Impervious Surface: Water Quality Index

Summary

Proportion of watershed covered by impenetrable materials such as roads, parking lots, and buildings preventing water from leaching directly into the soil. Water quality is affected by impervious surface development in watersheds. The more impervious surfaces are developed, the greater the chance that water quality will be degraded.

General Information about this Indicator

What is it?:

Impervious surface is a measure of land cover. Water quality is affected by impervious surface development in watersheds. The more impervious surfaces are developed, the greater the chance that water quality will be degraded. It is derived from the National Land Cover Database using satellite imagery primarily from Landsat. Images are analyzed to reveal 16 land cover classes, including: water, developed, barren, forest, shrubland, herbaceous, planted/cultivated, and wetlands. Each land cover class is assigned a value for percent imperviousness based on a 30*30km resolution raster data set (USGS National Landcover Database). It is important to note that the percent impervious surface measurement is an estimate of imperviousness and not a direct measurement.

This indicator covers a process category and serves as a potential measure of impact of development on water quality.

Why is it important?:

Impervious cover is a relatively easily measured metric that is valuable for watershed planners, storm water engineers, water quality regulators, economists, and stream ecologists (Schueler et al. 2009). It also acts as a measure of development and growth. Direct impacts of impervious surface include changes in land cover, hydrology, geomorphology, and water quality. Indirectly impervious surface impacts stream ecology, species richness, the economy, policy, and social well-being and human health. Bellucci (2007) cites multiple papers documenting the influence of land cover change on stream health, biotic integrity, and runoff; stating that increases in urbanization results in stormwater runoff that contributes to "flashier hydrograph, elevated concentrations of pollutants transported from impervious surfaces to streams, altered channel morphology, and reduced biotic integrity with dominance of more tolerant species."

What can Influence or Stress Condition?:

This indicator has a direct connection between stress and percent impervious surface. Therefore, development or conversion of land from "natural" to developed land is the only thing that could alter this condition. Furthermore, as stated previously, changes in land cover can indirectly affect geomorphology, water quality, and ecosystem health in terms of native species richness.

Climate change may influence the resulting scores (RGA, WQI, Etc) from this indicator by altering the timing and amount of precipitation as well as drought. Climate predictions result from a combination of scenarios and climate models that integrate estimates of greenhouse gas emissions and how the climate system will respond to these emissions. Therefore, variation within the predictions may result in different policy implications and actions. Furthermore, we are likely to see variation in the location, amount, and timing of precipitation rather than homogenous responses across the globe.

Target or Desired Condition:

There are many estimates for a threshold of percent impervious surface, beyond which, measurable damage to stream systems is endured. Wang et al. (2003) estimate that between 6-11% impervious area, major changes in stream fish could occur. Fitzgerald et al. (2012) estimate increased sensitivity of stream ecosystems at between 5-10% impervious surface. Hilderbrand et al. (2010) suggest that within their study area, once percent impervious area reaches 15% a loss of nearly 60% of benthic macroinvertebrate taxa could occur. Schiff et al. (2007) calculate that above a critical level of 5% impervious surface, stream health declines. However, Allan (2004) makes the argument that although there is strong influence on stream health and land cover change, direct associations are complex and depend on anthropogenic and natural gradients, scale, nonlinear responses, and the difficulty in parsing out impacts from today and the past.

Thus, modeled predictions that utilize actual monitoring data for regions of interest, the stream indicators of greatest concern, the main land cover type , and represent a range of possible outcomes may be more realistic (Schueler et al. 2009). Furthermore, Schueler et al. (2009) mention several caveats regarding the use of impervious surface as an indicator for stream hydrology and health. These caveats include: consideration of watershed scale, problems with forming relationships between impervious surface and watersheds with major point source pollutant discharge or dams, importance in grouping watersheds within the same physiographic regions, and caution when applying models based on impervious surface when management practices are poor, especially in areas of low impervious cover (Schueler et al. 2009).

Additional Details:

Allan, J. D. 2004. Landscapes and Riverscapes: The Influence of Land Use on Stream Ecosystems. Annual Review of Ecology, Evolution, and Systematics 35:257-284.

Bellucci, C. 2007. Stormwater and aquatic life: making the connection between impervious cover and aquatic life impairments for tdml development in conneticut streams. Water Environment Federation.

Fitzgerald, E. P., W. B. Bowden, S. P. Parker, and M. L. Kline. 2012. Urban Impacts on Streams are Scale-Dependent With Nonlinear Influences on Their Physical and Biotic Recovery in Vermont, United States1. JAWRA Journal of the American Water Resources Association.

Fry, J., G. Xian, S. Jin, J. Dewitz, C. Homer, L. Yang, C. Barnes, N. Herold, and J. Wickham. 2011. Completion of the 2006 National Land Cover Database for the Conterminous United States. Photogrammetric Engineering & Remote Sensing 77:758-762.

Hilderbrand, R. H., R. M. Utz, S. A. Stranko, and R. L. Raesly. 2010. Applying thresholds to forecast potential biodiversity loss from human development. Journal of the North American Benthological Society 29:1009-1016.

McMahon, G. 2007. Consequences of Land-cover Misclassification in Models of Impervious Surface. Photogrammetric Engineering & Remote Sensing 73:1343-1353.

Schiff, R. and G. Benoit. 2007. Effects of impervious cover at multiple spatial scales on coastal watershed streams. Journal of the American Water Resources Association 43.

Schueler, T. R., L. Fraley-McNeal, and K. Cappiella. 2009. Is Impervious Cover Still Important? Review of Recent Research. Journal of Hydrologic Engineering 14:309-315.

Wang, L., J. Lyons, and P. Kanehl. 2003. Impacts of Urban Land Cover on Trout Streams in Wisconsin and Minnesota. Transactions of the American Fisheries Society 132:825-839.

Xian, G., C. Homer, J. Dewitz, J. Fry, N. Hossain, and J. Wickham. 2011. The chamge of impervious surface area between 2001 and 2006 in the conterminous United States. Photogrammetric Engineering & Remote Sensing 77:758-762.

Indicator Preparation Information

Data Sources:

Spatial data for the impervious surface analysis come from: United States Geological Survey National Land Cover Database

Spatial data for years 2001 and 2006
Change in percent imperviousness
Percent Imperviousness

Data Transformations:

Data were downloaded from the NLCD database in zip files that included raster files for import into ArcGIS. We used Arc GIS spatial software to display percent impervious surface throughout California. To illustrate effects on individual watersheds we used Hydrologic Unit Codes representing the smallest sub-watershed level (HUC 12). Zonal statistics within each sub-watershed resulted in means and standard deviation from which confidence intervals at 95% were calculated. To illustrate change in percent impervious surface, zonal statistics were performed on spatial data for the change of impervious surface between the years 2001 and 2006. Because of challenges in comparing NLCD datasets from these two years, we used spatial data calculated by Fry et al. (2011) and Xian et al. (2011) for our analysis.

Water Quality Index

The water quality index (WQI) is a measure of water quality based on seven aspects of water chemistry: Total dissolved solids, suspended particle matter, fecal coliform, nitrate, phosphate, the chloride to sulfate ratio, and the nitrate to total nitrogen ratio (Schiff and Benoit, 2007).

What is it?:

Why is it important?:

What can Influence or Stress Condition?:

Target or Desired Condition:

Additional Details:

Data Sources:

Data Transformations:

Indicator Map

Indicator Results

Assessments

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Data Resources

Spatial Data for Indicator Scores