
Other Stream Temperature Models 96
Special Cases 101
LITERATURE CITED 2.0... ..sceceeeeeeee cet eee eet eneeaeeeereeneneeterenanees 107
APPENDICES
Data Sources ..... 120
& Equipment Vendors 121
C. Program Snippets ... 123
0. Alternative Data Formats from the Hiydrodata Data Base 127
E. Selected Meteorological Data for July . 132



1 REM PROGRAM HUMID.BAS -- COMPUTES DEW POINT AND RELATIVE HUMIDITY
2 REM ADAPTED FROM Blackadar, A.K. 1983.

3 REM USING HOME COMPUTERS TO STUDY THE WEATHER

4 REM WEATHERWISE, AUGUST 1983, P195

10 PRINT "ENTER THE DRY-BULB TEMPERATURE”

20 INPUT T

30 PRINT "ENTER THE WET-BULB TEMPERATURE"

40 INPUT Tl

50 PRINT "TYPE ‘C’ OR “F/ TO INDICATE CELSIUS OR FAHRENHEIT"
60 INPUT US

70 PRINT "ENTER HEIGHT ABOVE SEA LEVEL IN FEET"

80 INPUT H

90 IF US = "C" OR U$ = "c" GOTO 120

100 T= (T - 32) * 5/9

110 Tl = (Tl - 32) * 5/9

120 T = T + 273.15

130 Tl = Tl + 273.15

140 EL = EXP(21.4 - 5351 / T1)

150 P = 1014 - (H / 2900) * 100

160 E = El - P* (T - T1) / 1555

170 D = 5351 / (21.4 - LOG(E))

180 D = D - 273.15

190 IF US = "C" OR US = "Cc" GOTO 210

2000 =D*9/5 + 32

210 E2 = EXP(21.4 - 5351 / 1)

220 R= 100* E / £2

230 Q = .622* E / (P - .378 * E)

240 PRINT

250 PRINT ” ANSWERS”

260 PRINT

270 IF US = "C" OR US = "c" GOTO 300

280 PRINT "DEW POINT = "; D; " F."

290 GOTO 310

300 PRINT "DEW POINT = "; D3" C."

310 PRINT "VAPOR PRESSURE ="; E; “MB.”

320 PRINT "SATURATION VAPOR PRESSURE = "; £2; " MB."
330 PRINT "RELATIVE HUMIDITY = "; R; " PERCENT.”
340 PRINT "SPECIFIC HUMIDITY = "; 1000 * Q; " PARTS PER THOUSAND."

125


this example, the reservoir release temperature is not expected to vary, nor will
the stream geometry (e.g., shading, width) vary. The meteorological conditions
(air temperature, relative humidity, solar radiation, wind speed, percent
possible sun), however, will be naturally varying.

There are at least two specific management questions that could be asked.
First, what is the probability of exceeding a certain water temperature? For
example, with a constant release of 25 cfs, how many days during the 60-day
period are we likely to exceed 20 °C (68 °F)? Second, what flow is necessary
to keep water temperatures from exceeding a specified threshold on any day within
the 60-day period?

The first question is the most demanding to answer. One method is to
gather and enter 30 years (the meteorological standard for "normal") worth of
meteorological data for that 60-day period, run the simulation with the
caltbrated model, and tabulate the water temperature frequencies. Another method
is to gather the meteorological data and develop joint frequency distributions
for each parameter such that a Monte Carlo type simulation may be developed
(Richardson 1981). Both methods are costly to develop, but may be warranted in
Situations demanding a rigorous answer.

The second question is probably more tractable if one is willing to live
with a somewhat less accurate answer. First, refer to the sensitivity analysis
performed during calibration to determine which parameters can be safely ignored.
For example, using the standards from Figures 3 and 4, we might determine that
averages for percent possible sun, solar radiation, and wind speed are
acceptable. (Alternatively, since maximum temperatures are the issue, one might
use 100% possible sun and maximum solar radiation.) However, air temperature
and relative humidity appear too sensitive and variable to simply average.
Inspection of the record shows that mean air temperatures commonly exceed 27 °C
(80.6 “F), with occasional extremes of 32 °C (90 °F) (Figures 35 and 36).
Relative humidity for the same period commonly varies from 40% to 80%. Simple
trials with the model show that low air temperature and low humidity produce the
lowest water temperatures; high air temperatures and high humidity produce the
highest water temperatures; and the other combinations (high air temperature-
low humidity, low air temperature-high humidity) produce virtually identical
water temperatures. Furthermore, inspection of the record also shows that the
highest air temperature never occurs in combination with high humidity, thus a
purely "worst case" is beyond reality. So what is a Person to do?

A practical response would seem to be that, like the development of
conditional probabilities, we perform a regression or other fitting technique
between air temperature and relative humidity. Air temperature should be the
independent variable because it is the more sensitive of the two. Use this
relationship to estimate the relative humidity that would occur with the highest
(or 5% exceedence) recorded air temperature. The standard error of the estimate
may be used to increase this humidity level if desired. Your predictive analysis
ghould then include the highest air temperature and its associated relative
jumidity.

103



Water temperature data obtained from other sources should always be
somewhat suspect. Independent verification may be in order by limited field
sampling. After all, the whole basis for any temperature model calibration/
validation assumes that the observed temperatures are measured accurately and
without bias.

Instrumentation

Thermographs of all types are readily available from several vendors (see
Appendix B), and most decisions relate to how many instruments are to be deployed
at a time, initial and incremental costs of each instrument, and personal
preference. Many people, for example, still prefer strip chart or film type
recorders. They are relatively inexpensive, reasonably rugged, and commonly
available. Their biggest problems, being mechanical instruments, come from
failure of moving parts, drying of ink, and torn media. Also, they may not have
a wide enough recording range for a multiseason study.

Digital recorders are newer devices that clearly represent the trend in
temperature measurement (Gile 1986). They come with their own set of advantages
and disadvantages. Usually more expensive (e.g., $950) than thermographs,
digital recorders store readings in some form of memory (called RAM or EPROM) ;
they have better accuracy, little need for calibration, and are smaller and
lighter, but they require microcomputer involvement to get your data "out." Some
of these units require battewies to "remember" their data, and some do not. The
battery units are typically less expensive, but battery problems can and do
happen. So called nonvolatile units will at least remember what they have stored
before a battery problem arises. One must either take a micro to the field, be
willing to swap units, be without a unit for a time, or purchase more expensive
units with swappable memory chips. With digital recorders there is typically
no need to send anything for off-site digitizing or other data reduction
technique.

Before you buy, draft a set of specifications (Table 9).

No matter whether analog or digital units are used, one also has the choice
of in situ or remote placement. In situ is probably preferable in streams where
vandalism is a concern, but in situ placement runs a greater risk of loss of
the unit in high water, physical damage, leakage, or being covered with silt or
debris. In situ also requires a "dunk" to install, test, and replace the unit.
In contrast, a remotely mounted unit with a cable leading to a thermistor
requires clever concealment, but it is more easily serviceable and requires a
less costly housing, weatherproof rather than waterproof (Figure 25).

62



ry
=}
i=)

SUBREACH
A

SUBREACH

TRAVELTIME, INHOURS
&
7

30F

25r

20F

16

@ November 14, 1967

10F A June 7, 1968 4
i r © September 25, 1968 4
6r 4
5 pe

ai A 1 on
1 156 2253 4 5678910 15 20 2530
DISCHARGE, IN CUBIC METERS PER SECOND

Figure 6. Variation in travel time versus discharge for selected sites along
a stream. Reproduced from Hubbard et al. (1981).

