Applying the EPA's Regional Vulnerability Assessment (ReVA) Approach to the Pennypack Creek Watershed

Full text

Applying the EPA’s Regional Vulnerability
Assessment (ReVA) Approach to the
Pennypack Creek Watershed
Final Report
Edited by
John A. Sorrentino
Jeffrey Featherstone
Md Mahbubur R Meenar
The Center for Sustainable Communities
Temple University Ambler College
January, 2007

Applying the EPA’s Regional Vulnerability Assessment (ReVA) Approach to the
Pennypack Creek Watershed
Table of Contents
List of Tables ………………………………………………………………………………….…..……. 3
List of Figures ………………………………………………………………………………….….…… 4
Executive Summary …………….………………………………………………………….……….... 5
1. Introduction: Objectives of the Study ………………………………….……………….….…… 6
2. Applications of ReVA Methodology to the Pennypack Creek Watershed (PCW) ….….. 7
2.1. Developing a GIS-Based Watershed Data Inventory ……………………….….... 9
2.1.1. Natural Resources Inventory
2.1.2. Built Environment Inventory
2.2. The State of the Watershed ……………………………………………. ..…………. 13
2.3.Ecological Indicators in the PCW …………………………………………………… 20
2.3.1. Water Volume
2.3.2. Water Quality
2.3.3. Biological Integrity
2.3.4. Impervious Surface
2.4. Two Land-Use Scenarios …………………………………………………….…….… 46
2.4.1. Trend Development
2.4.2. Smart Growth
2.5. Differential Impacts of Land-Use Scenarios ………………………………………. 55
2.5.1. Water Volume
2.5.2. Energy Use, Air and Greenhouse Gas Emissions, Water Quality
and Biological Integrity
2.6. Pennypack Ecological Vulnerability Assessment (PEVA) ……………………… 68
2.7. Relation of PEVA to Pennsylvania’s Sustainability Indicators ………………… 72
3. Potential Applications to Other Watersheds ……………………………………………….… 73
3.1 Data & Metadata
3.2 Models
3.3 Scenario Generation
3.4 Impacts Assessment
4. The Involvement of Local Officials ……………………………………………………..……….. 80
5. Conclusions …………………………………………………………………………….…………… 80
References ……………………………………………………………………………………………… 82
Appendices …………………………………………………………………………...………………… 86
A.1. ReVA Web Page Summaries …………………………… …………………………. 86
A.1.1. Data Preparation
A.1.2. Examples of Variables Used to Estimate Vulnerability
A.1.3. Assessment Questions
A.1.4. Future Vulnerability
A.1.5. Multiple Criteria Decision-making
A.1.6. Demonstrations
A.2. Data & Metadata: Technical Details & Tables ……………………………………. 101
A.2.1. List of Shape Files
A.2.2. Shape File Metadata
A.2.3. List of Databases
A.2.3. DBF File Metadata
A.2.5. Attachment: Data CD
A.3. Ecological Indicators: Technical Details & Tables ……………………………… 167
A.3.1. Graphs and Tables for the Hydrological Modeling
A.3.2. Calculations for the Water Volume Indicator
A.3.3. Calculations for the Water Quality Indicator
A.3.4. Calculations for the Biological Integrity Indicator
A.3.5. Calculations for the Impervious Surface Indicator
2

List of Tables
Table 2.1.1. A List of GIS data layers
Table 2.2.1. Sub-basin Properties
Table 2.2.2. Reach Properties
Table 2.2.3. Rainfall Events Used for Calibration
Table 2.3.1. Interpreting Biological Integrity Scores
Table 2.3.2. Ranges of the IBI Score
Table 2.4.1. Forecasted Population Growth and Housing Unit Construction Needed
Table 2.4.2. Land Development Rates: Trend Development Scenario
Table 2.4.3. Land Development, Municipal Smart Growth Scenario
Table 2.4.4. Regional Smart Growth Scenario
Table 2.5.1. Peak Flows (cfs) for Varying Conditions
Table 2.5.2. Relative Importance of Suitability Criteria
Table 2.5.3. Fuel Economy & Emissions Rates of Baseline Vehicles
Table 2.5.4.Energy Use & Emissions from a Typical Automobile
Table 2.5.5. Energy Use & Emissions in Sprawl & Smart Growth
Table 2.5.6. Water Quality Index
Table 2.5.7. Biological Integrity
Table 2.5.8. Value of Ecosystem Functions/Services
Table 2.5.9. Impacts Summary
Table 2.7.1. PA Sustainability Indicators
Table 4.1. Municipalities Involved in PCW Stormwater Management
3

