United States

Department

of Agriculture

Forest Service

Rocky Mountain

Research Station

General Technical

Report RMRS-GTR-150WWW

March 2005

Measuring Stream

Temperature with Digital

Data Loggers: A User’s

Guide

Jason Dunham

Gwynne Chandler

Bruce Rieman

Don Martin
Federal Recycling Program Printed on Recycled Paper

Abstract

Dunham, Jason; Chandler, Gwynne; Rieman, Bruce; Martin, Don. 2005. Measuring

stream temperature with digital data loggers: a user’s guide. Gen. Tech. Rep. RMRSGTR-150WWW. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky

Mountain Research Station. 15 p.

Digital data loggers (thermographs) are among the most widespread instruments in use for

monitoring physical conditions in aquatic ecosystems. The intent of this protocol is to provide

guidelines for selecting and programming data loggers, sampling water temperatures in the

field, data screening and analysis, and data archiving.

Key words: temperature measurement, temperature sampling, thermographs, temperature dataloggers, water temperature, aquatic monitoring, monitoring protocol,

data quality control

The Authors

Jason Dunham is a Research Fisheries Scientist with the Rocky Mountain Research Station

at the Forestry Sciences Laboratory in Boise, ID. He completed a B.S. degree in zoology at

Oregon State University, an M.S. degree in zoology at Arizona State University, and a Ph.D.

degree at the University of Nevada – Reno.

Gwynne Chandler is a Fisheries Biologist with the Rocky Mountain Research Station at the

Forestry Sciences Laboratory in Boise, ID. She completed a B.S. degree in water resources

at the University of Wisconsin—Stevens Point, and an M.S. degree in fisheries management

from the University of Idaho.

Bruce Riemanis a Research Fisheries Scientist and Project Leader with the Rocky Mountain

Research Station at the Forestry Sciences Laboratory in Boise, ID. He completed a B.S.

degree in zoology at the University of Idaho, an M.S. degree in fisheries management at the

University of Idaho, and a Ph.D. degree in forestry, wildlife and range sciences at the

University of Idaho.

Don Martin is an Aquatic Ecologist with the U.S. Environmental Protection Agency, Region

10 and is based in Coeur d’Alene, ID. He completed a B.A. degree in zoology at Humboldt

State College and an M.A. degree in biology at Humboldt State University.

Acknowledgments

Data used in this protocol were assembled with the help of numerous individuals, including

K. Benkert, J. Delavergne, H. Gearns, S. Kanouse, D. Lantz, D. Low, J. Polos, J. Schultz, J.

Shellberg, S. Spalding, M. Weber, W. Bowers, N. Bruener, P. Coffin, B. Dickerson, C. Drake,

C. Evans, J. Fredrick, B. Hammon, M. Holford, O. Kulkoyluoglu, B. Lay, M. Meeuwig, M.

Peacock, K. Ramsey, R. Schroeter, M. Sevon, A. Talabere, D. Tracy, L. Weber, G. Vinyard,

D. Anderson, S. Bachman, T. Burton, C. Clancy, B. Connors, L. Dominquez, D. Garcia, B.

Gardner, S. Gerdes, T. Herron, D. Myers, R. Nelson, M. Northrop, S. Rosquist, B. Sanchez,

T. Smith, N. Swanson, D. Horan and R. Thurow. Thanks to Dan Isaak and Amanda

Rosenberger for comments on an early draft of this protocol, and Matt Dare, Dona Horan, and

Rudy King for comments on a later draft.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-150WWW. 2005 1

Introduction ___________________

Temperature is a variable of widespread interest in aquatic

ecosystems because it is an important component of water

quality (for example, Poole and others 2001a), and temperature is affected by many different natural and human-related

influences (for example, Poole and Berman 2001; Webb and

Zhang 1997). Digital temperature data loggers (or thermographs) are among the most widespread instruments in use for

monitoring physical conditions in aquatic ecosystems. Most

temperature data loggers are relatively inexpensive (less than

$200 US), simple to deploy, and capable of collecting large

amounts of data (more than 32 kb).

Due in part to the dramatic increase in the use of temperature

data loggers and other new technologies (for example,

Torgerson and others 2001), the quantity of data on water

temperature has increased dramatically. The rapid accumulation of new data has perhaps surpassed our processing ability,

Measuring Stream Temperature

with Digital Data Loggers: A

User’s Guide

Jason Dunham

Gwynne Chandler

Bruce Rieman

Don Martin

and it is not always clear that information from temperature

data loggers is reliable, accurate, or useful. This problem,

which is not new or unique in water quality monitoring, has

been termed the “data-rich, information-poor” syndrome (Ward

and others 1986).

This current general technical report presents a protocol that

is an attempt to provide guidance to improve the quality and

utility of water temperature data collected with digital temperature data loggers.

What This Protocol Covers

In this protocol, we explore a range of issues associated with

the use of temperature data loggers for water temperature

monitoring. Our intent is to provide a synthesis and analysis of

the issues that must be addressed to ensure that data collected

from temperature data loggers serve the objectives for which they

were collected (table 1). In addition to carefully considering

Table 1—Common objectives for sampling of water temperatures (see also NRCS 1997).

Objective Examples

Baseline monitoring Monitoring of pre- and posttreatment water temperature

regimes. Monitoring to determine spatial and temporal

temperature patterns. Monitoring to provide information

on temperature in previously unsurveyed habitats.

Water quality compliance Monitoring of temperatures to determine if beneficial

uses (for example, fish) are supported. Monitoring of

temperatures in relation to point source influences (for

example, warm or cold water discharges). Monitoring of

temperature patterns to validate or parameterize water

temperature models (for example, Bartholow 2000).

Research Monitoring of water temperatures to model responses

of aquatic biota (for example, Eaton and others 1995).

Monitoring of water temperatures to determine

appropriate spatio-temporal sampling designs for a

given sampling frame (for example, water body or

watershed of interest).
2 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-150WWW. 2005

the objectives for a monitoring effort, there are several other

potentially important issues affecting the quality and utility of

temperature data (table 2). Several protocols (for example,

Dunham 1999; Lewis and others 2000; Zaroban 1999) have

summarized information on field sampling methods. We review much of the information in these protocols here for the

sake of completeness, but we encourage users to refer to them

as well. In this protocol, we cover sampling issues (table 2) and

several related considerations, including selection of measurement intervals, data screening, correlations among various

temperature metrics, and development of a relational database.

Users must be cognizant of these issues during all phases (for

example, planning, implementation, analysis, interpretation)

of a monitoring effort. We do not wish to give readers the

impression that sampling of water temperatures with digital

data loggers should be excessively complex or difficult. Rather,

we wish to provide useful and relatively simple guidance that

can substantially improve the quality and utility of temperature

data.