7



elevation, 7, 12, 13, 26, 35-37, 41,
42, 75, 76, 81, 114, 129

empirical, 14, 16, 41, 76, 86, 96,
97, 99-102, 116

epilimnionic temperatures, 98

error, model, iii, vii, 6, 7, 13,
16, 18, 20, 21, 24, 43, 45, 46,
65, 68, 70-72, 75, 86-88, 92-99

estimates, parameter, 16, 18, 24,
26, 32, 41, 43, 45, 46, 50, 71,
73, 74, 78, 83, 86, 87, 92, 101,
113, 115, 119

evaporation rate, 39, 69, 96

extreme cases, 23, 36, 54, 59, 79,
84, 90, 93, 100, 101, 103, 105

failure of equipment, 23, 39, 48,

62, 63

filters, 100

flood, 1, 76

flow, iii, vi, vii, 1, 5, 6, 7, 8,
9, 10, 12-16, 18, 19, 21, 26,
27, 29, 36, 51, 53, 54, 65, 67-
71, 73-87, 96-103, 105, 108,
109, 111, 112, 114, 116-119,
123, 126

fluorescein dye, 18

fluorometer, 18

flushing, 98

flux, heat, 12, 13, 20, 21, 39, 46,
74, 97, 99

forebay, 67

forecast, 35, 105

gage, 18, 53, 55, 67, 127, 131

geological, 53, 55, 57, 112-115, 120

geometry, stream, iii, v, 6, 12, 73,
85, 88, 100-103

geothermal areas, 48, 84, 113

goodness-of-fit, Vii, 90, 92, 93,
116

gradient, stream, 8, 10, 12, 13, 19-
21, 48, 84

ground, vi, viii, 10, 13, 20, al,
24, 36, 41, 48-51, 70, 71, 76,
79, 84, 92, 111

ground reflectivity, 48, 50

ground temperature, vi, 8, 10, 20,

groundwater, 12, 23, 24, 48, 50, 51,
53-55, 70, 71, 73, 80, 92

habitat, stream, iii, 6, 13, 18, 54,
79, 81, 92, 108-111, 114, 115,
117, 118

HABTAT program, 14

HAQF data file, 14

harmonic models, 75, 76, 87, 99,
100, 117

headwater, 7, 36, 65, 80, 114

HEC program, 78

height of vegetation, vi, 27, 30,
41, 43, 46, 47, 125

historical conditions, 53, 55, 85,

hourly’time step, vi, 21, 25, 35,
39, 41, 63, 96, 98, 109, 118,
123

humidity, vi, vii, 8, 10, 13, 36,
41-44, 55, 86, 103, 105, 114,
123, 125, 127, 133

hydraulic, 16, 18, 101, 102

HYDRODATA data base, v, viii, 53,
55, 120, 127, 130, 131

hydrology, iii, v, 12, 51, 77, 78,
88, 92, 100, 102, 111, 113, 115,
120, 127, 131

hydropeaking, 98

ice, iii, 21, 51, 68, 74, 76, 110,
114

impoundment, 18, 75, 76, 109, 113,
1

inflow, 7, 8, 10, 12, 48, 53, 54,
75, 77, 80, 92

instrumentation, iv, 39, 62, 96,
107, 122

intergravel flow, 80

irrigation, 53, 55, 70, 71

Julian days, 87, 99, 123



“TEMPERATURE-LIMITED” TROUT HABITAT IF
GLOBAL WARMING OF 2.7°C

Figure 1b.
scenarios:

Approximate "temperature-limited" geographic range of rainbow trout under three
assuming a 2.7 °C (5 °F) uniform global warming.



Figure E-4. Mean daily solar radiation for duly.

ILY SOLAR | RADIATION ‘angleys),
rt



MAXIMUM

MINIMUM

CHANGE IN WATER TEMPERATURE (°C)

JF MAOMJ JASON D

MONTHS

Figure 8. Example of diel change in water temperature through time following
clearcutting. Adapted from Hewlett and Fortson (1982).

Much of the existing literature dealing with the effects of shade on water
temperature comes from foresters’ attempts to quantify the results of different
clearcutting practices on stream temperature. The primary emphasis has been
since the passage of the Federal Water Pollution Control Act as amended in Public

22



Full "Split Set" Calibration/Validation Scheme

Other
Records

Quality
Control

Field
Data

Quality
Control

Measured Conditions

"Data Base"
Entry

Quali

Calibration Validation
Set Set
Sensitivity Model Parfo
‘ Parameter erformance
Analysis & Validation rma
Calibration ( Transfer ) ROaLOI Statistics
Combine
Data
\ Sets \
Recalibrate
Pooled Data

Frequency Synthetic
Analysis Data Entry

Figure 33. Full “split set" calibration/validation scheme. Adapted from Ken
Voos (pers. comm.).



Figure E-2. Mean relative humidity for July.

Figure E-3. Mean percentage of possible sun for July.

133



From simple experimental calculations, it would appear that this formula is very
Conservative in its estimation for truly complete mixing by overestimating the
distance in small streams. For example, using the same situation as above with
an average velocity of 3 ft per second and a 1% gradient:

2
40.062) (3) (100°) = 7,440 feet
(5°) (.01)

One might hope that a reasonable answer may be bounded by these two calculation
methods. As always, there is no substitute for field verification.

L=

Not only do stratifications, seepage, and thermally segregated waters
produce measurable temperature differences, but also it is well documented that
fish take advantage of them. Gibson (1979) used skin diving techniques to
observe salmon parr moving into a spring seepage of 17 °C (62.6 °F) when the main
river temperature rose above 22 °C (71.6 °F). They were physically oriented
towards the source of the cool water and showed no apparent territorial behavior.
Kaya et al. (1971) documented trout moving into the mouth of cool-water streams
in a geothermally active area of Yellowstone Park.

Though the focus of the above studies has been towards the areas of cool
water during times of otherwise high temperatures, the other end of the spectrum
needs to be examined as well. During the winter, ground-water effluent,
undergrave) flow, tributary mouths, and other locally heated areas can offer
respite from very cold water with similarly documented concentrations of fish
(Kaya et al. 1971; Coutant et al. 1984).

It is unlikely that cost-effective models that can predict thermal
microhabitats will be available for some time. About the best that can be done
is to take spot temperature measurements at low flow to see if stratification,
ground-water seepage, or undergravel flows do occur and to what extent. You need
to be aware that such areas exist and that mobile aquatic organisms do seek
refuge in times of thermal stress, both when ambient waters become too hot or
too cold. It would be nice to determine the spatial and temporal uses of aquatic
species in differing thermal microhabitats in the field. I am aware of
temperature sensitive ultrasonic fish tags (Rochelle and Coutant 1973), but
unaware of specific results of using such tags.

The most effective and accurate way to make microthermal measurements is
with a thermistor probe. Bare-legged wading may show where these areas are
located; then use the probe to make very localized and undisturbing temperature
measurements. If such a probe is not available, a complete immersion thermometer
should be used, with the instrument turned horizontally such that its entire
length is exposed to a constant temperature (Neel 1951).

Though these microhabitats do exist, and offer the best refuge in times
of temperature extremes, don’t rely on their existence. Existing field data
Suggest that they are too thinly spread (Bilby’s 1.9% of total stream area and
Ozaki‘’s 9% of all pools) to support a true fishery, though they may offer hope
for repopulat on after extreme events.

