The Pennypack Creek Watershed, located in southeastern Pennsylvania, is a heavily urbanized 56-square-mile watershed that crosses 12 municipal boundaries including the city of Philadelphia. More than 300,000 people live in the watershed. Fast growth and land development have increased pollutant loads, impaired water quality, reduced natural riparian vegetated buffers, and resulted in recurrent flooding.
The Center for Sustainable Communities at Temple University has undertaken an extensive research project on the watershed, with funding from the William Penn Foundation, Federal Emergency Management Agency (FEMA), and Pennsylvania Department of Environmental Protection. A comprehensive inventory of the watershed's natural and built environment was created with funding from the United States Environmental Protection Agency (EPA) Regional Vulnerability Assessment (ReVA) program.
The ReVA program has made tremendous progress in integrating large-scale spatial databases for ecological vulnerability assessment. Temple's inventory of the Pennypack Creek Watershed is part of an effort to scale down ReVA's methods and tools to assess smaller watersheds. ArcGIS served as the primary data collection and analysis platform for a multidisciplinary team of researchers including urban planners, landscape architects, geologists, civil engineers, economists, and biologists.
How the Inventory Was Developed
Data layers covering the physical (geology, soil, slope), biological (fish, insect populations), chemical (pollutant loads, dissolved oxygen), hydrological (rainfall, stream flow), demographic, and land-cover/land-use features were collected for the entire watershed and its 49 subbasins. Subbasins were delineated from stream line files based on stream order and topographic elevation data using the Watershed Modeling System (WMS) 7.1 and HEC-GeoRas software. [HEC-GeoRas is an ArcView 3.2 extension developed by the U.S. Army Corps of Engineers' Hydrologic Engineering Center (HEC). It prepares GIS data for import into the HEC River Analysis System (HEC-RAS) and generates GIS data from RAS output.]
Natural Resources Inventory
Biological data for the watershed was collected from Philadelphia Water Department monitoring stations. Data on fish species, insect habitat, and macroinvertebrates provided indicators of water quality and the suitability of the streams and riparian areas to support a wide variety of species. Sampling data was only available at 20 locations within the watershed. In ArcGIS, sampling points were assigned to associated subbasins. The spatial tools in ArcGIS allow statistical analysis of the relationships between various land-use patterns and water quality or biological integrity.
An essential tenet of landscape ecology and the EPA's ReVA analysis is that landscape patterns, particularly human-influenced landscape change, affect ecological processes. The spatial analysis tools in ArcGIS, combined with freely available specialized extensions, allow for a detailed understanding of the effect of landscape change on ecological integrity and water quality.
Hawth's Analysis Tools for ArcGIS, created by H. L. Beyer, are a collection of free tools for landscape measurement that extends the functionality of ArcGIS 8 and 9 that were used in developing the inventory. For example, the Count Points in Polygon tool was used to calculate the number of bridges, culverts, dams, and discharge points within each subbasin, and the Sum Line Lengths in Polygons tool was used to calculate the length of roads within buffer distances from streams (30 and 100 feet) and to calculate the extent of impaired riparian buffers along each segment of the waterway. This data can assist in prioritizing stream segments for mitigation or restoration efforts as well as indicate the impact of stream impairments on water quality.
The ModelBuilder feature in ArcGIS was used to perform many of the repetitive geoprocessing steps. As part of the hydrologic modeling of the watershed, Curve Numbers (CN) were calculated for each subbasin of the watershed. Developed by the Soil Conservation Service [agency of the United States Department of Agriculture now known as the Natural Resources Conservation Service (NRCS)], CN are a measure of the storm water runoff potential for a drainage area. Calculation of CN involved using classified land-use data as well as soil data (in hydrologic soil groups) in ArcCN-Runoff, a tool developed by Min-Lang Huang of the Kansas Geological Survey and Xiaoyong Zhan of the University of Kansas. ArcCN-Runoff can be downloaded from the Esri ArcScripts site (www.esri.com/arcscripts). CN were calculated for each distinct land cover-soil group type and aggregated to produce composite CN for each subbasin.
Flood hazard mitigation is one of the purposes of a comprehensive watershed inventory. ArcGIS tools, combined with high-resolution digital orthophotography, provide an important tool in hazard mitigation planning. The Q3 Flood Data from the Flood Insurance Rate Maps was collected from FEMA in digital form. Building footprints for all buildings in the watershed were digitized from high-resolution (1.5-square-foot resolution) digital orthophotography provided by the Delaware Valley Regional Planning Commission (DVRPC). When floodplain and building footprint layers were overlaid, ArcGIS tools were used to count the number of buildings in the floodplain.
Built Environment Inventory
In order to understand the human influences on the watershed, demographic, and land-use datasets were collected. Demographic data was collected at the census block group level for 1990 and 2000 from the United States Census Bureau Web site. Census TIGER/Line boundary files for 1990 and 2000 were also downloaded from the Census Bureau to identify block groups located in the watershed. Data on population, number of housing units, median income, population density, and housing-unit density was summarized for each of the 49 subbasins. Across the subbasins, data showed wide variation in new housing construction. Demographic data will also be combined with land-use change data to produce predictions of future land-use scenarios.
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