List of Figures
Figure 2.1.1. Clipping with Specified Buffer Distance
Figure 2.2.1. PCW Land-Use, 2000
Figure 2.2.2. The PCW Hydrology
Figure 2.3.1. PCW Baseflow Geology
Figure 2.3.2. PCW Drainage to the Rhawn St. Gauge
Figure 2.3.3. PCW Upstream of the UM-H STP
Figure 2.3.4. Peak Flows under Current Conditions
Figure 2.3.5. PWD Stations & Associated Sub-Basins: Chemical Data
Figure 2.3.6. %-Alkalinity: Sub-Basin Classification
Figure 2.3.7. %-Fecal Coliform: Sub-Basin Classification
Figure 2.3.8. %-Nitrate: Sub-Basin Classification
Figure 2.3.9. %-Phosphate: Sub-Basin Classification
Figure 2.3.10. %-Suspended Solids: Sub-Basin Classification
Figure 2.3.11. PWD Stations & Associated Sub-Basins: Macroinvertebrate Data
Figure 2.3.12. PWD Stations & Associated Sub-Basins: Fish Data
Figure 2.3.13. Biological Integrity for Macroinvertebrates: Sub-Basin Classification
Figure 2.3.14. Biological Integrity for Fish: Sub-Basin Classification
Figure 2.3.15. %-Impervious Surface: Sub-Basin Classification
Figure 2.4.1. Trend Development Land Use, 2030
Figure 2.4.2. Municipal Smart Growth Land Use, 2030
Figure 2.5.1. Flows from Trend Development
Figure 2.5.2. Sites for Smart Growth: Darker Is More Suitable
Figure 2.5.3. Sites for Sprawl: All but Dark Sites Acceptable
Figure 3.1 Generic Ecological Vulnerability Assessment Workflow
4

Executive Summary
The Center for Sustainable Communities (CSC) at Temple University was
engaged by the Pennsylvania Department of Environmental Protection to
determine whether the ecological modeling approach known as ReVA (Regional
Vulnerability Assessment) developed by the Environmental Protection Agency for
large ecosystems or river basins could be applied at a much smaller scale for
watersheds in Pennsylvania. The CSC chose the 57 square-mile Pennypack
Creek Watershed (PCW) in southeastern Pennsylvania for this application. The
CSC engaged a study team of scientists and researchers from universities,
nonprofit organizations, and consulting firms to conduct this analysis.
This report presents the results of this work. It includes a detailed assessment of
the ReVA methodology and its applicability to the PCW, the preparation of a
comprehensive Geographic Information System (GIS) database, the delineation
of ecological indicators, the preparation of land-use scenarios, and the evaluation
of the differential impacts of those scenarios on various watershed attributes.
The study team has concluded that the ReVA modeling approach can be scaled
down so as to be informative and appropriate for smaller watershed assessments
in the Commonwealth of Pennsylvania. While many larger-scale ReVA
assessment tools and data variables are too coarse for assessment at the local
level, it is feasible to use such processes with more refined local data to
accurately outline the impacts of alternative land-use and resource-allocation
decisions on ecological and other watershed attributes.
The approach developed for the PCW also can be used as a template for other
watersheds. While the PCW is a seriously impaired watershed in a dense urban
setting in the Greater Philadelphia region, the assessment protocol outlined in
this study can be accurately applied for less developed and more pristine
watersheds as well. The report outlines data needs and analytical tools for this
information transfer.
Finally, the ReVA modeling process also can be used to develop watershed
sustainability indicators. The study team outlines four broad indicator categories
that can be applied elsewhere and aggregated to larger regions in Pennsylvania.
While the lack of good time series information makes ecological evaluations
difficult, better models and more accurate digital information on topography and
hydrology allow researchers to more accurately assess many watershed
attributes.
5

1. Introduction: Objectives of the Study
Ecological assessment is a subject of paramount importance as the nation
moves to prevent further degradation of the environment. While many
environmental problems are global or regional in nature, the local level
affects them, and is affected by them. The US Environmental Protection
Agency (EPA) has undertaken regional and national assessments over the
past few decades. The Environmental Monitoring and Assessment Program
(EMAP) is a long-term research effort to enable status and trend
assessments of aquatic ecosystems across the U.S. The Mid-Atlantic
Integrated Assessment (MAIA) project incorporates data from state,
regional and national environmental monitoring programs into regional
assessments for the Mid-Atlantic region. An outgrowth of MAIA is the
Regional Vulnerability Assessment (ReVA) Program. The evolution of this
monitoring and assessment is what the current research has as its focus.
This project was a collaboration among individuals from the federal, state and
local levels of government, local universities, and non-government organizations
(NGOs). The expertise of the research participants spanned the natural and
social sciences. The list of participants is:
Temple University Faculty
A.S.M. Abdul Bari, Center for Sustainable Communities (CSC)
Michel Boufadel, Civil and Environmental Engineering
Jeffrey Featherstone, CSC
Shirley Loveless, CSC
Md Mahbubur R Meenar, CSC
Richard Nalbandian, CSC
Jon Nyquest, Geology
Kurt Paulsen, Community & Regional Planning
John Sorrentino, Economics
Susan Spinella, CSC
Laura Toran, Geology
Temple University Graduate Student Research Assistants (GSRA)
Andreea Ambrus
Dennis Dalbey
Jesse Sherry
Ibrahim Ibrahim
Straso Jovanovski
Melanie Martyn
Grisselle Rodriguez-Herrera
Lilantha Tennekoon
6