84



“TEMPERATURE-LIMITED" TROUT HABITAT IF
GLOBAL WARMING OF 5.6°C

Figure lc. Approximate "temperature-limited" geographic range of rainbow trout under three
scenarios: assuming a 5.5 °C (10 °F) uniform global warming.



observed situation, then we must assume the model is to some degree (theoreti-
cally) inconsistent with the real world and hence retreat to the process of model
formulation. This implies that we have a dynamic understanding of the system
being modeled and that we are really in an experimental process (Lee 1973).
Again, I will not deal with this issue; refer to an excellent description of the
model-building process called “invalidation” by Holling et al. (1978), which is
approached as finding the (confidence) limits of model credibility, both under
normal and extreme conditions.

There are several criteria that may constitute the verification process.
These criteria are not fixed and rigidly defined, but rather depend on the
objectives of the study. Accuracy is always the first criterion that comes to
mind. Undoubtedly, one’s trust in the application of a model will increase if
the model is capable of faithfully reproducing the current or historical
situation. I will also discuss certain statistical measures of goodness-of-fit.
Calibration/verification may also serve in determining the model’s overall
sensitivity to changes in parameter values and may suggest additional data
collection to replace estimated parameters with measured ones.

The term "validation" will for the moment be narrowly constrained to
describe the quantitative measures of goodness-of-fit between a parameterized
model’s output and the observed system. There are two primary ways in which
this may be done (Reckhow and Chapra 1983). The first is most useful in
validating process-oriented simulation models; one may calibrate a model for a
subset of the data, carry the Parameters thus determined to the other data subset
and test the unadjusted model’s performance using goodness-of-fit criteria
(Figure 33). For example, you could calibrate a model based on the year 1985,
then test it’s performance for the years 1986 and 1987.° The second method is
similar and most useful in regression-type models. In this method, one may
divide the entire data set into two relatively equal parts, each with the full
complement of variation. Both sets are then independently calibrated,
Parameters determined for the first data set may be compared to parameters
derived from the second data set using goodness-of-fit criteria. If not
significantly different, the models are confirmed (validated). Care needs to
be exercised here such that the two data sets are not really the same due to high
autocorrelation. You would not want to divide a daily time-step model into
every-other-day sets, whereas every-other-week would probably work out well.
Each day is usually highly correlated with the day, before, whereas each week is
Tess likely to be correlated with the week before.’ In fact, the more different

eS

STypically, the subset of the data used for calibration purposes has been a
subset in time; however, the possibility exists that a spatial subset may also
be employed in the sense of using a parameterized temperature model from one
watershed to neighboring watersheds.

“It has been shown that the maximum weekly average temperature, computed from
running 7-day averages, can be closely approximated by a pure weekly time step
within 1.4 °C (Ferraro et al. 1978). How general these results are, I don’t
now.

90



While probably not important for data sets reflecting a month-long time-step,
it would seem that the method outlined by Ringuest would be an improvement to
Theurer’s approach.

Another technique that has been advocated in the literature as a
“reliability index" for simulation models is that of Leggett and Williams (1981).
Gordon (1981) used similar measures. This method computes a percent deviation
statistic that is supposed to measure the overall reliability of a model. This
approach, however, implies that we can tolerate larger errors at higher
temperatures because they are based on percent error (as in Figure 34f), which
is obviously not the case.

It is not clear to me that many of the improvements advocated in the
literature are warranted in the face of observed data that clearly violate the
assumptions for the most commonly used statistical tests, such as independence
of the observed data (autocorrelation) and measurement without bias. The problem
of autocorrelation is substantially increased when using daily time-step
simulations (yesterday’s high temperature influences today’s minimum, which in
turn influences today’s average temperature). In addition, we know that there
are errors in the estimation or measurement of model input values, and errors
in observed system response (water temperature). Finally, the errors usually
do not have zero mean, normal distribution, and constant variance. Thus, it is
certainly incorrect to ascribe all differences between predicted and observed
water temperatures as model errors. In addition, the issue of validating the
SNTEMP model is clouded if the internal regression models have been used to fill
or smooth extensive data gaps.

Model users should be especially skeptical of the observed data when major,
unexplained differences between observed and simulated values occur (Theurer
1982; Donigian 1983). For example, Waddle (1987) repeatedly showed that the
SSTEMP model, in conjunction with a dose of skepticism, was quite capable of
identifying errors in observed data.

In addition, there are, or should be, questions related to the representa-
tiveness of observed temperature data. If "validation" temperatures are measured
1 km below a reservoir, the measurements will not provide nearly as robust a
measure of validity as measurements taken 30 km downstream. (Of course the
critical question really revolves around where the biological effects will be
felt, but you get the idea.)

The whole subject of model validation, especially for the type of model
we are using, is a complicated matter. For further study of these confirmation
issues, the ambitious reader is referred to Chapra and Reckhow (1983), Reckhow
and Chapra (1983), and Reckhow et al. (1986) for fairly exhaustive treatment.
The dubious reader may refer to an interesting paper called "Statistical Analysis
and the Ijlusion of Objectivity" by Berger and Berry (1988).

The bottom line is that no matter how well you have "validated" your model,
employment of that model with an informed skepticism of both the model and the
observed data will improve its use (Donigian 1983). I can say with little doubt
that we are underestimating the prediction errors, but I do not know how much.
This will have to remain the case until there is a well-accepted validation

95



Forsythe, W.E. 1954. Smithsonian Physical tables. Smithsonian Miscellaneous
Collections Volume 120. Publication 4169. Smithsonian Institution,
Washington, DC.

Furman, R.W., and G.E. Brink. 1975. The national fire weather library: what
it is and how to use it. USDA For. Serv. Gen. Tech. Rep. RM-19. Rocky Mt.
For. Range Exp. Stn., Fort Collins, CO. 8 pp.

Geiger, R. 1965. The climate near the ground. Harvard University Press,
Cambridge, MA. 611 pp.

Gibson, R.J. 1966. Some factors influencing the distribution of brook trout
and young Atlantic salmon. J. Fish. Res. Bd. Can. 23(12):1977-1980.

Gile, D.E., Jr. 1986. Temperature sensors: how they work, where they are
used. Power 130(4):191-194.

Gilroy, E.J., and T.D. Steele. 1972. An analysis of sampling-frequency
alternatives for fitting a daily stream-temperature model. Pages 594-608 in
International symposium on uncertainties in hydrologic and water resource
systems proceedings.

Goodall, C. 1983. Examining residuals. Pages 211-246 in D.C. Hoaglin, F.
Mosteller, and J.W. Tukey, eds. Understanding robust and exploratory data
analysis. John Wiley, New York. 447 pp.

Gordon, J.A. 1981. LARM two-dimensional model: an evaluation. Amer. Soc.
Civ. Eng., J. Environ. Eng. Div. 107 (#EES) :877-886.

Gosink, J.P. 1986. Synopsis of analytic solutions for the temperature
distributions in a river downstream from a dam or reservoir. Water Resour.
Res. 22(6):979-983.

Gray, D.M., ed. 1970. Handbook on the principles of hydrology. Water
Information Center, Port Washington, NY. v.p.

Gunnison, 0., J.M. Brannon, and R.L. Chen. 1986. Reservoir site preparation:
summary report. Technical Report E-86-4, U.S. Army Engineer Waterways
Experiment Station, Vicksburg, MS. 48 pp.