Important Issues Not Covered

Our focus in this protocol is on sampling temperatures at

specific localities or sites. We do not provide extensive guidance on different sampling designs for making inferences

about larger scale spatial patterns of stream temperatures (for

example, Poole and others 2001b). Another topic that is worthy

of consideration but is not considered in detail here, is documentation and archiving of temperature data in a format that is

readily accessible by a wide range of users. As water temperature data accumulate at an accelerating pace and scale, the need

to organize this information in a useable format will increase

accordingly. Although we have developed a relational database for the temperature data used herein, we did not wish to

duplicate existing efforts to archive water quality information.

Several noteworthy efforts related to this need include

StreamNet (http://www.streamnet.org), the U.S. Environmental Protection Agency (for example, STORET; http://

www.epa.gov/storet/) the USDA Forest Service National Resource Information System (http://www.fs.fed.us/emc/nris/),

and the U.S. Geological Survey National Water Quality Assessment (http://water.usgs.gov/nawqa/).

Outline of the Protocol __________

This protocol is organized into four major sections that

correspond to the series of steps that users must take in using

temperature data loggers. These steps include (1) study planning, (2) field sampling, (3) data processing, and (4) data

storage and archiving.

Table 2—Temperature sampling issues covered in this document.

Issue Examples

Instrument error Accuracy and precision, range of measurement,

lag time in temperature recording

Calibration Post- and preuse calibration of data loggers,

“drifting” of temperature readings, reliability of

calibration conditions

Measurement interval Effects of temperature measurement interval on

probability of detecting important maximum and

minimum temperatures

Field sampling Locating representative sampling sites to make

inferences about temperatures of interest (for

example, surface versus benthic temperatures),

effects of data logger housings on temperature

readings

Error screening Numerical filters for detecting outlier and erroneous

observations, visual inspection of thermal patterns

to detect possible errors

Data summaries Choice of statistical summaries of temperature,

correlations among different temperature metrics,

methods for defining “exceptional” conditions
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-150WWW. 2005 3

Step 1. Study Planning

Study objectives—Who will use the data, and why?

A variety of objectives exist for measuring or monitoring

water temperature (table 1). In our experience, most uses of

temperature data loggers are linked to a specific objective. It is

also common, however, to find several independent water

temperature monitoring efforts occurring in the same water

body at the same time. Data loggers from different investigators are often located in the same reach of stream, for example.

Coordination among investigators would help to minimize

duplication of effort and allow opportunities for multiple uses

of information from a single data collection effort. Readers are

referred to the National Handbook of Water Quality Monitoring (NRCS 1996) for additional details on defining objectives.

Choosing a data logger

There are many manufacturers and models of data loggers

from which to choose (table 3). Prices for data loggers at the

time this protocol was written started at approximately $50.00

(US). Important features to consider when choosing a logger

include accuracy, precision, memory capacity, durability, and

programmability.

Accuracy and precision: When properly functioning, most

data loggers are accurate and capable of relatively precise

(±1 ∞C or less) temperature readings. Most manufacturers

provide relatively detailed information on the accuracy and

precision of their instruments (table 3).

Memory capacity: Memory capacity is more important if

temperatures are to be recorded for long periods (for example,

more than 1 year) or short sampling intervals (for example, less

than 30 minutes). Most data loggers manufactured today have

a minimum of 8kb of memory, which allows deployment of

165 days with data recorded at 30-minute intervals (7,920

observations; table 3).

Durability: While some data loggers are quite durable, a

wide variety of field conditions might lead to damage or loss.

For this reason, we recommend using data logger housings in

situations where there is any possibility of damage or loss. For

example, data loggers in streams could be damaged or lost

during high flows, bed scour, and associated transport of

sediment and wood. Trampling from humans or animals could

be important in some locations.

Data logger housings: Many data loggers are not submersible and must be deployed within sealed waterproof housings.

Data loggers within waterproof housings are not in direct

contact with the water and are actually recording air temperatures within the sealed housing. Heat transfer between the air

within the housing and the surrounding water is not immediate, but air temperatures within the housing should track

surrounding water temperatures. A short time lag (approximately15 minutes) is required for the air within the housing to

equilibrate with the surrounding water temperature. Thus,

temperatures recorded from data loggers within housings may

not track water temperatures precisely on very short (less than

15 minutes) time scales.

Table 3—Types of data loggers among those currently available.

Logger Memory Temperature Battery

Manufacturer type Submersible capacity range Accuracy Resolution type Web site

Onset HOBO H8 No 7943 –20—70 0.7 0.4 1 year

replaceable Onsetcomp.com

HOBO Pro Temp No 65291 –30—50 0.2 0.02 3 year

replaceable Onsetcomp.com

StowAway Yes 32520 –4—37 0.2 0.16 5 year nonTidbit replaceable Onsetcomp.com

OpticStow Yes 32520 –4—37 0.2 0.16 10 year

Away replaceable Onsetcomp.com

Veriteq Spectrum Yes 32520 –40—85 0.15 0.05 10 year non1000 replaceable Veriteq.com

Gemini Tinytag No 7943 –40—85 0.2 0.4 2 year

Ultra replaceable Geminidata

loggers.com

TinyTag No 10836 –40—85 0.2 0.4 2 year

Plus replaceable Geminidata

loggers.com

Vemco Minilog Yes 10836 –5—40 0.1 0.015 Replaceable Vemco.com
4 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-150WWW. 2005

In situations where temperatures must be measured precisely, it may be more advisable to use data loggers with

sensors that are in direct contact with water. To consider this

issue, we placed two paired data loggers in two streams for the

summer months. Data loggers in each pair were submersible,

but one was placed in a sealed, waterproof housing and the

second was placed in a flow-through housing. Differences in

temperature measurements between the two in a stream with

moderate diel fluctuation (6 ∞C) were within the reported

accuracy of the instruments (fig. 1 and 2). However, measurements from the paired data loggers in a stream with more diel

fluctuation (10 ∞C) differed by more than 1.5 ∞C. Temperatures

recorded in the sealed housing were cooler during the day and

warmer at night (fig. 1 and 2). It seems likely that air temperature within the sealed housings lagged behind ambient water

temperatures, leading to an underestimation of the maximum

temperatures and overestimation of the minimum temperatures. This problem appears to be most important for streams

with large daily fluctuations in temperature, but further study

is needed to identify the range of conditions that could be

important.

Data loggers that are submersible should be placed in flowthrough, durable housings (for example, heavy duty, UVresistant PVC pipe) to protect them from physical impact or

abrasions and direct solar radiation. Investigators must consider local conditions when designing data logger housings.

For example, housings with fine screens or small flow-through

holes could be easily fouled in eutrophic systems with abundant periphyton or algal growth. Housings placed in areas with

abundant sediment deposition could be buried or filled with

fine sediment. It is important to maintain an exchange of water

through the housing to minimize bias in temperature readings.