Faculty from Other Universities
Kathi Beratan, Duke University
Amy Liu, West Chester University
Peter Petraitis, University of Pennsylvania
Philadelphia Water Department
Jason Cruz, Office of Watersheds
Joanne Dahme, Office of Watersheds
Howard Neukrug, Office of Watersheds
Others
Sean Greene, F.X. Browne, Inc.
David Hart, Acadamy of Natural Sciences
David Robertson, Pennypack Ecological Restoration Trust (PERT)
Puneet Srivastava, Academy of Natural Sciences
Shandor Szalay, F.X. Browne, Inc.
Jim Thorne, Natural Lands Trust
Project Advisors
Donald Brown, Pennsylvania Dept. of Environmental Protection (PA DEP)
Libby Dodson, PA DEP
Deborah Forman, Environmental Protection Agency, Region III
Stanley Laskowski, EPA, Region III
Richard Paste, EPA, Region III
Anita Street, EPA, Headquarters
Betsy Smith, EPA, Research Triangle Park
The objectives of the project were clearly stated at the outset: (1) Determine
whether the ReVA modeling approach can be scaled down so as to be
informative and appropriate for watershed assessment in the Commonwealth of
Pennsylvania; (2) Determine whether the assessment model developed for the
Pennypack can be used as a "template" for assessments in other watersheds in
Pennsylvania and elsewhere; and (3) Determine whether the ReVA modeling
process can be used to develop watershed sustainability goals and indicators.
The Pennypack Creek Watershed (PCW) was used to assess the applicability of
ReVA methods.
Section 2. describes the PCW and applies ReVA methods to some aspects of
the watershed’s ecology. The reader interested in a more detailed account of
ReVA methodology is invited to visit Appendix A.1. and the references cited
there. The methods include data acquisition and generation, metadata creation,
indicator development, scenario generation and impact assessment. These
7

ultimately lead to vulnerability assessment. Section 3. discusses what aspects of
the methodology of Section 3. can and cannot be directly applied to other small
watersheds. Section 4. contains a brief discussion of the potential for informing
and organizing officials from neighboring municipalities with the purpose of
inducing watershed-wide cooperation in avoiding ecological vulnerabilities. Some
Conclusions follow section 4., and References direct the reader to sources of
further information. The Appendices contain more detail on some of the topics in
the text.
2. Applications of ReVA Methodology to the Pennypack Creek Watershed
As noted above, the ReVA program evolved from two EPA programs that began
earlier: EMAP & one of its sub-programs, MAIA. A concise overview of ReVA is
given in the words of the EPA:
The ReVA Program focuses on regional scale integrated assessment with the aim
of assisting decision makers in identifying and locating both environmental
resources and the conditions that are stressing those resources. ReVA
strengthens the decision-making process by identifying the current status and
relationships between stressors and sensitive environmental resources and
estimating the environmental changes that follow from specific actions. (p.1, US
EPA 2006b)
The PCW research team has examined these data and methods with the
charge of determining what aspects of EMAP/MAIA/ReVA methodology are
relevant to watersheds smaller than the USGS hydrologic accounting units
in the Hydrologic Unit Code (HUC) used by the MAIA research team.
(Jones et al. 1997) The application of ReVA methodology in this section will
essentially follow the ReVA assessment questions: (1) What is the overall
condition of the region? (2) What is the relative environmental condition
given all variables or a subset (e.g. those related to water quality)? (3) What
and where are the current most pressing environmental risks for a region?
(4) What and where is the greatest risk in the future likely to be? (5) Where
are the strategic planning or restoration priorities for a region? (US EPA
2006b)
Questions (1) and (2) are answered in the description in Section 2.2. and the
relatively exhaustive data accumulation recounted in Section 2.1. and Appendix
A.2. The answer to Question (3) took the form of assessing the relative values of
indicators measured in Pennypack Creek as described in Section 2.3. The
diagnosis is that certain stressors have put considerable pressure on the Creek’s
resources with the result that the Creek is designated “moderately impaired.” The
future risks referred to in Question (4) took the form of hydrological impacts
resulting from the residential growth scenarios in Section 2.4, and other
environmental impacts discussed in Section 2.5 based on alternative placement
scenarios. Question (5) is dealt with in Section 2.5 and in the Conclusion.
8