Hamelink, J.L. 1979. A proposed method for deriving effluent limits from water
quality criteria. Pages 122-127 in K.L. Dickson, A.W. Maki, and J. Cairns,
dr., eds. Analyzing the hazard evaluation process. Proceedings of a workshop
held in Waterville Valley, NH, August 14-18, 1978,

Hamilton, K., and E.P. Bergersen. 1984. Methods to estimate aquatic habitat
variables. Colorado Cooperative Fishery Research Unit, Fort Collins, CO.
v.p.

Henriksen, J. 1988. Technical assistance - before or after. Instream Flow

Chronicle 5(1):1-2. Colorado State University Office of Conference Services,
Fort Collins.

m1



Table 7b. (Concluded)

22.0 24.0 23.8 24.0 26.5 25.5 26.0 23.0 22.5 23.0
22.0 24.0 23.0 23.5 27.0 26.0 26.5 22.5 21.5 22.0
22.5 24.5 23.5 24.0 26.0 25.0 25.5 22.5 21.5 22.0
23.0 24.5 24.0 24.5 25.0 28.5 25.0 22.0 © 22.0 22.0
23.5 24.5 24.0 26.0 25.5 24.5 25.0 22.0 21.5 21.5

24.0 24.0 24.0 25.5 25.0 25.0 == Oo
23.5 19.5 22.0 25.0 21.5 23.0 27.0 23.0 25.0 2.5 21.8 24.0

27.0 8.5 17.5

8S



+9

Figure 25.

In situ and remotely configured thermographs.


MY

HEAT FLUX SOURCES
» L
/ = =
ATMOSPHERIC RADIATION v ~
Aap
7
SOLAR RADIATION
SCATTERING ~
ATER BACKRADIATION
° IPARIAN
VEGETATION
RADIATION

REFLECTION

ALSO: (1) HEAT LOSS DUE TO
EVAPORATION

(2) HEAT GAIN DUE TO
FLUID FRICTION

(3) HEAT EXCHANGE DUE TO
AIR CIRCULATION (CONVECTION) rae aupeD CONDUCTION
Adapted from

Various ways in which heat either enters or leaves a flowing stream.

Figure 2.
Theurer et al. (1984).



the two sets of data are, and the greater the variety of conditions from one to
the other, the more confidence the modeler should have in the predictive
capability of the model. In either case, upon successful confirmation, the data
subsets should be recombined and the parameters re-estimated so that all of the
data can be used in the final calibration process. However, I would stress that
neither of these calibration/validation methods must be used all the time; the
need, or lack thereof, is directly related to the objectives of your study. If
models of this type have been successfully validated in similar settings, you
may limit yourself to calibration and verification only.

When faced with "new" data, previously calibrated models may exhibit
strange behavior. You may be asking the model to operate outside the “domain"
of its calibrated parameters. This is especially true for purely statistical
models, and becomes even less true for the very best process oriented models.
As used here, therefore, validation may be considered a component of the basic
scientific method, in which hypotheses are tested and either accepted or rejected
on the basis of predetermined, statistically rigorous, criteria (Sanders 1985).
It is worth mentioning that if the model (hypothesis) is not rejected, it is not
truly "valid," but rather it is “confirmed” or "corroborated" in the sense that
the hypothesis is compatible with the current evidence. No model is "valid"
because no model is completely "true" (Reckhow and Chapra 1983).

When properly validated, a model may be expected to adequately predict
future outcome from a set of specified input data if we can reasonably expect.
the model’s parameters to exhibit a degree of constancy over a broad range of
space and time. It is important that we critically examine this aspect of
modeling. A concrete example might involve a proposed change in a hydrologic
regime. Will this change lead to subsequent changes in stream width and riparian
vegetation? Will altering the riparian vegetation change the temperature of the
groundwater? Will constructing a reservoir increase the ground-water inflow
downstream? If so, will a specific model, or modeling process, handle that
change?

All of the, above definitions are actually preceded by an umbrella of
overall model validation to be determined before a model application. This
definition gets more at the qualitative issues of whether the model is
appropriate for the task at hand and whether it will successfully help us make
more accurate, timely, and ultimately useful decisions. "Appropriateness" refers
to such things as the availability of estimates for model variables, data
collection needs and costs, whether the model is accepted academically or
institutionally, whether the model is compatible with other models that must be
used (e.g., habitat models and reservoir release temperature models), what the
limiting assumptions are, and whether the model’s output is truly relevant to
the problems at hand (Bartholow 1976; Ambrose et al. 1981).

Goodness-of-fit criteria for temperature modeling may take several forms.
Some measure, such as root mean square error, should be employed as the primary
statistical index (Chapra and Reckhow 1983):
2 0.5
RUSE = [ 2uei =o? |

92



Q = discharge (cms)
a and b = empirically derived coefficients

It is apparent that this formulation has the following properties. First, if
b equals zero, the "a" term becomes the width. Second, the width will be zero
if the flow is zero, not accounting for pools. Third, the relationship between
width and flow is linear if plotted on a log-log scale.

The best procedure to develop this relationship is as follows. First,
obtain several (three or more) sets of width and flow measurements at random
points along each stream segment. This may be accomplished in the field or from
output from the HABTAT (or related model such as AVDEPTH) portion of the PHABSIM
models, which will report the total stream surface area (per 1,000 feet of
stream) as a function of flow on the so-called HAQF output file. Care should
be taken to make sure weighting factors are applied to represent the entire
segment and that river bends are accounted for if necessary (program ADDBEND,
Milhous et al., in press). Second, take the natural log of both width and
discharge and perform a standard linear regression with discharge being the
independent variable. The antilog of the intercept should be computed, not
forced to zero, because it will be equal to the "a term in the relationship.
The "b" term will be the coefficient (slope) of the regression; the antilog of
"b" should not be taken because it is a unitless term. Note that this analysis
may be done in any units system you choose as Tong as they are consistent
(Figure 5). Appendix C presents the skeleton of a Lotus 1-2-3 worksheet useful
for doing this analysis.

Some authors (Currier and Hughes 1980) have argued that the width should
only be measured for flowing water. Large pools with little or no flow, they
state, do not influence the temperature of flowing water. I concur that areas
of limited heat interchange may be omitted from width calculations. However,
in areas where much of the flow goes through deep pools with little velocity,
the width should not be adjusted. What is more important, I believe, is
weighting the formulation ‘of the coefficients toward the flow regime of
importance. If you know, for instance, that you need the most accurate mode?
for low flow conditions, only put low flow width measurements into your
regression. Small braided streams will require more accurate field measurements
(Currier and Hughes 1980).

If you cannot develop a width-flow relationship, set the b coefficient to
zero and employ an average width.

Manning’s n

This is a measure of the roughness of the streambed and channel, which
causes flowing water to backup due to friction, and is a necessary component of
the SNTEMP model in predicting daily maximum water temperatures. At lower flows,
the roughness tends to be due primarily to the stream bottom characteristics;
as the flow increases, the whole channel shape, including river bends and
constrictions, becomes dominant. Therefore, Manning’s n is not constant with

14



measurements, since out of doors the wind speed is rarely below 0.5 mph. The
most common problem with humidity recording stations is freezing of the wet-bulb
reservoir during the winter (Anderson 1955).