Figure 1—Comparison of 2 days of recorded

temperatures for data loggers placed in two streams.

Each stream had a data logger placed in a flow

through housing and a data logger placed in a

sealed (waterproof) housing.

Figure 2—Comparison of the differences in

temperature recorded from paired data loggers in two

streams with different daily ranges. The difference is

the recorded temperature of the sealed (waterproof)

minus the flow through housing over a period of 48

hours (X axis) during July. Grey shading indicates the

range of accuracy for the instruments (± 0.2 ∞C).

10

12

14

16

18

20

22

Temperature (°C) Temperature (°C)

Daily range of temperature = 10 oC

10

12

14

16

18

20

0 10 20 30 40 50 60 70 80 90 100

Time (Hours)

Flow Through Waterproof

Daily range of temperature = 6 oC

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

Daily range of temperature = 10°C

Daily range of temperature = 6°C

Difference from Flow Through (°C) Difference from Flow Through (°C)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-150WWW. 2005 5

One important function of the data logger housing is to

protect the sensor from direct solar radiation. If the housing

itself absorbs solar radiation, it may conduct heat to the data

logger’s sensor and bias temperature readings. For example,

we tested temperatures recorded by data loggers that were

placed in waterproof housings of three different colors: white,

metallic, and transparent. The data loggers were placed in a

sunlit portion of a stream at approximately 0.5 m depth.

Maximum water temperatures measured by loggers in clear

housings were up to 5 ∞C warmer than temperatures measured

by loggers in reflective white or metallic colored housings.

Thus, clear data logger housings may have acted like miniature

greenhouses that trapped solar radiation, causing erroneously

warmer water temperature measurements. Temperature differences between white and metallic housings were not evident. Black data logger housings may also bias temperature

measurements because they can absorb and reradiate significant amounts of heat.

Programmability: Most temperature data loggers allow the

user to program a starting time (delayed deployment) and

sampling interval. Delayed deployment is particularly useful

when using several data loggers within a single system. Delayed deployment can also ensure that temperatures are taken

at the same times for all data loggers, if necessary. Some data

loggers have a variable sampling interval option. This can be

useful in a variety of situations (for example, if memory is

limited and temperatures must be sampled for the entire year).

Measurements can be programmed for longer sampling intervals in winter months when the daily range of temperatures is

smaller and for shorter intervals during the summer when daily

variability in temperature is higher.

Calibration of data loggers

Regardless of the type of data logger used, it is good practice

to make sure it is functioning properly. Calibration is a relatively simple process and well worth the time, given the

consequences of lost or misleading data. A simple and effective procedure for calibrating data loggers is the “ice bucket”

method (see also Onset Computer Corporation 1995). The

procedure involves the following steps:

1. Deploy the data loggers at a short sampling interval (for

example, 1 minute).

2. Submerge data loggers in an insulated, well-mixed

water bath with a generous amount of melting ice (for

example, a large cooler with ice water). Be sure to use

fresh water (dissolved minerals may alter the thermal

properties of water).

3. If possible, record water temperatures using a NIST

(National Institute of Standards and Technology, http:/

/www.nist.gov/) thermometer to ensure the temperature

of the water bath is 0 ∞C.

4. After at least an hour, remove the data loggers and

download the data. If the data loggers are calibrated

correctly, the temperature readings should level out at 0

∞C (fig. 3).

5. It is good practice to check calibration both before and

after data loggers are deployed and retrieved. It is also

advisable to use a NIST thermometer to test the accuracy

of data loggers at temperatures other than 0 ∞C.

6. If temperature measurements are to be synchronized

among different data loggers or measured on a short time

interval (for example, less than 30 minutes), calibration

to determine the accuracy of time recorded by data

loggers may also be necessary.

Choosing a sampling interval

Most data loggers can be programmed to measure and record

temperatures at a variety of time intervals. Obviously, longer

intervals will result in lower resolution and greater potential for

bias. For some measures of temperature, it may be necessary to

sample with high frequency (short time intervals) if the variability or range in temperatures over the course of a day is large.

In other words, infrequent sampling (for example, greater than

2 hour sampling intervals) in systems with variable daily

temperatures may not adequately describe the true thermal

regime at a site. This may be particularly true of instantaneous

measures of temperature, such as the daily maximum temperature. With longer sampling intervals, it is more likely that

estimates of maximum temperatures will be negatively biased

(that is, measured maximum temperatures are cooler than

actual maximum temperatures). For a given daily range of

Figure 3—Illustration of data logger calibration using

the “ice bucket” method. The first arrow indicates the

point at which the logger reached 0 ∞C, and the

second arrow is the point at which the logger was

removed from the bath. The steep decline of the

temperature represents when the logger was placed

in the bath. The dashed horizontal line is the

temperature of the water bath as recorded with NIST

handheld thermometers. This calibration “check” was

carried out over a period of 6 hours.

0 60 120 180 240 300 360

0

–5

5

10

15

20

25

Time (min)

Temperature (°C)
6 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-150WWW. 2005

variation in temperatures, it should be possible to prescribe

sampling intervals that ensure temperature regimes are adequately described.

To quantitatively address the bias associated with temperature sampling frequency or sampling interval, we used temperature data from data loggers deployed at 1,252 sites sampled

in the Pacific Northwest and Rocky Mountain regions (Dunham 1999; Dunham and Chandler 2001; Rieman and Chandler

1999). Sampling intervals at these sites ranged from as few as

five observations per day (every 4.8 hours) to 96 observations

per day (every 15 minutes). These samples represent sites

exhibiting a large range of variability in daily water temperatures and the variability is highly correlated with the daily

range of temperature (fig. 4). The maximum range in daily

temperature for the entire data set was 17.8 ∞C.

We focused our analyses on the influence of sampling

interval on the observed maximum daily temperature. Both

maximum and minimum daily temperature should be sensitive

to sampling interval, because either may only be observed for

a short time within a day. To evaluate the potential for bias

related to temperature sampling intervals, we needed a baseline

or reference representing the “true” thermal regime. The “true”

thermal regime within a day is the theoretical distribution of

temperatures observed by sampling at infinitely small intervals. In the data set we used, there were 211 samples, and the

shortest sampling interval for which there were sufficient data

to analyze was 30 minutes.

To compare maximum daily temperatures observed in the

baseline data samples to those observed with sampling at

greater than 30 minute-intervals, we subsampled observations

from the baseline data sets to simulate sampling at 1-, 2-, 3-,

and 4-hour intervals. For these simulated sampling intervals,

we predicted the probability of missing the maximum daily

temperature by more than 1 ∞C from the baseline samples. We

selected a 1 ∞C unit of measure to detect measurement bias

likely attributable to sampling interval and not instrument

error. The obvious consequence of missing the maximum

temperature during sampling is underestimating the warmest

temperatures that occurred at a given site. We predicted the

probabilities of missing the maximum temperature with logistic regression (Allison 1999; also see Dunham 1999).