2.1. Developing a GIS-Based Watershed Data Inventory
Watershed management and vulnerability assessment require an
interdisciplinary approach to a complex problem. Comprehensive Geographic
Information Systems (GIS) inventories of the natural and built environment
provide watershed managers with the data, tools, and techniques to manage the
complexities. The tools of GIS and spatial analysis allow decision-makers and
citizens to understand and visualize the many features of a watershed, from land-
use patterns to species diversity to flood-hazard areas. The powerful spatial
analytic features of the ArcGIS1 system, combined with increasingly available
high-quality digital data should prove of great benefit to all concerned with
managing the complex ecological, economic, and political systems involved in a
watershed.
The Center for Sustainable Communities (CSC) has developed a comprehensive
inventory of the PCW's natural and built environment. ArcGIS served as the
primary platform for data collection and analysis from a multidisciplinary team of
researchers, including urban planners, landscape architects, geologists, civil
engineers, economists and biologists. Data layers covering the physical
(geology, soil, slope), biological (fish, insect populations), chemical (pollutant
loads, dissolved oxygen), hydrologic (rainfall, runoff, stream flow), demographic,
and land-cover/land-use features were collected for the entire watershed. Table
2.1.1. shows a list of data layers generated for this project.
In order to undertake a more refined assessment of the 56-square-mile PCW, the
study team subdivided the PCW into 49 smaller sub-watersheds or sub-basins,
which would correspond to the size and location of the first-order streams within
its boundaries. Sub-basins 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.2
In defining sub-basins, the study team sought to create them to allow
aggregation and dis-aggregation from current and past studies. Originally, the
watershed was divided into 10 sub-basins to correspond with the CSC's
hydrologic and hydraulic modeling studies. Those 10 sub-basins provided the
basis for preparation of detailed maps of the watershed's floodways, and 100-
year and 500-year floodplains. Those 10 were further sub-divided into 20
watersheds to correspond with the Philadelphia Water Department's (PWD) 20
water-quality sampling sites. In order to improve the potential richness of the
1 ArcGIS is a GIS software package developed by the Environmental Systems
Research Institute (ESRI) Inc.
2 HEC-GeoRas is an ArcView 3.2 (ESRI software) 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.
9

ecological assessment, the basins were ultimately sub-divided into the 49
smaller, first-order sub-watersheds.
2.1.1. Natural Resources Inventory
Biological data for the watershed were collected from monitoring stations of the
PWD. Data on fish species, insect habitat and macroinvertbrates provided
indicators of water quality and the suitability of the streams and riparian areas to
support a wide variety of species. Sampling data were available at 20 locations
within the watershed. ArcGIS was used to assign those sampling points to the
sub-basin(s) draining to the sampling point. The spatial tools within ArcGIS thus
allow statistical analysis of the relationships between various land-use patterns
and water quality or biological integrity.
Table 2.1.1. A List of GIS data layers
Data Source Year
Biological Data
Fish Philadelphia Water Department (PWD) 2002
Habitat PWD 2002
Macroinvertebrate PWD 2002
Water Related Data
Wetland Delaware Valley Regional Planning Commission
(DVRPC) 1981
Bridge & Culvert Center for Sustainable Communities, Temple
University(CSC) 2005
Dam PWD 1999
Riparian Buffer Heritage Conservancy 2002
Effluent
Concentration PWD 2003
Discharges &
Withdrawals Delaware River Basin Commission (DRBC) 1996
Stream Center for Sustainable Communities, Temple
University(CSC) 2004
Floodplain Federal Emergency Management Agency (FEMA) 1996
Geological Data
Bed Rock Geology DRBC 1998
Soil DRBC Unknown
Base Flow PWD 1998
Demographic Data
Household Density US Census Bureau 1990/2000
Median Household
Income US Census Bureau 1990/2000
Population Density US Census Bureau 1990/2000
Land Features Data
Land use DVRPC 1990/1995/20
00
Land Cover United States Geological Survey (USGS) 2001
Tree Canopy Density USGS 2001
Impervious Surface Pennsylvania State University, Dr. Toby Carlson 1985/2000
10

Slope CSC 2004
Road Density CSC 2005
Forest Fragmentation CSC 2005
An essential feature of the field of "landscape ecology" and the EPA's ReVA
analysis is that landscape patterns – particularly human-influenced landscape
change – affect ecological processes. The ArcGIS tools, combined with freely
available specialized extensions, allows for a detailed understanding of the effect
of landscape change on ecological integrity.
Hawthorne's Analysis Tools for ArcGIS (www.spatialecology.com) is one of a
number of free extensions for the ArcGIS 8 and 9 systems with specialized tools
for landscape measurement. The "Count Points in Polygon" tool, for example,
was used to calculate the number of bridges, culverts, dams, and discharge
points within each sub-basin. The tool, "Sum Line Lengths in Polygons," 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. Those data can assist in prioritizing stream segments
for mitigation or restoration efforts, as well as indicating the impact of stream
impairments on water quality. The Model Builder feature in ArcGIS was used to
perform many of the repetitive geo-processing steps. Figure 2.1.1. shows an
example of a Model, clipping with buffer distance.
Figure 2.1.1. Clipping with Specified Buffer Distance
11

