1.2 Study Design
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based on the rate of change in water level, which is usually not constant over
time. Samples collected during events can then be evaluated using the flow
measurements recorded at the times of sample collection to optimize the selection of samples for chemical analysis. This approach offers the opportunity to
greatly reduce sampling costs with minimal loss of information. However, the
use of automatic-sampling equipment is moderately expensive, and its use is
restricted in wilderness settings.
1.2.4 Long-Term Monitoring
Long-term monitoring of stream or lake chemistry usually involves collection
of water samples at regular intervals from weekly to quarterly or even annually, with the primary purpose of detecting trends that reflect an environmental change over time. How quickly a trend can be detected depends on the
strength of the trend (the rate of change) and the amount of intra-annual and
interannual variability in the water chemistry. It is generally possible to detect
a change of smaller magnitude under conditions of less variability and longer
period of record. The likelihood of detecting a significant trend in the concentration of a given water chemistry variable will be determined in large part by
the length of the monitoring period. In the event of a small-to-moderate change
in chemistry, it may take 10 to 20 years, or more, of monitoring data to document a significant change.
An effective monitoring plan stems from a series of questions and constraints that sequentially focus the plan into specific elements that are well
defined and unambiguous. Because information is gained during implementation of a monitoring plan, it is often desirable to revisit a number of elements of
the plan to continuously refine and update the monitoring activities. In addition, external factors such as changes in monitoring technology, analytical
methods, and regulations will often impinge on the design and execution of
the monitoring. For these reasons, routine (e.g., annual) reviews of the results
and methods should be incorporated into the monitoring plan. However, if
trend detection is one component of the plan, care should be exercised in making changes to the program that might compromise the integrity of the data
and the ability to use earlier data to infer statistically significant changes in
water quality.
1.2.5 Other Uses of Resulting Data
Surface water quality data can also be used to support process-based modeling studies using a watershed model such as MAGIC or the Photosynthesis and
Evapotranspiration–Biogeochemistry (PnET-BGC) model. Such models can be
used to hindcast preindustrial chemistry to determine whether and to what
21
based on the rate of change in water level, which is usually not constant over
time. Samples collected during events can then be evaluated using the flow
measurements recorded at the times of sample collection to optimize the selection of samples for chemical analysis. This approach offers the opportunity to
greatly reduce sampling costs with minimal loss of information. However, the
use of automatic-sampling equipment is moderately expensive, and its use is
restricted in wilderness settings.
1.2.4 Long-Term Monitoring
Long-term monitoring of stream or lake chemistry usually involves collection
of water samples at regular intervals from weekly to quarterly or even annually, with the primary purpose of detecting trends that reflect an environmental change over time. How quickly a trend can be detected depends on the
strength of the trend (the rate of change) and the amount of intra-annual and
interannual variability in the water chemistry. It is generally possible to detect
a change of smaller magnitude under conditions of less variability and longer
period of record. The likelihood of detecting a significant trend in the concentration of a given water chemistry variable will be determined in large part by
the length of the monitoring period. In the event of a small-to-moderate change
in chemistry, it may take 10 to 20 years, or more, of monitoring data to document a significant change.
An effective monitoring plan stems from a series of questions and constraints that sequentially focus the plan into specific elements that are well
defined and unambiguous. Because information is gained during implementation of a monitoring plan, it is often desirable to revisit a number of elements of
the plan to continuously refine and update the monitoring activities. In addition, external factors such as changes in monitoring technology, analytical
methods, and regulations will often impinge on the design and execution of
the monitoring. For these reasons, routine (e.g., annual) reviews of the results
and methods should be incorporated into the monitoring plan. However, if
trend detection is one component of the plan, care should be exercised in making changes to the program that might compromise the integrity of the data
and the ability to use earlier data to infer statistically significant changes in
water quality.
1.2.5 Other Uses of Resulting Data
Surface water quality data can also be used to support process-based modeling studies using a watershed model such as MAGIC or the Photosynthesis and
Evapotranspiration–Biogeochemistry (PnET-BGC) model. Such models can be
used to hindcast preindustrial chemistry to determine whether and to what