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4.5 Spatial scale and aggregation of fine scale data to subwatershed

A desired feature when selecting indicators is that they can be scalable; that is, they are valid across different spatial and temporal scales. For instance, indicators reviewed on a larger (national) scale, can be also useful on the regional and local level. The Indicator Development for Estuaries Manual (US-USEPA, 2008) suggests that, whenever possible, it is always best to try to align local and regional programs with programs at a higher (i.e., national) spatial scale because this allows for future comparisons with data collected over the larger area. For example, the “benthic index”, which provides a quantification of the response of benthic communities to stress, is an example of a scalable indicator (Kurtz et al., 2001). Finding scalable indicators is a difficult task because many cost-effective methods to measure and summarize social, economic and ecological data are scale dependent (Hagan and Whitman, 2006).

Scalability of indicators may be more feasible in nested systems (e.g. South Yuba subwatershed -> Feather River Watershed -> Sacramento River Basin) than in non-nested ones. For nested systems the issues of sampling and data aggregation are more straightforward because of the direct spatial correlation from one scale to the next. Data can be sampled at one scale finer (e.g., monitoring site) than the question of interest and then “up-scaled” to a larger evaluation or reporting unit (e.g., subwatershed). Sampling and data aggregation in non-nested systems proves more difficult because the emergent properties of the systems are different and simply aggregating data will overlook the synergistic effects of systems (US Forest Service). In nested natural systems, cross-scale aggregation of environmental indicators may be more realistic than social or economic indicators. In contrast, social and economic indicators may be easier to aggregate when using nested political boundaries (e.g. municipality-county-state).

In the particular case of the US-USEPA SAB reporting framework, the Essential Ecological Attributes (EEAs) were successfully mapped onto structural, functional, and compositional characteristics of ecological systems at a variety of scales in order to assure coverage of different aspects of natural systems (Young and Sanzone, 2002). Furthermore, the EEAs and their subcomponents were checked to determine whether they would be relevant at several geographic scales (ecoregion, 1000 km2; regional landscape, 100 km2; small watershed or ecosystem, 10 km2; reach or stand, <1 km2). Overall, it was found that all the components of the SAB reporting framework were relevant to each geographic scale (Young and Sanzone, 2002), which is important because the SAB approach is the basis for the Watershed Assessment Framework and the Report Card.

Several different nested geographic scales at which aggregated indices can be developed include: (a) whole ecosystem/watershed, (b) primary subsystem habitat types (e.g., uplands, wetlands, in-stream), (c) categories of parameters within habitat types (e.g., wetland water quality), and (d) parameters within habitat types (e.g., in-stream nitrogen concentration). For the Feather River Watershed we reported indicator values and aggregated values to goals and objectives at the subwatershed extent (e.g., North Yuba River). The Report Card provides a method for translating characteristics at the site, reach, or creek drainage scale to the river basin and state scale.

The technique for reporting to the subwatershed level depended on the geographic type of data collected. Many of the datasets such as the water sampling information were collected at point localities, for instance a monitoring station on a stream. In this case, these data were assigned to subwatersheds by a GIS operation of overlaying the points on the polygon boundaries of the subwatersheds, and averaging values within a subwatershed. Some of the datasets, such as the fire history information, were originally represented by vector polygon GIS coverages. These were intersected with a finer-scale polygon layer for analysis based on hydrological planning watershed units that nest into each subwatershed. Values were then reported to the subwatershed level by averaging across all planning units within each subwatershed. Finally, some datasets were developed from raster surface layers such as land cover data which exhaustively covered the entire watershed. In these cases, the derived data (e.g. carbon stock values) were reported to a subwatershed by averaging the values for all pixels within a subwatershed.