As expected, sites with larger diel fluctuations (larger daily

range in temperature) have a greater probability of missing the

true maximum than those with smaller diel fluctuations (fig. 5).

A daily range of 8 ∞C would have an error rate of 4.5 percent

and 8.5 percent for 3- and 4-hour sampling intervals, respectively.

Step 2. Field Procedures

Spatial thermal variation and sample site selection

Spatial patterns of thermal variability are common in water

bodies of all types. Spatial differences in water temperature

may be obvious at a variety of scales. In lakes and reservoirs,

larger scale (for example, greater than 10 m) patterns of

vertical stratification are commonly associated with thermal

differences in the density of water. Patterns of stratification

may vary on a seasonal or irregular basis. Smaller scale (less

than 10 m) variability in the temperature of lakes and reservoirs

can be caused by groundwater (for example, springs) and

tributary inflow. Small-scale thermal heterogeneity is similarly common in streams. Within a short segment of stream,

localized variation in temperature can occur in a lateral, horizontal, or vertical direction (fig. 6).

If the objective is to characterize the well-mixed or “thalweg” temperature in a stream, then small-scale variability in

Figure 4—Linear correlation of mean daily range

of temperature with mean daily standard

deviation (square root of variance) for each site

in the data set (r=0.98).

Figure 5—Probability of underestimating the maximum

daily temperature by at least 1 ∞C in relation to daily

range of temperature and sampling interval.

0

1

2

3

4

5

6

0 5 10 15 20

Daily Range of Temperature (°C)

Daily Standard Deviation (°C)

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

0 2 4 6 8 10 12 14 16 18 20

Daily Range of Temperature (°C)

Probability of Missing True Maximum by more than 1 °C 

4 hr interval

3 hr interval

2 hr interval

1 hr interval
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-150WWW. 2005 7

Figure 6—An illustration of spatial variation in

temperature in the Lochsa River, Idaho. Note the

influence of tributary inflow. The image was generated

using infrared aerial videography (Torgerson and others

2001; image provided by Don Essig, Idaho Department

of Environmental Quality). The colors represent different

surface temperatures.

temperature must be carefully considered in selection of sites

for temperature measurements. For example, stream temperatures near tributary junctions where flows are incompletely

mixed are not representative of thalweg temperatures (fig. 6).

Information on thermal variability at a small spatial scale is

best obtained by probing with a hand-held thermometer. Spatial variation in dissolved gases (for example, dissolved oxygen) or conductivity may also indicate sources of thermal

variation. These alternatives may be useful when potential

thermal variability is not measurable at the time of data logger

deployment. This might be important during high spring flows,

seasonal turnover periods in lakes and still pools, and periods

when groundwater and surface water temperatures are not

distinguishable. At larger spatial scales, information from

infrared aerial videography can be useful for designing sampling programs for water temperatures, particularly in streams

(Torgerson and others 2001).

Protecting the data logger in the field

Once a suitable site is selected for temperature sampling, the

data logger must be securely placed within the site. The three

common reasons for the loss or damage of data loggers are: (1)

failure to relocate the data logger after initial field deployment;

(2) human tampering or vandalism; and (3) natural disturbances, such as flooding, substrate movement, and animal

influences (for example, trampling by livestock or wildlife,

beaver pond construction).

Failures to relocate data loggers can be minimized by attention to a few simple practices. Detailed hand-drawn maps and

notes are usually necessary to relocate temperature data loggers following initial field deployment. This is particularly

important when different individuals are involved in different

stages of field operations, as is often the case. Site descriptions

should reflect potential changes in conditions that could affect

a person’s ability to relocate the data logger (for example,

changes in stream flow or reservoir level, and seasonal changes

in vegetation). Storing of geographic coordinates using a

global positioning system (GPS) may be useful, but GPS

coordinates are often insufficient by themselves.

Human disruption or vandalism can be a challenge. In many

situations it is necessary to record temperatures in areas with

high levels of human activity. The options for minimizing

human interference include camouflage, secured storage, or

use of backup data loggers. The choice obviously depends on

the situation. Camouflage is generally less expensive, but it

may also make the data logger more difficult to relocate.

Alternatively, data loggers can be secured in locked and signed

housings that are relatively impervious to physical vandalism

or disruption. A third option is to use two or more data loggers

in a single location as backups in the case of interference.

Human tampering can also result in unintentional interference. Some examples include damage to data loggers from

construction or restoration efforts in the stream channel and

electrofishing surveys. Active coordination with ongoing research, monitoring, or management in the study area is useful,

not only to minimize duplication of temperature sampling

efforts, but also to minimize problems with unintentional

interference.

Natural disturbances to data loggers are obviously impossible to control entirely, but they can be anticipated in many

situations. Stream environments pose the biggest problems in

terms of natural disturbance. The most common disturbances

affecting data loggers are those associated with high stream

discharges. Drag that is induced by higher water velocities and

associated substrate movement and transport of debris can

damage or dislodge data loggers. In our experience, housings

provided by manufacturers or made by individual users generally are sufficient to protect data loggers, but stronger housings

might be needed if extreme conditions are anticipated.

A common source of data loss is dislodging of the logger.

Accordingly, it is important to properly anchor the data logger.

A variety of anchors can be used, including large rocks,

concrete blocks, and metal stakes (see also Onset Computer

Corporation 1995). A practical consideration is the effort

involved in transporting the anchor to the field site. We have

encountered a variety of weight-reducing alternatives. Lightweight and durable bags or containers that can be easily carried

to the site and filled with rocks or sand are popular. Examples

include sand bags (usually available from hardware stores) and

rubber inner tubes from automobile tires. Most data loggers

can be quickly attached to sandbags using nylon zip ties. Chain,

cable, or metal stakes are also useful, but they must be firmly

anchored into the substrate. Chains or cables are often tethered

to rocks or large wood in the stream or anchored into the

streambed.
8 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-150WWW. 2005

Burying data loggers to protect them from disturbance may

not be an effective measure because influences from groundwater, subsurface flow, and the substrate can cause subsurface

temperatures to deviate from temperatures in the well-mixed

portion of the stream. Nonetheless, data loggers can be buried

under substrate, aquatic vegetation, or accumulations of debris

during deployment. The location of data loggers in such

situations can be aided by use of a metal detector. The potential

for biased temperature readings could be measured by comparing temperature data to records from nearby sites.

Finally, the effects of domestic or wild animals on data

loggers may be important. Given that most data logger housings are relatively durable, we have encountered few problems

with trampling from livestock or wildlife. In our experience,

beaver activity has been more important. We have had data

loggers buried under beaver dams or impounded in associated

ponds. Given the range of factors that can lead to loss of data

loggers, perhaps the best approach is to use more than one data

logger (“backups”) in areas where problems are most likely to

occur and where loss of data has the most serious consequences.

