Lake and Reservoir Monitoring
Introduction
Topics in this guide include how to establish goals, identify data uses
and users, assign staff responsibilities, establish a pilot program, prepare
a quality assurance plan, and fund a program.
Setting General Goals
As a first step, organizers should establish their general goals. Are
they interested in providing credible information on water quality conditions
to State and local agencies? Or are they primarily interested in educating
the public about water quality issues? Do they wish to build a constituency
of involved citizens?
All three goals can be achieved by a well-organized and maintained program,
but it is important to determine which of these goals is paramount. This
methods manual is directed primarily to those programs that seek to improve
the understanding of lake conditions and protection needs by supplementing
water quality data.
Identifying Data Uses
Early in the planning stage, organizers should identify how data collected
by the lake volunteer program will be used and who will use it. Data can
be used to establish baseline conditions, determine trends in water quality,
or identify current and emerging problems.
Prospective users of volunteer-collected data include State water quality
analysts, planners, fisheries biologists, agricultural agencies, and parks
and recreation staffs; local government planning and zoning agencies; university
researchers; and Federal agencies. A planning committee made up of represen
tatives from the identified data users should be convened early in the
development of a program.
Initially, the planning committee must make several important decisions
in the development of a volunteer monitoring program. Basically, the committee
must decide:
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What the major goal of the program will be;
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What existing or potential lake condition will be the focus of monitoring;
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What sampling parameters should be used to characterize the selected lake
condition;
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What procedures should use to sample each parameter;
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How personnal will be trained; and
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How the results of monitoring will be presented.
Once the monitoring program is established, the planning committee should
meet periodically to evaluate it, update objectives, and fine-tune activities.
This review should ensure that the monitoring efforts continue to
provide useful information to those who need lake data.
Establishing Quality Assurance and Quality Control
Many potential users of data believe that only professionals can conduct
sampling and generate high quality results.
This is not true. Given proper training and supervision, dedicated volunteers
can conduct monitoring activities and collect samples that yield high quality
data.
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Accuracy is the degree of agreement between the sampling
result and the true value of the param eter being measured. Accuracy is
most affected by the equipment and the procedure used to measure a sample
parameter.
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Precision, on the other hand, refers to how well you are
able to reproduce the data result on the same sample (regardless of accuracy).
Human error in sampling techniques plays an important role in estimating
precision.
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Representativeness is the degree to which the collected data
accurately and precisely represent the lake condition being measured. It
is most affected by sample site location. For example, if the monitoring
objective
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Completeness is a measure of the amount of valid data obtained
versus the amount expected to be obtained as specified by the original
sampling design objec tives. It is usually expressed as a percentage. For
example, if 100 samples were scheduled, but volunteers only sampled 90
times because of bad weather, broken equipment, and so forth, the completeness
record would be 90 percent.
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Comparability is very important to the manager of a citizen
monitoring program because it represents how well data from one lake can
be compared to data from another. As part of a statewide or regional report
on the volunteer monitoring program, most managers compare one lake to
another. It is vital, therefore, that sampling methods and procedures are
the same from lake to lake.
When forming data quality objectives, the planning committee must also
examine the program budget. Sophisticated analysis of some param eters
(yielding high precision and accuracy) usually comes at higher costs in
terms of equipment, procedures, laboratory fees, agency time, and citizen
training. These higher costs may be worthwhile if the program is oriented
toward supplementing agency data collection.
For programs oriented more toward public education and participation,
the use of less sensitive equipment and procedures may be in order. In
this case, budget money could be better spent for public awareness materials
and supporting an increase in citizen monitors. An efficient sampling design
is one that balances cost components with acceptable levels of uncertainty
in context with program goals and objectives.