As part of the hydrologic modeling of the watershed, Curve Numbers (CNs) were
calculated for each sub-basin of the watershed. The CN, developed by US
Department of Agriculture Natural Resource Conservation Service (formerly the
Soil Conservation Service), is a measure of the stormwater runoff potential for a
drainage area. Calculation of the CNs involved using land-use class data, as well
as data in hydrologic soil groups in the ArcCN-Runoff extension. CNs were
calculated for each distinct land-user/soil group type, and aggregated to produce
a composite CN for each sub-basin.
One of the purposes of a comprehensive watershed inventory is flood hazard
mitigation. ArcGIS tools combined with high-resolution digital ortho-photography
provide an important tool in hazard mitigation planning. The Q3 Flood Data from
the Flood Insurance Rate Maps (FIRMS) were collected from FEMA in digital
form. Building footprints for all buildings in the watershed were digitized from
high-resolution (1.5 sq.ft. resolution) digital ortho-photography provided by the
DVRPC. When flood-plain and building-footprint layers were overlain in ArcGIS,
software tools were used to count the number of buildings in the floodplains.
2.1.2. Built Environment Inventory
In order to understand the human influences on watershed, demographic and
land-use data were collected. Demographic data were collected at the Census
Block Group level for 1990 and 2000 from the US Census Bureau website.
Census TIGER/Line boundary files for 1990 and 2000 were also downloaded
from the Census to identify block groups located in the PCW. Data on the
population, number of housing units, median income, population density, and
housing-unit density were summarized for each of the 49 sub-basins. Data show
wide variation in new housing construction across the sub-basins. Demographic
data were combined with land-use change data to produce scenarios of future
land use.
A number of data sources were combined to produce a detailed picture of the
land uses and landscapes within the watershed. The 2001 National Land Cover
Database is a consistent land-cover database for the entire continental US at a
30m resolution, and was downloaded from seamless.usgs.gov. In addition, the
National Land Cover Database provides tree-canopy and impervious-surface
data at the same resolution. For Pennsylvania, higher-resolution impervious-
surface data was estimated by Dr. Toby Carlson of Penn State University and is
available for 1985 and 2000 to show changes in impervious surfaces within a
watershed. The DVRPC made available digital land-use data interpreted from
high-resolution digital ortho-photography. Data from 1990, 1995 and 2000 allow
for an assessment of land-use patterns and land-use change. Although few
areas of the country currently have available consistently-interpreted, high-
resolution land-use data from more than one time period, these data are
becoming increasingly available.
12

ArcGIS tools combined with high-quality land-use and land-cover data from
multiple sources allow for a detailed assessment of landscape patterns. Forests,
for example, play an important role in both species habitat and water quality.
Detailed measures of forest fragmentation can be used to assess ecosystem
threats and prioritize land conservation strategies. Using an ArcGIS extension
written by Kurt Paulsen (author of Section 3.4) and a collaborator, measures of
forest fragmentation are calculated for each sub-basin. Hawthorne's Tool was
used to calculate distances between forest patches, and the proximity of forested
patches to streams.
The next section puts these data and the ArcGIS tools to work in discussing the
hydrology and ecological indicators for the PCW.
2.2. The State of the Watershed
The PCW is a 56 square mile area located in southeastern Pennsylvania that
contains a population of about 300,000. It contains parts of Bucks, Montgomery
and Philadelphia Counties. Besides a section of Northeast Philadelphia, the
watershed resides in all or part of 11 municipalities: Abington, Bryn Athyn,
Hatboro, Horsham, Jenkintown, Lower Moreland, Rockledge, Upper
Southampton, Upper Dublin, Upper Moreland and Warminster. The topography
of the watershed is characterized by gently rolling hills in the headwaters,
moderately sloping valley in the central part of the watershed, and tidal flats
draining to the Delaware River. The elevation over the watershed ranges from
436 feet to less than 10 feet above sea level. The flow regimen in the Creek and
the interrelationships between surface and groundwater within its watershed are
complicated not only by development and other human activities within the basin,
but also by its complex environmental character. In particular, the bedrock
geology is highly diverse and the geologic history spans more than 600 million
years. There are great differences in the physical characteristics of the many
different rock types within the watershed. The Pennypack Creek system contains
roughly 79 miles of surface waters and is classified for the following uses: warm
water fishery, trout stocking fishery, aquatic life, water supply and recreation.
The climate of the region is characterized by warm summers and cold winters
with moderate intermediate seasons. The mean annual temperature is 54oF and
the average annual precipitation is 41.41 inches. (Meenar 2006)
Much of the PCW area was developed as a part of the “inner ring suburbs” of
Philadelphia from the 1950s to the 1980s. As can be seen in Figure 2.2.1., the
area is quite built up and hosts a myriad of land uses that impact energy use and
the air, water and biological integrity.
The water quality in the PCW is significantly influenced by the large Upper
Moreland-Hatboro Sewage Treatment Plant (UM-H STP). Its location is shown in
Figure 2.3.3. The UM-H STP is authorized by the PA DEP through an NPDES
permit and DRBC to discharge treated sewage at an average annual rate of
13