Step 3. Data Processing

Error screening

Once data loggers are retrieved from the field and data are

downloaded, it is important to verify the quality of the data and

check for potential errors. It is useful to visually inspect each

time series to note any obvious data logger malfunctions or

dewatering of site (fig. 7). In many cases, the data need to be

trimmed. For example, if the logger was recording temperatures in the office during or after deployment, these observations should be removed before continuing with any analysis.

Usually, these problems are obvious from visual inspection of

the data (fig. 7).

It may be useful to automatically flag any suspect observations. For example, Rieman and Chandler (1999) flagged all

temperature observations that fell below –1 ∞C or above 30 ∞C.

Observations were also flagged if there was a rate of change

greater than 3 ∞C per hour or a daily mean change of greater

than 3 ∞C between two successive days. The upper and lower

5

th percentiles of the overall distribution of observed temperatures were also flagged. Flagged observations were not removed from the database. They were reverified with personnel

involved in data logger programming and field sampling.

Flagged observations were only removed if obvious problems

were found.

Statistical summaries of temperature data

A variety of statistical summaries or “metrics” describe

important elements of temperature regimes. Most often, the

focus is on maximum temperatures, due to their regulatory

importance. Water quality criteria for temperature commonly

use one or more metrics to describe maximum temperatures.

From a biological perspective, a number of components of

thermal regimes are biologically important (for example, minimum temperatures, seasonal patterns, timing and duration of

different temperatures), but we will focus our discussion on

temperature metrics that describe maximum temperatures.

Maximum temperatures are often summarized within an

annual timeframe. Within a year, there are a variety of

timeframes over which maximum temperatures may be described. For water quality criteria, temperatures are most often

summarized for the warmest day or week of the year. Summaries of mean and maximum temperatures are common, but it

may also be useful to describe cumulative exposure to temperatures exceeding a critical threshold. For example, if important biological effects (for example, lethal or sublethal) are

known to occur above a certain temperature, then the duration

of exposure above that threshold may be important. Many

different temperature metrics should be highly correlated, but

the relationships may also vary with local conditions. We were

interested in determining the degree of correlation between

different metrics under different thermal regimes (daily range

of temperature). This information is useful for understanding

potential statistical redundancies among metrics, and for converting one metric to another when data for only one metric (for

example, maximum temperatures from a max-min thermometer) are available.

To examine correlations between selected temperature

metrics, we summarized 1,252 temperature records collected

from throughout the Pacific Northwest and northern Great

Basin based upon the hottest day of the summer, hottest week

of the summer, and cumulative exposure during the hottest

week and throughout the summer. We used 15 July through 15

September to represent the summer, because this is the window

in which maximum temperatures are generally realized within

Figure 7—Example of temperature measurements

from a site that was dewatered during the sampling

period. Note the extreme (greater than 30 ∞C) daily

fluctuation in temperature, and extraordinarily warm

(greater than 40 ∞C) temperatures (at the arrow).

50

45

40

35

30

25

20

15

10

5

0

Temperature (°C)

06/25 07/01 07/15 07/10
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-150WWW. 2005 9

the area we considered. Different temporal windows may be

necessary to capture maximum temperatures in other areas or

unique conditions. The metrics we summarized were:

1. Daily average on the hottest day (MDAT) – The highest

average temperature summarized one calendar day during the summer.

2. Overall summer maximum (MDMT) – The highest

instantaneous maximum temperature recorded during

the summer.

3. Maximum weekly average maximum temperature

(MWMT) – The highest average temperature summarized over a continuous 7 days during the summer.

4. Maximum value of average weekly temperature

(MWAT) – The highest average maximum temperature

summarized over each day of a continuous 7 days during

the summer.

5. Overall average summer temperature (AWAT) – The

average temperature recorded across all observations

from 15 July through 15 September.

6. Cumulative days maximum greater than 14 ∞C during

hottest week (WEEK_14)

7. Cumulative days maximum greater than 18 ∞C during

hottest week (WEEK_18)

8. Cumulative days maximum greater than 22 ∞C during

hottest week (WEEK_22)

9. Cumulative days maximum greater than 14 ∞C during

entire summer (SUM_14)

11. Cumulative days maximum greater than 18 ∞C during

entire summer (SUM_18)

12. Cumulative days maximum greater than 22 ∞C during

entire summer (SUM_22)

We categorized each site by average daily range and grouped

the sites on 2 ∞C daily range intervals to account for the

influence of local conditions on correlations among temperature metrics. The correlations and conversion factors among

temperature metrics for each grouping are summarized in

tables 4 through 10. As was expected, the greater the daily

range of temperatures the lower the correlation of metrics.

Metrics describing an instantaneous measure (for example,

maximum or mean summer temperature) were more correlated

among each other than correlated with cumulative exposure

Table 4a—Correlation matrix among temperature metrics (n=101) where the average daily range over the summer was 0 to 2 ∞C. Numbers

underlined and in italics were not significant correlations (note in comparisons between tables that statistical significance is related

to sample size).

MDAT MDMT MWMT AWAT MWAT WEEK_14 WEEK_18 WEEK_22 SUM_14 SUM_18 SUM_22

MDAT 1.00 0.98 0.98 0.96 0.99 0.72 0.46 0.21 0.72 0.46 0.21

MDMT 0.98 1.00 0.98 0.96 0.98 0.73 0.47 0.24 0.73 0.47 0.24

MWMT 0.98 0.98 1.00 0.97 0.99 0.73 0.46 0.24 0.73 0.46 0.24

AWAT 0.96 0.96 0.97 1.00 0.97 0.71 0.46 0.22 0.72 0.46 0.22

MWAT 0.99 0.98 0.99 0.97 1.00 0.72 0.46 0.23 0.72 0.46 0.23

WEEK_14 0.72 0.73 0.73 0.71 0.72 1.00 0.58 0.28 0.99 0.58 0.28

WEEK_18 0.45 0.47 0.46 0.46 0.46 0.58 1.00 0.48 0.59 1.00 0.48

WEEK_22 0.21 0.24 0.23 0.22 0.23 0.28 0.48 1.00 0.29 0.49 1.00

SUM_14 0.72 0.73 0.73 0.72 0.72 0.99 0.59 0.29 1.00 0.59 0.29

SUM_18 0.46 0.47 0.46 0.46 0.46 0.58 1.00 0.49 0.59 1.00 0.49

SUM_22 0.21 0.24 0.24 0.22 0.23 0.28 0.48 1.00 0.29 0.49 1.00

Table 4b—Conversion factors for the continuous temperature metrics where the average daily range over the summer was 0 to 2 ∞C. The

conversion is row minus column (in other words, if maximum summer [MDMT] was 12 ∞C then the overall mean summer temperature

[AWAT] would be: 12.00-2.55 = 9.45 with 95% confidence bounds of 9.22 to 9.67).

