This indicator measures the average percent of non-impervious cover within each catchment. It originates from the 2019 National Land Cover Database percent developed impervious layer.
Reason for Selection
Impervious cover is easy to monitor and model, and is widely used and understood by diverse partners. It is also strongly linked to water quality, estuary condition, eutrophication, and freshwater inflow. The 90% permeable surface threshold (i.e., 10% impervious) is a well-documented signal of major, negative changes to aquatic ecosystems (Schueler et al. 2009). The 95% permeable surface threshold (i.e., 5% impervious) has been documented to impact Piedmont fish [tricolor shiner (Cyprinella trichroistia), bronze darter (Percina palmaris), Etowah darter (Etheostoma etowahae)] (Wenger et al. 2008) and estuarine species [blue crab (Callinectes sapidus), white perch (Morone americana), striped bass (M. Saxatilis) and spot (Leiostomus xanthurus)] (Uphoff Jr. et al. 2011).
Input Data
- Base Blueprint 2022 extent
- 2019 National Land Cover Database (NLCD): Percent developed imperviousness
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National Hydrography Dataset Plus (NHDPlus) Version 2.1 medium resolution catchments (note: V2.1 is just the current sub-version of the dataset generally called NHDPlusV2 - view the user guide for more information)
NHDPlus V2.1 Medium Resolution Catchments
A catchment is the local drainage area of a specific stream segment based on the surrounding elevation. Catchments are defined based on surface water features, watershed boundaries, and elevation data. It can be difficult to conceptualize the size of a catchment because they vary significantly in size based on the length of a particular stream segment and its surrounding topography—as well as the level of detail used to map those characteristics.
More specifically, the NHDPlus V2.1 medium resolution catchment dataset used in this indicator incorporates snapshots of a) surface water features from the medium-resolution (1:100K scale) National Hydrography Dataset b) watershed boundaries from the Watershed Boundary Dataset, and c) the National Elevation Dataset 30 m digital elevation model.
To learn more about catchments and how they’re defined, check out these resources:
Mapping Steps
- Calculate percent impervious for each NHDPlus catchment using the NLCD 2019 impervious surface layer and the ArcPy Spatial Analyst Zonal Statistics “MEAN” function. The Zonal Statistics Mean calculates the average of the impervious surface values in each catchment, and assigns that value to all the cells inside that catchment.
- Convert percent impervious to percent permeable using the formula [percent permeable = 100 - percent impervious] to maintain consistent scoring across Southeast indicators (high values indicate better ecological condition).
- Reclassify the above raster into 4 classes, seen in the final indicator values below.
- As a final step, clip to the spatial extent of Base Blueprint 2022.
Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint 2022 Data Download under BlueprintInputs > BaseBlueprint2022 > 6_Code.
Final Indicator Values
Indicator values are assigned as follows:
- 4 = >95% of catchment permeable (likely high water quality and supporting most sensitive aquatic species)
- 3 = >90-95% of catchment permeable (likely declining water quality and supporting most aquatic species)
- 2 = >70-90% of catchment permeable (likely degraded water quality and not supporting many aquatic species)
- 1 = ≤70% of catchment permeable (likely degraded instream flow, water quality, and aquatic species communities)
Known Issues
- This indicator may not account for differences in permeability between different types of soils and land uses.
- The catchment boundaries are inconsistent in how far they extend toward the ocean. As a result, this indicator does not consistently apply to estuaries, coastal areas, and barrier islands in different parts of the Southeast.
- The catchment boundaries cross the United States/Mexico border, but the NLCD impervious data does not; as a result, the values along the United States/Mexico border are only based on the portion of the catchment where there are NLCD impervious values.
- The NLCD percent impervious layer contains classification inaccuracies that may cause this indicator to overestimate or underestimate the amount of permable surface in some catchments.
Disclaimer: Comparing with Older Indicator Versions
There are numerous problems with using Southeast Blueprint indicators for change analysis. Please consult Blueprint staff if you would like to do this (email hilary_morris@fws.gov).
Literature Cited
Schueler, T., Fraley-McNeal, L., and Cappiella, K. 2009. ”Is Impervious Cover Still Important? Review of Recent Research.” J. Hydrol. Eng. 14, SPECIAL ISSUE: Impervious Surfaces in Hydrologic Modeling and Monitoring, 309–315.
Uphoff Jr. JH, McGinty M, Lukacovic R, Mowrer J, Pyle B. 2011. Impervious surface, summer dissolved oxygen, and fish distribution in Chesapeake Bay subestuaries: linking watershed development, habitat conditions, and fisheries management. North American Journal of Fisheries Management 31:554-566.
U.S. Environmental Protection Agency (USEPA) and the U.S. Geological Survey (USGS). 2012. National Hydrography Dataset Plus 2. http://www.horizon-systems.com/nhdplus/].
U.S. Geological Survey (USGS). Published June 2021. National Land Cover Database (NLCD) 2019 Land Cover Conterminous United States. Sioux Falls, SD. [https://doi.org/10.5066/P9KZCM54].
Wenger, S. J., J. T. Peterson, M. C. Freeman, B. J. Freeman, D. D. Homans. 2008. Stream fish occurrence in response to impervious cover, historic land use and hydrogeomorphic factors Canadian Journal of Fisheries and Aquatic Sciences 65, 1250-1264.
Yang, L., Jin, S., Danielson, P., Homer, C., Gass, L., Case, A., Costello, C., Dewitz, J., Fry, J., Funk, M., Grannemann, B., Rigge, M. and G. Xian. 2018. A New Generation of the United States National Land Cover Database: Requirements, Research Priorities, Design, and Implementation Strategies, ISPRS Journal of Photogrammetry and Remote Sensing, 146, pp.108-123. [https://doi.org/10.1016/j.isprsjprs.2018.09.006].