7.173 mgd and a maximum monthly flow of 9.08 mgd. According to the permits,
the STP is required to provide advanced secondary treatment and ultraviolet
disinfection prior to discharge to the Pennypack Creek. Discharge monitoring
reports (DMRs) provided by the plant operator to the DEP indicate that the STP
operates at or near its maximum design capacity. The service area of the STP
includes the Borough of Hatboro and portions of Horsham, Upper Dublin and
Upper Moreland Townships, Montgomery County, PA and a portion of
Warminster Township, Bucks County, PA. The UM-H STP is one of four point
sources of discharge in the watershed. The remaining three are small “package”
plants that do not have significant adverse impacts on water quality.
Air quality is dependent on the mix of land uses in the PCW. Except for Vacant,
Water, & Wooded, the land uses listed in the figure legend entail the use of
human-processed energy & materials for everyday functioning. Where electricity
in not the end-use energy source, fuels are generally burned in stationary or
mobile sources to provide goods and services while generating entropy and
waste materials. Ambient air quality over the PCW is generally good, and
affected mostly by mobile-source emissions.
Figure 2.2.1. PCW Land-Use, 2000
14

2.2.1. Hydrology
The interactions and relationships among the components of the hydrologic cycle
(precipitation, evapo-transpiration, runoff, etc) must be understood in order to
predict a stream’s responses to changes in any of those components, whether
natural or manmade. Rigorous hydrologic modeling was performed in the PCW
to gain such an understanding of the watershed’s hydrologic regimen.
It has been long and widely known that the changes in land cover that
accompany urbanization generally result in decreases in evapo-transpiration and
infiltration (and consequently in baseflow), and increases in surface runoff, (US
EPA 1993). What has also been long, but perhaps less widely realized, is that
the most effective or dominant channel-forming flow of a stream, i.e., that which
transport the largest total amount of sediment over a period of years, is the
bankfull stage (Walman and Miller 1960). This most effective of flows has a
recurrence interval of 1.5 years in the annual maximum flood series 9 and 0.9
years in the partial duration series in a large variety of streams (Dunne and
Leopold 1978).
The CNs used in the hydrologic modeling discussed below are measures of the
influence of the land cover characteristics on infiltration and runoff. CNs are
empirically derived, and depend on a combination of factors: vegetation types
and conditions; impervious cover; land use practices; and hydrologic soil groups
(HSGs). As forest gives way to pasture or cropland, and then the lawns in
subdivisions, as pavements and roofs are introduced, and as soils are
compacted, CNs increase in value, and so too does surface runoff.
Thus, the causal relations are clear – increased CN leads to increased runoff,
which in turn produces increases in the amount of flow in the above-mentioned
bankfull discharge (or conversely, the larger flows have reduced recurrence
intervals). This increased dominant channel-forming flow must then enlarge the
channel to accommodate the increased runoff. In other words, the stream flows
with recurrence intervals of 0.9 to 1.5 years (depending on the series used) are
larger and the channel must adjust itself accordingly. It will do this by eroding its
banks, and, where the gradients permit, by downcutting its bed as well.
During our field reconnaissance, examples of such channel enlargement, recent
and ongoing, were found throughout the watershed – in virtually all tributaries, as
well as the main stem. The most dramatic examples noted were those where the
stream beds of tributaries were founded in bedrock, leading to marked widening
of the channels. Such widespread channel erosion will produce corresponding
increases in sediment transport and thereby in the Total Suspended Solids
(TSS), load of the streams, one of the principal factors cited in the impairment of
stream quality in this and similar watersheds.
15

While the causal and qualitative relationships outlined above are clear, additional
research is needed to quantify the linkages. That is, we can model directly the
runoff increases that will result from specific increases in the CNs, but the
channel responses to those increased bankfull discharges, the consequent
erosion, and the resultant increases in TSS will require more study.
The Hydrologic Modeling
Figure 2.2.2. The PCW Hydrology
For hydrologic modeling, the U.S. Army Corps of Engineers’ (ACOE) software
HEC-HMS was used. The watershed was treated as consisting of 10 subbasins,
whose areas range from 2.6 to 8.3 mile2 with an average of 5.5 mile2. A CN was
computed for each subbasin based on Land Use Land Cover (LULC) data and
soil type data.
The outflows from the subbasins were assumed to pass through six Junctions as
available from HEC-HMS. The junctions are designated by the symbols C in
Figure 2.2.2. (e.g., junction 2C). They were connected to each other and to the
outlet of the watershed by six reaches (designated by the symbol R in Figure
2.2.2., (e.g., 3R). The routing of water flow through the reaches was conducted
16