AWAT MWAT MDAT MWMT

MEAN LOWER UPPER MEAN LOWER UPPER MEAN LOWER UPPER MEAN LOWER UPPER

MDMT 2.55 2.33 2.78 1.49 1.34 1.64 1.17 0.97 1.38 0.55 0.44 0.67

MWMT 2.00 1.84 2.15 0.93 0.87 0.99 0.62 0.49 0.75

MDAT 1.38 1.22 1.54 0.31 0.21 0.41

MWAT 1.07 0.92 1.21
10 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-150WWW. 2005

Table 6a—Correlation matrix among temperature metrics (n=336) where the average daily range over the summer was 4 to 6 ∞C.

Numbers underlined and in italics were not significant correlations (note in comparisons between tables that statistical

significance is related to sample size).

MDAT MDMT MWMT AWAT MWAT WEEK_14 WEEK_18 WEEK_22 SUM_14 SUM_18 SUM_22

MDAT 1.00 0.94 0.96 0.95 0.98 0.82 0.79 0.39 0.90 0.79 0.39

MDMT 0.94 1.00 0.98 0.93 0.94 0.83 0.81 0.40 0.92 0.83 0.41

MWMT 0.96 0.98 1.00 0.94 0.97 0.86 0.82 0.39 0.94 0.83 0.39

AWAT 0.95 0.93 0.95 1.00 0.97 0.83 0.77 0.37 0.93 0.78 0.38

MWAT 0.98 0.94 0.97 0.97 1.00 0.84 0.79 0.38 0.92 0.79 0.38

WEEK_14 0.82 0.83 0.86 0.83 0.84 1.00 0.51 0.17 0.85 0.52 0.18

WEEK_18 0.79 0.81 0.82 0.77 0.79 0.51 1.00 0.43 0.74 0.98 0.43

WEEK_22 0.39 0.40 0.39 0.37 0.38 0.17 0.43 1.00 0.28 0.45 0.97

SUM_14 0.90 0.92 0.94 0.93 0.92 0.85 0.74 0.28 1.00 0.76 0.28

SUM_18 0.79 0.83 0.83 0.78 0.79 0.52 0.98 0.45 0.76 1.00 0.45

SUM_22 0.39 0.41 0.39 0.38 0.38 0.18 0.43 0.97 0.28 0.45 1.00

Table 6b—Conversion factors for the continuous temperature metrics where the average daily range over the summer was 4 to 6 ∞C. The

conversion is row minus column (in other words, if maximum summer [MDMT] was 14 ∞C then the overall mean summer temperature

[AWAT] would be: 14.00-5.60 = 8.40 with 95% confidence bounds of 8.22 to 8.57).

AWAT MWAT MDAT MWMT

MEAN LOWER UPPER MEAN LOWER UPPER MEAN LOWER UPPER MEAN LOWER UPPER

MDMT 5.60 5.43 5.78 4.13 3.99 4.28 3.55 3.42 3.68 0.95 0.85 1.05

MWMT 4.64 4.53 4.77 3.18 3.10 3.27 2.60 2.51 2.69

MDAT 2.05 1.94 2.15 0.58 0.53 0.63

MWAT 1.47 1.38 1.55

Table 5a—Correlation matrix among temperature metrics (n=520) where the average daily range over the summer was 2 to 4 ∞C. Numbers

underlined and in italics were not significant correlations (note in comparisons between tables that statistical significance is related

to sample size).

MDAT MDMT MWMT AWAT MWAT WEEK_14 WEEK_18 WEEK_22 SUM_14 SUM_18 SUM_22

MDAT 1.00 0.95 0.96 0.95 0.99 0.88 0.50 0.19 0.90 0.51 0.20

MDMT 0.95 1.00 0.98 0.93 0.95 0.91 0.52 0.22 0.93 0.53 0.24

MWMT 0.96 0.98 1.00 0.95 0.97 0.91 0.51 0.19 0.93 0.51 0.20

AWAT 0.95 0.93 0.95 1.00 0.97 0.86 0.50 0.18 0.90 0.51 0.19

MWAT 0.99 0.95 0.98 0.97 1.00 0.88 0.50 0.18 0.91 0.51 0.19

WEEK_14 0.87 0.91 0.91 0.86 0.88 1.00 0.43 0.15 0.96 0.44 0.17

WEEK_18 0.50 0.52 0.51 0.50 0.50 0.43 1.00 0.41 0.51 0.98 0.43

WEEK_22 0.19 0.22 0.19 0.18 0.18 0.15 0.41 1.00 0.18 0.41 0.95

SUM_14 0.90 0.93 0.93 0.90 0.91 0.96 0.51 0.18 1.00 0.52 0.19

SUM_18 0.51 0.53 0.51 0.51 0.51 0.44 0.98 0.41 0.52 1.00 0.43

SUM_22 0.20 0.24 0.20 0.19 0.19 0.17 0.43 0.95 0.19 0.43 1.00

Table 5b—Conversion factors for the continuous temperature metrics where the average daily range over the summer was 2 to 4 ∞C. The

conversion is row minus column (in other words, if maximum summer [MDMT] was 12 ∞C then the overall mean summer

temperature [AWAT] would be: 12.00-4.08 = 7.92 with 95% confidence bounds of 7.79 to 8.04).

AWAT MWAT MDAT MWMT

MEAN LOWER UPPER MEAN LOWER UPPER MEAN LOWER UPPER MEAN LOWER UPPER

MDMT 4.08 3.96 4.21 2.76 2.67 2.86 2.28 2.18 2.37 0.80 0.71 0.88

MWMT 3.29 3.22 3.36 1.96 1.92 2.01 1.48 1.43 1.53

MDAT 1.81 1.74 1.88 0.49 0.45 0.52

MWAT 1.32 1.26 1.38
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-150WWW. 2005 11

Table 7a—Correlation matrix among temperature metrics (n=130) where the average daily range over the summer was 6 to 8 ∞C. Numbers

underlined and in italics were not significant correlations (note in comparisons between tables that statistical significance is

related to sample size).