Often data other than relative humidity may be available. For example,
one may find dew point instead of relative humidity. For these applications,
you may need to refer to a set of tables published by the National Weather
Service (1973). By knowing the dew point and air temperature (dry bulb), the
relative humidity is determined. The published tables give the most accurate
result, but are extremely tedious to use. A useful approximation, typically
resulting in only a 0.6% error, may be found in Linsley et al. (1975) as:

Rh = [(112 - 0.1 TA + Tdp) / (112 + 0.9 my ®
where Rh = relative humidity
TA = temperature of the air (dry bulb) °C
Tdp = dew point temperature °C

Like air temperature, relative humidity measured at the canyon top may not be
representative of water surface humidity. Figure 19 (Troxler and Thackston
1975) shows this variation with height. This figure tends to justify the
adjustment of relative humidity values by up to 20% from values taken off site.

Solar Radiation

Solar radiation is probably one of the most difficult and costly items to
collect (properly) yourself. Unless local conditions dictate on-site data
collection, I do not recommend tackling this job yourself. Though an important
parameter, especially for maximum daily water temperature, it is relatively easy
to estimate given other, more easily obtained, measurements. For example, either
the SNTEMP or SSSOLAR programs will estimate daily solar radiation for any
specific time of year and set of conditions. The Cinquemani (1978) publication
mentioned by Theurer is a good one, though hard to find.

Measurement will, of course, provide verification of such estimates, and
if you do not feel that you can obtain representative estimates elsewhere, you
may wish to measure solar radiation yourself. All radiation measuring
instruments are called radiometers. There are a variety of species of
radiometers, such as pyrradiometers, pyranometers, pyrgeometers, pyrheliometers,
The most common recording instrument used is simply called a pyranograph,
available from meteorological or forestry supply houses for $600 to $1,000.

Most of these instruments work by measuring the temperature differential
between differently reflective pieces of metal that are either fully or partially
exposed to the sun’s rays through a hemispheric window. Some use a silicon
photocell as a sensing element. In either case, the mechanism produces an
electrical current proportional to the solar radiation. They must be mounted
on a level surface with a totally unobstructed view of the sky, horizon to
horizon. In other words, they should not be influenced by local topography or
vegetation--no shadows allowed. This may mean special problems in vandal -prone

43



which is very easy to deal with and generally produces good (R-squared values
>0.85; standard error <1.5 °C) correlations. This still requires scrutiny.
Continue to make sure which terms should or should not be in the equation.
Narrowing the time frame to times of rising or falling water temperatures will
always help. This formulation will almost always underpredict water temperatures
jn the winter, and should, at a minimum, be constrained to predict positive
numbers.

It may be tempting to use one of these regression techniques in place of
more process-oriented models. But it is imperative to remember that models
based on regression have major limitations, due to their underlying assumptions--
they are valid only as long as the surrounding conditions do not change. As with
the direct observation method, regression models do not lend themselves to
extrapolation outside of the range of hydrologic, meteorologic, or stream
geometry conditions measured. Nor may they be translated geographically
upstream, downstream, or to other drainage basins without great care. Theurer
(1984) makes excellent use of the regression models to fill in the inevitable
missing temperature data in large network analyses, but he applies a separate
regression to each specific geographic location. Properly developed process-
oriented models largely overcome these limitations.

1 and others also feel that measures of standard error obtained from
regression analysis models are more understated than for the process-oriented
models, due to the substantial autocorrelation in both the observed and predicted
water temperatures (Millard et al. 1985). Hirtzel et al. (1982) developed
methods to better account for this high autocorrelation, but the techniques are
perhaps too sophisticated to use on a regular basis.

Please see the section on statistical models for more on this subject and
results other modelers have had.

Segment Models

The class of programs known as the segment models are abbreviated versions
of the more complete SNTEMP programs. Currently, there are three programs making
up the segment family: SSTEMP for temperature modeling, SSSHADE for shade
estimation, and SSSOLAR for solar radiation estimation. This class of programs
has proven valuable for handling one to a few stream reaches in a simple
configuration for a limited number of time periods, and for sensitivity analysis.

The stream segment models (Theurer 1984; Bartholow 1988a,b,c) are quite
useful for simplified modeling and sensitivity analysis. Data input parameters
may range from "back of the envelope" type calculations to detailed micrometeoro-
logical field measurements, with corresponding degrees of reliability. However,
their use becomes tedious and error prone as the number of stream segments or
time periods increases. Nonetheless, these segment models may be used for a high
percentage of temperature modeling applications.

Network Models

The SNTEMP model, though initially more complicated than the segment
models, will quickly negate that complication when you are dealing with over

88



taking opposite horizon angles with a clinometer, subtracting the sum from 180
degrees to obtain what they called the “sun arc degrees.” They found that this
measurement correlated well with fish standing crop in higher elevation streams,
providing good year-to-year accuracy and narrow confidence intervals.

How to measure shade in the field. Shade measurements can be costly to
make. I recommend that detailed shade measurements be made in only two cases.
First, careful attention must be given to shade measurements in any stream
project that includes alteration of the shade as an explicit or implicit
management option. Second, if experimentation with a trial version of the
temperature model suggests that shade is a sensitive parameter, then due
attention must be given to it. Otherwise, relatively simple, quick-look
"windshield surveys" should provide satisfactory shade measurements.

Quigley (1981) outlined the computational procedures for estimating the
contribution of riparian vegetation to stream surface shade. He considered the
wetted stream width, distance from wetted edge to the vegetation, crown
measurements and density, stream aspect, latitude, date, and time of day.
Quigley also illustrated how to perform a sensitivity analysis with shade
variables, but discusses why it is impossible to truly generalize the results
to any stream condition.

Quigley (1981) recommends that the crown measurements for deciduous trees
be the diameter, while using the radius for conifers with a triangular shape to
account for the tapering shadow. Other practitioners of this technique (Voos
1986) also recommend this approach. However, I have observed that shadows cast
by conifers rarely assume this tapered shape on any streams except those oriented
nearly north-south. You must judge the best method for specific situations.

Quigley’s techniques were subsequently modified and enhanced by Theurer
et al. (1984) to include topographic (and streambank) shade, integrate over the
course of a multiday time period, and add shade quality as an additional
variable. Quigley points out that the shading is a function of stream width,
which in turn is a function of discharge. This is a feature not dynamically
considered in the SNTEMP or SSSHADE shade models, but has been considered in
dealing with watershed alterations in Washington State by Theurer et al. (1985)
in a general application of the network temperature models. That is, the shade
algorithms work only with a constant stream width. You should characterize the
width as that most indicative of the important conditions. If low flow-high
temperature conditions are to be simulated, make the average width for the shade
calculations be representative of those low flow conditions.

The algorithms developed by Theurer compute the position of the sun with
respect to the location of the stream segment on the earth’s surface. Day length
is first computed for the level-plain case, i.e., as if there were no local
topographic influence. Next the local topography is factored in by recomputing
the sunrise and sunset times based local topographic angles; this local
topography results in a percentage decrease in the level plain daylight hours,
technically termed "hour angles." From this local sunrise/sunset, the program
then computes the percentage of light that is filtered by the vegetation. This
filtering is the result of the size, position, and density of the shadow-casting
vegetation on both sides of the stream. The topographic shade and vegetative

26



OMY HE RENE

A B c BD E F
LOTUS 1-2-3 WIDTH VS FLOW RELATIONSHIP
POUDRE RIVER FROM PAT NELSON'S PHABSIM DATA

Martinez Park

Y x
Q (cfs) Width (ft) @LN(width) @LN(Q)

2 27,96 3.33 0.69 Regression Output:
3 26.54 3.28 1.61 Constant

10 30.29 3.41 2,30 Std Err of Y Est

50 43.68 3.78 3.91 R Squared

100 52.83 3.97 4.61 No. of Observations

200 65.33 4.18 5.30 Degrees of Freedom

300 69.10 4.24 §.70

400 73.92 4.30 5,99 X Coefficient(s) 0.210435

$00 78.39 4.36 6.21 Std Err of Coef.