using the Modified Puls method, which required evaluation of the number of
subreaches. In this work, the following approach was followed for each reach:
1. A Hec-RAS model was developed using multiple cross sections at a
spacing of 30 – 200 feet.
2. Different flow rates, varying from 100cfs to 30,000 cfs were routed.
3. The cumulative volume of water for each reach was recorded and a
storage-outflow table was developed for each reach (see details under the
section “reach properties”).
4. An average travel time was determined for each reach based on the
computational interval of 15 minutes. The number of sub-reaches was
then computed.
5. The number of sub-reaches from 4) was allowed to change by ± 20% in
matching simulated hydrographs to observed hydrographs at the USGS
station (Rhawn Street).
The values of the hydrologic parameters are reported in Table 2.2.1. and 2.2.2.
These values were obtained in a two step process, where the values computed
based on watershed characteristics (such as LULC, soil type, slope) were altered
by calibration of the model to the outflow at the Rhawn Street USGS station in
Philadelphia. In general, the differences between the final and initial values were
less than 5% for CN (Table 2.2.1.) and 20% for the time lags (Table 2.2.1.) and
the travel times in reaches (Table 2.2.2.).
Table 2.2.1. Sub-basin Properties
Basin Area CN Percent
Impervious Time lag
(mile2) (minute)
1B 8.314 80.53 13.34 126
3B 5.9627 77.93 11.64 116
2B 7.9365 80.03 21.32 122
4B 4.9918 74.3 2.37 95
5B 4.1826 77.45 7.41 102
6B 3.9409 77.7 6.8 85
7B 4.7719 74.92 5.14 98
8B 2.6074 74.97 22.39 128
9B 7.1235 73.13 25.49 145
10B 6.0329 76.71 34.97 182
17

Table 2.2.2. Reach Properties
Reach Length Width Slope Manning’s n
ID (feet) (feet) (ft/ft)
1R 17691 15 0.003 0.035
2R 15963 20 0.0017 0.035
3R 1782 25 0.0047 0.035
4R 16502 25 0.0008 0.035
5R 28211 30 0.0018 0.035
6R 18306 30 0.0008 0.035
Hydrologic Data
Topography plays a vital role in the distribution and flux of water and energy in a
watershed. The USGS has prepared 7½° quadrangle topographic maps at a
scale of 1:24,000 for most of the country, and a common contour interval is 10 ft.
This scale is generally considered the minimum scale in hydrologic modeling and
a tighter interval is preferred for accurate and detailed studies. With the help of a
research grant obtained for the Pennypack Creek Watershed Study (Meenar
2006), the CSC consultant, Aero2 Inc., created the digital ortho-photography and
2 ft-resolution elevation data. The aerial mapping was done in the non-growing
season, when foliage was off the trees. Aero 2 has undertaken the following
steps:
 Aerial Photography at 1”=660’ negative scale using Airborne GPS
technology flight;
 Ground Control Survey, performed by licensed land surveyor;
 Analytical Aero-triangulation, which performs image measurements to
achieve interior and exterior image parameters; and
 Stereo compilation and creation of new data.
Integration of GIS and hydrologic modeling connects geospatial data with
hydrologic process models describing how water moves through the
environment. GIS is commonly used for watershed delineation, runoff estimation,
hydrologic modeling, and floodplain mapping. In addition to the GIS-based data
inventory described in the previous section, the newly created GIS datasets
include 2003 digital ortho-photographs (1 ft pixel resolution), 2 ft resolution
elevation data such as DEM, Triangulated Irregular Network (TIN), and contour
intervals, updated stream networks, flow-paths, bridges and culverts, dams, and
building foot prints.
Research has indicated that the precipitation values (Herschfield 1961) widely
used in previous studies are no longer valid. These values were used in the
creation of existing Pennypack Flood Insurance Rate Maps (FIRMs) as well. It
has been well established that TP-40 systematically underestimated the extreme
18

precipitation events. This was due to a number of factors: the short average
duration of the precipitation records analyzed; the relatively small number of
weather stations; and the statistical distribution used to analyze the data. The
Temple researchers requested and received permission from FEMA to use more
recent data from the National Oceanic and Atmospheric Administration (NOAA)
Atlas 14, which can be accessed at: http://hdsc.nws.noaa.gov/hdsc/pfds/orb/pa_pfds.html.
This makes a very significant difference in the inputs to the hydrologic and
hydraulic models employed in the re-delineation of the floodplains in the
watershed. For instance, according to TP-40, the precipitation from the 100-year,
24-hour storm in our study area was expected to be 7.2 inches, and this number
has been codified in virtually all local stormwater management ordinances. The
more recent data indicates that the 100-year, 24-hour event is 8.75 inches, a
difference of more than 20%.
Streamflow data used for calibration were obtained from the USGS Station
01467048 located approximately at Rhawn Street. As mentioned previously, in
predicting the runoff resulting from the 100-year storm, the depth of rainfall
estimated by the NOAA Atlas 14 (8.75 inches) was used instead of the older TP-
40 study. This greater rainfall depth along with the new land use data resulted in
runoff peak values and volumes that are larger than those predicted in prior
studies in the PCW. At the location (Lat=40.1; Long=-75.3), the upper 95%
confidence gave 6.28, 7.19, 8.18, 10.81, for the 25, 50, 100, and 500 year
storms. The temporal distribution of rainfall pulses for the design storms for the
area are of the SCS Type II.
Calibration
The hydrologic modeling process entails developing an actual or hypothetical
design storm and then calculating the runoff and peak discharge for the selected
event. Eight storms were used for the calibration. They are listed in Table 2.2.3.
along with the total amount of rainfall and the runoff duration. The automatic
calibration option in HEC-HMS was not used because it provided a different set
of parameters for each storm. We elected to adjust the parameters based on
heuristic arguments and to put a special effort on matching the peak value and
the time-to-peak. This resulted in a unique set of parameters, reported in Tables
2.2.1. and 2.2.2.
19