MDAT MDMT MWMT AWAT MWAT WEEK_14 WEEK_18 WEEK_22 SUM_14 SUM_18 SUM_22

MDAT 1.00 0.93 0.94 0.93 0.98 0.43 0.86 0.75 0.74 0.91 0.76

MDMT 0.93 1.00 0.98 0.90 0.92 0.44 0.89 0.82 0.71 0.92 0.83

MWMT 0.94 0.98 1.00 0.93 0.95 0.45 0.92 0.82 0.75 0.95 0.83

AWAT 0.93 0.90 0.93 1.00 0.95 0.45 0.83 0.75 0.74 0.92 0.77

MWAT 0.98 0.93 0.95 0.95 1.00 0.44 0.87 0.77 0.77 0.93 0.78

WEEK_14 0.43 0.44 0.45 0.45 0.44 1.00 0.39 0.20 0.45 0.39 0.20

WEEK_18 0.86 0.89 0.92 0.83 0.87 0.39 1.00 0.65 0.69 0.92 0.66

WEEK_22 0.75 0.82 0.82 0.75 0.77 0.20 0.65 1.00 0.57 0.76 0.98

SUM_14 0.75 0.71 0.75 0.74 0.77 0.45 0.69 0.57 1.00 0.82 0.58

SUM_18 0.91 0.92 0.95 0.92 0.93 0.39 0.92 0.76 0.82 1.00 0.77

SUM_22 0.76 0.83 0.83 0.77 0.78 0.20 0.66 0.98 0.58 0.77 1.00

Table 7b—Conversion factors for the continuous temperature metrics where the average daily range over the summer was 6 to 8 ∞C. The

conversion is row minus column (in other words, if maximum summer [MDMT] was 14 ∞C then the overall mean summer temperature

[AWAT] would be: 14.00-7.27 = 6.73 with 95% confidence bounds of 6.48 to 6.98).

AWAT MWAT MDAT MWMT

MEAN LOWER UPPER MEAN LOWER UPPER MEAN LOWER UPPER MEAN LOWER UPPER

MDMT 7.27 7.02 7.52 5.57 5.33 5.80 4.93 4.71 5.14 1.01 0.88 1.14

MWMT 6.26 6.07 6.45 4.55 4.41 4.71 3.92 3.74 4.09

MDAT 2.34 2.15 2.53 0.64 0.53 0.74

MWAT 1.70 1.54 1.86

Table 8a—Correlation matrix among temperature metrics (n=61) where the average daily range over the summer was 8 to 10 ∞C. Numbers

underlined and in italics were not significant correlations (note in comparisons between tables that statistical significance is related

to sample size). Dashes indicate no days exceeding 14 ∞C during the hottest week of the year.

MDAT MDMT MWMT AWAT MWAT WEEK_14 WEEK_18 WEEK_22 SUM_14 SUM_18 SUM_22

MDAT 1.00 0.82 0.84 0.92 0.97 — 0.70 0.80 0.39 0.82 0.84

MDMT 0.82 1.00 0.98 0.79 0.81 — 0.68 0.90 0.12 0.64 0.90

MWMT 0.84 0.98 1.00 0.83 0.86 — 0.70 0.93 0.17 0.68 0.92

AWAT 0.92 0.79 0.83 1.00 0.94 — 0.70 0.78 0.30 0.80 0.85

MWAT 0.97 0.81 0.86 0.94 1.00 — 0.71 0.82 0.37 0.84 0.87

WEEK_14 — — — — — — — — — — —

WEEK_18 0.70 0.68 0.70 0.70 0.71 — 1.00 0.54 0.39 0.69 0.53

WEEK_22 0.80 0.90 0.93 0.78 0.82 — 0.54 1.00 0.14 0.64 0.95

SUM_14 0.39 0.12 0.17 0.30 0.37 — 0.39 0.14 1.00 0.66 0.18

SUM_18 0.82 0.64 0.68 0.80 0.84 — 0.69 0.64 0.66 1.00 0.72

SUM_22 0.84 0.90 0.92 0.85 0.87 — 0.53 0.95 0.18 0.72 1.00

Table 8b—Conversion factors for the continuous temperature metrics where the average daily range over the summer was 8 to 10 ∞C. The

conversion is row minus column (in other words, if maximum summer [MDMT] was 16 ∞C then the overall mean summer

temperature [AWAT] would be: 16.00-8.79 = 7.21 with 95% confidence bounds of 6.77 to 7.66).

AWAT MWAT MDAT MWMT

MEAN LOWER UPPER MEAN LOWER UPPER MEAN LOWER UPPER MEAN LOWER UPPER

MDMT 8.79 8.34 9.23 7.07 6.63 7.51 6.42 6.00 6.84 1.06 0.91 1.22

MWMT 7.72 7.36 8.08 6.01 5.68 6.34 5.36 5.02 5.70

MDAT 2.36 2.11 2.61 0.65 0.49 0.80

MWAT 1.72 1.50 1.93
12 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-150WWW. 2005

Table 10a—Correlation matrix among temperature metrics (n=25) where the average daily range over the summer was over 12 ∞C. Numbers

underlined and in italics were not significant correlations (note in comparisons between tables that statistical significance is related to

sample size). Dashes indicate no days exceeding 14 ∞C during the hottest week of the year.

MDAT MDMT MWMT AWAT MWAT WEEK_14 WEEK_18 WEEK_22 SUM_14 SUM_18 SUM_22

MDAT 1.00 0.55 0.62 0.81 0.77 — — 0.43 –0.10 0.14 0.53

MDMT 0.55 1.00 0.95 0.74 0.65 — — 0.47 –0.05 0.27 0.59

MWMT 0.62 0.95 1.00 0.81 0.72 — — 0.47 –0.08 0.28 0.61

AWAT 0.81 0.74 0.81 1.00 0.93 — — 0.47 –0.11 0.25 0.68

MWAT 0.77 0.65 0.72 0.93 1.00 — — 0.47 0.03 0.36 0.72

WEEK_14 — — — — — — — — — — —

WEEK_18 — — — — — — — — — — —

WEEK_22 0.43 0.47 0.47 0.47 0.47 — — 1.00 –0.30 –0.06 0.47

SUM_14 –0.10 –0.06 –0.09 –0.11 0.03 — — –0.30 1.00 0.88 0.40

SUM_18 0.14 0.27 0.28 0.25 0.36 — — –0.06 0.87 1.00 0.72

SUM_22 0.53 0.58 0.61 0.68 0.72 — — 0.47 0.40 0.72 1.00

Table 9b—Conversion factors for the continuous temperature metrics where the average daily range over the summer was 10 to 12 ∞C.

The conversion is row minus column (in other words, if maximum summer [MDMT] was 16 ∞C then the overall mean summer

temperature [AWAT] would be: 16.00-10.37 = 5.67 with 95% confidence bounds of 5.13 to 6.13).

AWAT MWAT MDAT MWMT

MEAN LOWER UPPER MEAN LOWER UPPER MEAN LOWER UPPER MEAN LOWER UPPER

MDMT 10.37 9.87 10.87 8.17 7.82 8.52 7.60 7.13 8.06 1.31 0.97 1.64

MWMT 9.06 8.55 9.58 6.86 6.60 7.13 6.29 5.94 6.64

MDAT 2.77 2.28 3.26 0.57 0.30 0.84

MWAT 2.20 1.79 2.61

Table 9a—Correlation matrix among temperature metrics (n=26) where the average daily range over the summer was 10 to 12 ∞C. Numbers

underlined and in italics were not significant correlations (note in comparisons between tables that statistical significance is

related to sample size). Dashes indicate no days exceeding 14 ∞C during the hottest week of the year.