0.014883

@AVG{Predicted Width) =

Predicted
Width

23.7582)
3.022065 28.81073
0.088492 33.33508
0.966518 46.77238
54. 11738
62.61582
88.19302
7.44884
7593198

51, 77594

perry

to be used in shade file}

A-term = @EXP(Constant)
B-term = X Coefficient
Thus, Predicted Width =
Thus, Predicted Width =

eoin

G. i
EXP($HSS)*O*$GS15.

20.53366 (dimensionless)
0.210435 (dimensionless)
53 * Q * 0.2

126


Table 2. Range of input values used to determine relative sensitivity of SNTEMP.

Parameter Low value High value Units
Inflow 10.0 20.0 cfs
Inflow temperature 6.0 12.0 °C
Roughness 2035 2055 NA
Width’s A 13.5 18.0 NA 2
Thermal gradient 1.5 1.8 d/m*/sec/*C
Air temperature 65.0 85.0 °F
Relative humidity 40.0 70.0 percent
Wind speed 6.0 9.0 mph
Percent possible sun 60.0 80.0 percent
Solar radiation 495.0 630.0 Langleys
Segment shade 25.0 75.0 percent
Ground temperature 10.0 16.0 *c

In Table 1, water temperature is very sensitive to changes in air
temperature when stream flow is low, inflow temperature is low, width-to-depth
ratio is high, relative humidity is high, and wind speed is high. Water
temperature may be sensitive to air temperature when these conditions are not
present, but it will not be as sensitive.

Water temperature is insensitive to changes in thermal gradient all the
time. However, changes in thermal gradient cause the most change in water
temperature when stream flow is low, width-to-depth ratio is high, air
temperature is high, and relative humidity is high. Note that the entry for
travel time/roughness applies only to maximum water temperatures; it does not
effect mean daily water temperatures in the SNTEMP model.

Another way to look at the relative sensitivity of water temperature to
changes in model variables is to plot the absolute change in predicted
temperature produced by varying the parameters through the same combinations
displayed above. The range of values so produced can be large. It is
instructive to plot the data by quartiles, showing the minimum, maximum, and
median values. Graphs for the mean and maximum water temperatures are shown in
Figures 3 and 4. The values for mean and maximum are similar except for shade,
solar radiation, and roughness.

10



For example, discharges at km 7 and 12 are known to be 50 and 55 cms, respec-
tively. The discharge at km 10 is estimated by:

gio = 50 + (55 - 50) * HPF) ~ 5045» 3/5 = 53 cms

Note that this formulation could be applied on a drainage-area basis instead of
distance basis.

Groundwater

Since it is usually impossible to measure irregularities in groundwater
discharge either into or out of a stream, you must assume that the rate of gain
or loss is uniform between points of known or calculated instream discharge.
Most models, including SNTEMP, will calculate this for you, but some will not.

As with all other data elements, if you have reason to believe that
groundwater inflow is not uniform, such as in areas of complex geology, and that
the temperature of the discharge is markedly different from that of the stream,
further investigation is necessary. A crude sensitivity analysis may demonstrate
whether this is warranted. See the section on microthermal habitats and
references for some ideas. No particular attention need be given if the stream
is a losing stream (groundwater recharge).

WATER TEMPERATURE COMPONENTS
Existing Data

Getting your hands on existing water temperature data would usually be
the first choice in most temperature studies. Often a quick perusal of the
existing data will tell you whether, or how often, extremes have been reached.
Occasionally, this may be all you need. However, existing data typically may
not let you construct any relationship between flow and stream temperature. More
importantly, existing data will not be sufficient to describe what will take
place in the face of changing the system in some way.

Water temperature data is collected by a wide variety of State and Federal
agencies. A publication by the USGS Office of Water Data Coordination (Pauszek
1972) attempted to tabulate all the agencies involved. Though undoubtedly out-
of-date, this publication is useful as a summary of data availability. It
tabulates the agencies by State, and partitions the collections by lakes,
reservoirs, canals, estuaries, drains, springs, and wells, as well as the
frequency of measurements. This publication does not present any data; however,
it does contain a 194-reference bibliography of sources that do.

The Federal agencies that collect water temperature data are given as:
USDA Forest Service

Army Corps of Engineers
National Marine Fisheries Service

54



APPENDIX €. PROGRAM SNIPPETS

The following three programs have been included for their potential
usefulness in data synthesis. The first is a FORTRAN program that can estimate
hourly air or soil temperatures given minimum and maximum temperatures. The
second is a BASIC program that can compute dew point and relative humidity from
wet and dry bulb temperatures. The last is a Lotus 1-2-3 template useful for
calculating the width versus flow relationship. All should be modified to suit
your unique purposes.

SUBROUTINE TEMP(T, TMX, TMN,HR,A,B,C,NDAY,APHI)

THIS SUBROUTINE CALCULATES THE TEMPERATURE FOR A SPECIFIC HOUR
GIVEN THE MAXIMUM AIR OR SOIL TEMPERATURE.
IT IS CORRECTED FROM:
PARTON, W.J. AND J.A. LOGAN. 198).
A MODEL FOR DIURNAL VARIATION IN SOIL AND AIR TEMPERATURE
AGRICULTURAL METEOROLOGY 23(1981):205-216
WITH ERRATA FROM REPRINT PAGE 219

MAXIMUM TEMPERATURE

MINIMUM TEMPERATURE

TEMPERATURE AT THE SPECIFIED HOUR

HOUR FOR WHICH THE TEMPERATURE IS CALCULATED (0-24)
TIME LAG IN MAXIMUM TEMPERATURE AFTER NOON (HR}
COEFFICIENT THAT CONTROLS TEMPERATURE DECREASE AT NIGHT
c TIME LAG FOR THE MINIMUM TEMPERATURE AFTER SUNRISE (HR)
NDAY= THE JULIAN DATE (1-365)

APHI= LATITUDE (RADIANS)

CALCULATE DAY LENGTH (ADY-HR) AND NIGHT LENGTH (ANI-HR)

ADELT
TEM]
TEM]
TEM2
AHOU
ADY
ANI

TMX
TMN
T
HR
A

B

tunhaakte

a

AMMOMAGANMAAAMAMAMAAAO

.4014 * SIN(6.28 * (NDAY - 77.) / 365.)
1. - (-TAN(APHI) * (ADELT)) ** 2
SQRT(TEM1)

(-TAN(APHI) * TAN(ADELT))

ATAN2 (TEML, TEN2)

(AHOU / 3.14) * 24.

(24. - ADY)

aft hoe Ra

(Continued)

123


LITERATURE CITED

Ambrose, R.B., T.0. Najarian, G. Bourne, and M.L. Thatcher. 1981. Models for
analyzing eutrophication in Chesapeake Bay watersheds: a selection methodol -
ogy. Chesapeake Bay Program, USEPA, Annapolis, MD.

American Association for the Advancement of Science. 1988. Water not quite
everywhere, but probably enough to drink. AAAS Observer. pp. 12-13.

Anderson, H.W. 1973. The effects of clearcutting on stream temperature: a
literature review. DNR Rep. 29. Washington Dept. of Nat. Res., Olympia.
24 pp.

Anderson, J.W., and L. Miyajima. 1975. Analysis of the Vincent Creek fish
rearing pool project. USDI Bureau of Land Management Technical Note 274.
Denver, CO. 25 pp.

