4.6 Temporal scale and aggregation

Estimates of status and trend for a given indicator must also consider temporal aggregation. The temporal scale includes both aggregation within a year and among years.

How the data are summarized within a year depends on the specific data type. Some data are collected frequently throughout the year (e.g., continuous temperature or flow data). In such cases periodic behavior should be accounted for before aggregation takes place, and these methods are described in detail in section 4.3 (Trend Analysis). For many indicators there is only one record per site per year and there is no aggregation to consider within the year. The temporal scale or resolution of the data can affect its meaning. Higher resolution (i.e., more times at which data were collected) will tend to lead to a more accurate assessment of condition/status and change in condition than a single time-point measurement.

Status refers to the “current state,” and most often this refers to the state for a specific year. However, if reporting occurs only every few years the status should reflect the average status since the last report, or the status for some recent time window (e.g., 5 years). In the case of this Report Card, the best and most recent available data were used. In some cases, these data were several years old.

It is insufficient to simply assess the current status, without assessing whether or not a trend exists or vice versa. These two pieces of information together provide far more useful tool for decision makers. It is important to consider the time-frame (i.e., number of years) within which to evaluate trends. In most cases there are insufficient data to allow much choice, but as more data are collected it is possible to have scenarios where the recent trend is much different from the older trend, imagine a shift in the slope from negative before restoration to positive after restoration. It may be necessary to limit the analysis to the more recent years or to weight scores from recent years more heavily. Another strategy is to use piece-wise regression to allow different windows of time to have different slopes. Section 4.3 provides detailed information about how to complete trend analysis.