MDAT MDMT MWMT AWAT MWAT WEEK_14 WEEK_18 WEEK_22 SUM_14 SUM_18 SUM_22

MDAT 1.00 0.83 0.92 0.89 0.95 — 0.33 0.61 –0.05 0.19 0.80

MDMT 0.83 1.00 0.93 0.83 0.92 — 0.33 0.61 –0.10 0.08 0.68

MWMT 0.92 0.93 1.00 0.86 0.97 — 0.33 0.68 –0.03 0.21 0.78

AWAT 0.89 0.83 0.86 1.00 0.90 — 0.33 0.59 –0.32 –0.02 0.65

MWAT 0.95 0.92 0.97 0.90 1.00 — 0.33 0.62 –0.03 0.20 0.80

WEEK_14 — — — — — — — — — — —

WEEK_18 0.33 0.33 0.33 0.33 0.33 — 1.00 0.45 0.26 0.34 0.33

WEEK_22 0.61 0.61 0.68 0.59 0.62 — 0.45 1.00 0.01 0.34 0.70

SUM_14 –0.05 –0.10 –0.03 –0.32 –0.03 — 0.26 0.01 1.00 0.85 0.36

SUM_18 0.19 0.08 0.21 –0.03 0.19 — 0.34 0.34 0.85 1.00 0.64

SUM_22 0.80 0.68 0.78 0.65 0.80 — 0.33 0.69 0.36 0.64 1.00

Table 10b—Conversion factors for the continuous temperature metrics where the average daily range over the summer was over 12 ∞C. The

conversion is row minus column (in other words, if maximum summer [MDMT] was 16 ∞C then the overall mean summer temperature

[AWAT] would be: 16.00-11.82 = 4.18 with 95% confidence bounds of 3.55 to 4.81).

AWAT MWAT MDAT MWMT

MEAN LOWER UPPER MEAN LOWER UPPER MEAN LOWER UPPER MEAN LOWER UPPER

MDMT 11.82 11.19 12.45 10.20 9.46 10.94 9.70 8.78 10.61 1.17 0.91 1.43

MWMT 10.65 10.21 11.09 9.03 8.45 9.60 8.52 7.76 9.29

MDAT 2.12 1.55 2.69 0.51 -0.04 1.05

MWAT 1.62 1.31 1.92
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-150WWW. 2005 13

Table 11—Example of a relational database application for storage of temperature data collected using data loggers.

Table name Field name Description

Site Site ID Auto number assigning consecutive numbers to sites

Stream name Name of stream sampled

Site Number of descriptor of site within stream

Basin River basin

Quad 24K Quad name

UTM X UTM easting coordinate

UTM Y UTM northing coordinate

UTM zone UTM zone number

Elevation Elevation in meters of site

Logger Logger ID Unique ID or serial number of logger

Type Manufacturer and/or model of logger

Year Year of sample

Pre calib Pre calibration factor

Post calib Post calibration factor

Deployed Site ID Site ID of stream and site (relates back to Site table)

Logger ID Unique ID of logger deployed in the stream (relates back to Logger table)

Date Date logger placed in water

Time Time logger placed in water

Interval Time interval of samples

Width Wetted width of site at deployment

Depth Depth of logger

Hab type Habitat type where logger was placed

Removal Site ID Site ID of stream and site (relates back to Site table)

Date Date logger removed from water

Time Time logger removed from water

Width Wetted width at time of removal

Comments Any site differences from time of deployment to time of removal

Temperature Site ID Site ID of stream and site (relates back to Site table)

Date Date of sample

Time Time of sample

Temperature Temperature (in ∞C or ∞F) of sample

metrics (for example, number of days maximum was over 16

∞C). The correlations and conversions reported herein are

useful for comparing the equivalence of information from

different summary water temperature metrics, but local verification of these relationships is advised.

Step 4. Data Archiving

Data archiving is one of the most important steps in a

temperature monitoring effort. A plan for data archiving is

especially important for large studies. The volume of information data loggers can collect warrants storage within a relational database system (such as Oracle, Sybase, Access). The

details of using different database programs or setting up

databases are beyond the scope of this document; our intent

here is to outline some of the pieces of information to be

archived within a database. Spreadsheets can be useful for

small datasets, but they are limited by the number of observations each sheet can hold as well as the ease of summarization

of data. Relational databases, designed correctly, will have a

minimal (if any) amount of redundant information. Therefore,

the time needed to summarize and edit data is greatly reduced.

Relational database applications also require less storage space.

There are three basic types of information to include in a

temperature database: (1) pre-deployment information, (2)

field deployment information, and (3) post-deployment information (table 11). Pre-deployment information includes data

and notes on field site characteristics, locations, and calibration of the data loggers. Site data should include stream name,

drainage and topographical map name. Data logger information should include logger type (model), logger serial number,

and precalibration factor (if calibration was performed).

Field deployment information includes site definition and

time of deployment. Efficient data collection at this point will
14 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-150WWW. 2005

save hours of work at the post-deployment stage. Data needed

at this stage include stream name and site number (if appropriate), UTM coordinates or other location information to georeference the site, description of site, habitat type that the data

logger was deployed in, date and time data logger was placed

in stream, time interval of samples, data logger serial number

(to relate back to precalibration information), wetted width at

data logger, depth of logger, and a picture of the site. An

example of a field deployment datasheet is shown in figure 8.

Post-deployment information is gathered in the field as well

as in the office. Minimal field data include date and time of

removal, wetted width at time of removal, and any other

relevant site information (for example, “Did the site dry up

during sampling?” or “Was there any evidence of tampering

with the data logger?”) Once the data logger is retrieved and the

data downloaded the logger should be calibrated again to note

any differences in the pre- and postcalibration factors. We have

found that loggers can drift (in other words, the pre- and

postcalibration factors are not the same).

Figure 8—An example of a field datasheet

used for deployment of data loggers.

Basin:

 Panther

 Boise

 SF Salmon

Waypoint gathered:  Yes

 No Site noted on map:  Yes

 No

Stream Information

Logger Information

Logger Type: Tidbit Sampling Int.: 30 min.

Site Information

Habitat type of placement:  Riffle

 Pool

 Run

 Pocket

(in a well mixed zone)

Tethering Method: Sandbag

 Other: 

Detailed map of site:
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-150WWW. 2005 15

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Dunham, J.B. and G.L. Chandler. 2001. Models to predict suitable habitat for

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Lewis, T.E., D.W. Lamphear, D.R. McCanne, A.S. Webb, J.P. Krieter, and

W.D. Conroy. 2000. Regional Assessment of Stream Temperatures Across

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