Anderson, L.J. 1955. Instrumentation for mass-transfer and energy-budget
studies. Pages 35-45 in M.H. Staatz and A.F. Trites, eds. Geology of the
Quartz Creek Pegmatite District, Gunnison County, Colorado. USGS Prof. Paper
265.

ASTM. 1983a. Standard method for verification and calibration of Viquid-in-
glass thermometers. Annual Book of ASTM Standards, Designation E£77-82.
890 pp.

ASTM. 1983b. Standard specification for ASTM thermometers. Annual Book of
ASTM Standards, Designation E1-83. 890 pp.

ASTM. 1983c. Standard definitions of terms relating to temperature measurement.
Annual Book of ASTM Standards, Designation £344-79. 890 pp.

Bark, A.W., D.W. Cartwright, C.S. Lovatt, and J.M. Watts. 1986. A simple and
inexpensive monitor for recording oxygen and temperature in lakes and
reservoirs. Water Resour. 20(8):1005-1010.

Bartholow, J.M. 1977. Fort Niobrara Refuge: big game management modeling.
M.S. Thesis. Colorado State University, Fort Collins. 207 pp.

Bartholow, J.M. [1987b.] Stream reach solar model (SSSOLAR). Temperature

model technical note no. 6. Computer program and documentation for IBM-PC.
U.S. Fish and Wildlife Service, Fort Collins, CO. 6 pp. Unpubl. MS.

107



Bovee, K.D. 1982. A guide to stream habitat analysis using the Instream Flow
Incremental Methodology. Instream Flow Information Paper 12. U.S. Fish
Wild]. Serv. FWS/OBS-82/26. 248 pp.

Brazier, J.R., and G.W. Brown. 1973. Buffer strips for stream temperature
control. Research Paper 15. Forest Research Laboratory, School of Forestry,
Oregon State University, Corvallis. 9 pp.

Brown, G.W. 1969. Predicting temperatures of small streams. Water Resour.
Res. 5(1):68-75.

Brown, G.W. 1970. Predicting the effect of clearcutting on stream temperature.
J. Soil Water Conserv. 25:11-13.

Burton, T.M., and G.E. Likens. 1973. The effect of strip-cutting on stream
temperatures in the Hubbard Brook Experimental Forest, New Hampshire.
Bioscience 23:433-435,

California Department of Water Resources. 1976. Eel-Russian Rivers streamflow
augmentation studies. Bulletin No. 105-5. Sacramento, CA. 41 pp. +
appendices.

Carroll, T.A., R.D. Nobel, and R.G. Buschman. 1983, Analytical prediction of
the hourly temperature variation in rivers. J. Environ. Syst. 12(3): 279-288.

Cassidy, R.A., and G.D. Holmes. 1980. Temperature and turbidity control
downstream of a reservoir with multilevel withdrawal capability. Pages 761-
771 in H.G. Stefan, ed. Proceedings of the symposium on surface water
impoundments. American Society of Civil Engineers. Paper 5-5.

Cinquemani, V., J.R. Owenby, and R.G. Baldwin. 1978. Input data for solar
systems. U.S. Dept. of Energy. Environ. Resour. and Assess. Branch. 192 pp.

Chapra, $.C., and K.H. Reckhow. 1983. Engineering approaches for lake
management. Volume 2: mechanistic modeling. Butterworth Publishers, Boston,
MA. 492 pp.

Chiu, C., and £.0. Isu. 1978. Stream temperature estimation using Kalman
filter. Am. Soc. Civ. Eng. Proc. [HY 9 no. 13991] 104:1257-1268.

Collins, M.R. 1969. Temperature analysis of a stream. USGS Professional Paper
650-B. pp. B174-B179.

Colorado Climate Center. 1988. Colorado climate newsletter. Department of
Atmospheric Science, Colorado State University, Fort Collins. 11 pp.

Comer, L.E., and W.J. Grenney. 1977. Heat transfer processes in the bed of a
small stream. Water Res. 11(8):743-744,

Coutant, C.C., K.L. Zachman, D.K. Cox, and B.L. Pearman. 1984. Temperature
selection by juvenile striped bass in laboratory and field. Trans. Am. Fish.
Soc. 113:666-671.

109



oe we oN

oor 7

FIGURES

Approximate "temperature-1imited" geographic range of rainbow
trout under three scenarios ................0055

Various ways in which heat either enters or leav
stream

Sensitivity of the SNTEMP mode is of mean daily
water temperature to changes in various input parameters

Sensitivity of the SNTEMP model’s predictions of maximum daily
water temperature to changes in various input parameters ......
Development of width versus flow relationships showing no
effect of units of measurement on slope, but definite effect

on intercept ............
Variation in travel time versus discharge for se’
along a stream ............
Schematic of dye concentratior
during time-of-travel study
Example of diel change in water temperature through time
following clearcutting ..........scccsesssceceeceeceacuenensess
Effect of one timber management alternative on hourly water
temperatures ......... cece eeceeceeenceesstessevueennneenes
Schematic showing the interception of solar radiation by
topography and riparian vegetation
Conventions for determining the sign and degi
orientation as well as east and west bank designations .......,
The “Bovee" home-made clinometer using a protractor, fishing
weight, and soda straw .............
Riparian vegetation shade parameters .
Influence of buffer strip width on shade density .. os
Frequency distribution of temperature lapse rates at different
altitudes oo... cece eee
Field-installed meteorological station .
Variation of amount of usable data from
meteorological stations .
Variation of air temperature ve the Caney Fork River, TN
Variation of relative humidity above the Caney Fork River, TN...
Total hemispheric radiation taken at Fort Collins, CO, during
December 1988

Effect of various shelterbelt heights on wind speed .

Annual variation of ground temperatures at Fort Collins, .

Departure from the mean ground temperature according to direction
of slope and time Of year oo... i... ceceececesecencusuevevscscus



robust. The "zero lateral flow heat transport" regression model is theoretically
the best model because its parameters have been "transformed" to the mathematical
form used in the process-oriented simulation models, but some experience has
shown that the variance reduction in the estimates may not be worth the trouble.
It is especially prone to problems if the geographic location of interest is
below a reservoir or a "major" tributary that represents a thermal discontinuity.
The simple multiple linear regression may be a reasonable alternative. Its
first-order form is:

Tw = a0 + al Ta + a2 Wa + a3 Rh + a4 (S/SO) + a5 Hsx + a6 Q

where Tw = temperature of the water

Ta = temperature of the air

Wa = wind speed

Rh = relative humidity

$/SO = percent possible sun
Hsx = solar radiation
Q = discharge

The units may be any you choose, and the predicted temperatures may be
minimum, mean, or maximum. Second-order terms may be added if the data set is
large enough. Please consult a statistician for appropriate sample sizes and
determination of which parameters to retain in an analysis. Always examine the
standard error for each coefficient to see which terms to include in your model
and which to throw out. Beyond the simple linear regression above, ingenuity
may be in order. For example, one application determined that the difference
in maximum temperature from predicted mean daily temperature was best estimated
by a regression of the form (Wim Kimmer, pers. comm.)

AT = a + b * In(Q)
where AT = max daily temperature - mean daily temperature
a and b = empirically derived coefficients
Q = mean daily discharge
In = natural Tog

Some support for this formulation may be found by remembering the form of the
relationship between width and flow as well as between n and flow.

The SNTEMP model has a limited ability to fill in missing discharge
Measurements based on an average of known values from the same time periods.
It has a much more sophisticated set of regression techniques for missing water
temperature values. In addition, water temperature values may be "smoothed" to

86



1€

Vp = average vegetation height

Vo = average vegetation offset

<
°
”

average maximum diameter

<
a
ut

= ratio of shortwave
radiation eliminated
to incoming over entire
reach shaded area

RIPARIAN VEGETATION SHADE PARAMETERS
Figure 13. Riparian vegetation shade parameters. Modified from Theurer et al. (1984).



Theurer, F.D., K.A. Voos, and W.J. Miller. 1984. Instream water temperature
lel. Instream Flow Information Paper 16. U.S. Fish Wildl. Serv.
FWS/OBS-84/15. approx. 200 pp.

Theurer, F.D., I. Lines, and T. Nelson. 1985. Interaction between riparian
vegetation, water temperature, and salmonid habitat in the Tucannon River.
Water Resour. Bull. 21(1):53-64,

Troxler, R.W., Jr., and E.L. Thackston. 1975. Effect of meteorological
variables on temperature changes in flowing streams. Ecological Research
Series EPA-660/3-75-002.

U.S. Army Corps of Engineers. 1987. Water operations technical support
information exchange bulletin. Environmental Laboratory, Waterways Experiment
Station, Vicksburg, MS. Vol. E-87-1. 2 sheets.

U.S. Department of Commerce. 1949, Weekly mean values of daily total solar
and sky radiation. Weather Bureau Technical Paper No. 11. Washington, DC.
17 pp. [Supplement No. 1, 1955. 4 pp.J

U.S. Department of Commerce. 1951. Sunshine and cloudiness at selected stations
in the United States. Weather Bureau Technical Paper No. 12. Washington, OC.
16 pp.

U.S. Department of Commerce. 1952. Normal weather charts for the northern
hemisphere. Weather Bureau Technical Paper No. 21. Washington, DC.

U.S. Department of Commerce. 1960. Climatological summary of the U.S.
1951-1960. Climatography of the U.S. No. 86. Asheville, NC.

U.S. Department of Commerce. 1960. Summary of hourly observations. Climato-
graphy of the U.S. No. 82. Asheville, NC.

U.S. Department of Commerce. 1968. Climatic atlas of the United States.
Asheville, NC. 80 pp. [Reprinted 1983. ]

U.S. Dept. of Commerce. 1980. Daily normals of temperature, precipitation,
and heating and cooling degree days 1951-80. Climatography of the U.S.
No. 84. Asheville, NC.

U.S. Department of Commerce. [Published every 10 years.] Monthly normals of
temperature, precipitation, heating and ‘cooling degree days 1951-80,
Climatography of the U.S. No. 81. Asheville, NC. v.p.

U.S. Environmental Protection Agency. 1984. Technical support manual: waterbody
surveys and assessments for conducting use attainability analyses, volume III,
lake systems. Office of Water Regulations and Standards, Washington, DC.
V.p.

U.S. General Accounting Office. 1979. Guidelines for model evaluation.
PAD-79-17. -Washington, DC. 31 pp. [Exposure Draft.]

118



where Pi = prediction at time/space i
Oi = observed value at time/space i
n = number of samples

This measure should be specifically directed toward the predictive values of
importance: mean values, variability, or extreme values. It would be
inappropriate to test a model’s performance for predicting mean values if the
maximum values were the true performance measure. Examples of validation
criteria that have been used after a calibration to a mean bias error of 0 °C
are:

1. No more than 10% of the simulated temperatures are greater than 1 °C
from measured temperatures.

2. No single simulated temperature is greater than 1.5 °C from measured
temperatures.

3. The mean of the absolute values of the observed minus predicted
values is less than 0.5 °C.

4. There is no trend in spatial, temporal, or "temperature" error.

This last criterion needs further explanation. Since, in the case of SNTEMP,
we are dealing with the prediction of stream temperatures through both space
and time, we are using a multidimensional model in a statistical sense. We may
be able to identify errors (prediction minus observation) in any dimension.
Perhaps the best way to do this is to (1) plot error at single or aggregate times
from upstream to downstream, (2) plot error at single or aggregate locations
through a time series, (3) plot error against observed water temperatures, or
(4) plot error against input variables. If trends are present, it is evidence
of some systematic error. In statistical terms, we are looking for homoscedastic
residuals (Reckhow et al. 1986) (Figure 34).

Making error plots will serve not only to highlight "outlier" data points,
but also to provide information on the general shape of the distribution. A
"point cloud” will indicate no trend (Figures 34b,e). A sloping band may
indicate that there is a linear bias in your model. A curved band indicates a
more fundamental, nonlinear, problem. Finally, a wedge-shaped distribution
indicates a systematic error that increases or decreases with the value on the
x-axis (Goodall 1983).

There are various other statistical techniques that have been advocated
to establish a goodness-of-fit relationship. As previously mentioned, the most
common method is regression analysis between the observed data and the simulation
values, testing the hypothesis of a zero intercept and a unit slope. This is
essentially the technique employed by Theurer et al. (1984). The principal
drawback of this approach seems to be that it does not determine that the
variance of the two data sets is less than some tolerable limit (Ringuest 1986).

93



If you wish to be even more precise, instruments called pyrheliometers or
solarimeters may be obtained to measure direct-beam, short wave radiation.
However, these instruments are expensive, and an acceptable alternative, though
not as accurate because only visible light is being measured (Reifsnyder and Lull
1965), is outlined below. This method is similar to one mentioned in the early
literature (Shipman 1954), but uses even more readily available equipment and
supplies.

Purchase from a photographic supply store an item called an 18% gray card
(about $5). Using an accurate, hand-held light meter or camera light meter, set
the ASA value to a low number, such as 25. Set the f-stop to a high value, such
as 16. Stand in direct sunlight and hold the gray card perpendicular to the
sun’s rays such that the meter readings are maximized. Hold the light meter
about 6 inches from the gray card, so that the light meter only picks up light
reflected from the gray card, being careful to cast no shadow on the gray card.
Read the exposure-time from the meter; we will call the denominator of that
exposure-time Eo. Now repeat the measurement in the shade, with the card held
perpendicular to the path of the sun’s rays through the vegetation, without
changing the ASA or f-stop. This denominator we will call Ei. Then use the
following formula to calculate the filtering effect:

Shade quality = 1.0 - (Ei / Eo)

For example, if the in-shade exposure-time is 1/50th of a second and the out-
of-shade exposure time is 1/350th of a second, then

Shade quality = 1.0 -(50 / 350)
or
Shade quality = 0.86

You may need to experiment with the ASA and f-stop settings on your light meter
such that you find a combination that adequately can take both in- and out-of-
shade exposure-time measurements without changing the settings. (Similar
Measurements may be accomplished with a foot-candle meter if additional accuracy
is desired.) The disadvantage of this method is that only the visible radiation
is measured, and it may be difficult to accurately interpolate exposure times
on the togarithmic scale. In addition, wind can create moving shadows, which
confound point-in-time measurements. The advantage is that the sun need not be
clearly visible, though the pair of measurements does need to be made under equal
degrees of sunshine. Also, following the advice of Jackson and Harper (1955)
and Wellner (1979), measurements should be taken between the hours of 0900 and
1500 at randomly or systematically selected sites in the stream, uninfluenced
by topographic shading.

METEOROLOGICAL COMPONENTS

The simplest approach to data collection is to let someone else do it for
you. There are a variety of sources for meteorological data. The list of
candidates would include:

34