Table 2.2.3. Rainfall Events Used for Calibration
# Date Total Rainfall
(inch)
1 October 08, 1996 2.00
2 October 16, 1996 3.46
3 September, 1999
(1)
7.03
4 November, 99 1.12
5 December, 99 1.65
6 March, 2002 1.15
7 May, 2002 1.50
8 June, 2002 1.62
(1)
Hurricane Floyd
The graphs in Section A.3.1. of Appendix A.3. show the comparison between
predicted (or simulated) runoff and those observed.
2.3. Ecological Indicators in the PCW
Indicators are used to describe and quantify the status of a system. Changes in
indicators can gauge the outcomes of actions or policies. Indicators are often
used to avoid having to process large amounts of detailed information. Meadows
(1998) presented a list of characteristics that sustainability indicators should
display. They should be, among other things: (1) clear in value; (2) clear in
content, with units that make sense; (3) measurable at reasonable cost; (4)
timely; (5) appropriate in scale; (6) hierarchical; (7) based on physical units rather
than money and prices; and (8) leading, so as to provide information in time to
act on them. These criteria will be discussed below. In the sequel, the terms
variables, environmental metrics and indicators will be used synonymously.
2.3.1. Water Volume
Based on the hydrology discussion above, this indicator seeks to measure the
current amount of water that is consistently available as compared to the amount
of water that would be available if the watershed were in a natural state. As the
area within a watershed is developed, less water is generally available because
impervious surfaces such as buildings, streets, parking lots and driveways cause
water to run immediately into streams rather than infiltrating into the groundwater
system. Groundwater moves slowly through cracks and spaces in the bedrock,
providing water for streams in dry weather as well as drinking water from wells.
New development reduces groundwater supply at the same time that it increases
water demand. This means that as development increases, planning for
adequate, long-term supplies of high-quality water becomes increasingly
important.
20

Data and Methods
Baseflow (often called the dry-weather flow) of a stream, a flow which is supplied
by groundwater. Because of this, baseflow is a good measure of water
availability. Figure 2.3.1. (Map BASEFLOW1) shows the bedrock geology areas
of the Pennypack watershed. The Pennypack baseflow was calculated using
data from the USGS Rhawn St. Stream Gauge in Philadelphia. Figure 2.3.2.
(Map BASEFLOW2) shows the area that drains to the Rhawn St Gauge. The
lack of additional operative stream gauges in the PCW currently makes it
impossible to calculate the baseflow at more than one point along the Creek.
This limitation is considerable, particularly when considering that the discharge
from the UM-H STP falsely inflates the baseflow, and the only functioning stream
gauge is downstream of this plant. The average daily discharge for the UM-H
STP was obtained from the Pennsylvania Department of Environmental
Protection (PA DEP). Figure 2.3.3. (Map BASEFLOW3) shows the area that
drains to the UM-H STP.
The natural baseflow rates used for this discussion came from the Water-Use
Analysis Program for the Neshaminy Creek Basin in Bucks and Montgomery
Counties, prepared by the USGS and the DRBC. This report contains the
baseflow discharge rates for the various geologic formations in PCW.
The 25-year baseflow at the Rhawn St gauge was arrived at by separating the
hydrographs of the stream gauge data using a computer program based on the
local-minimum method. (Sloto and Crouse 1996) Low-flow conditions was
selected for these calculations because it is during dry periods that water supply
presents a problem, and the aim of the indicator is to assess the water supply
that would be consistently available. The 25-year low flow was selected because
it is the most extreme condition that could be accurately modeled with the
available data. The 25-year low-flow condition is the low flow, or drought
condition that is predicted to occur one year out of every 25 years, or has a 4%
chance of occurring in any given year. The amount of the discharge from the UM-
H STP plant that was drawn from outside the PCW was then calculated, and that
amount was subtracted from the baseflow calculated by the separation of
hydrographs. This is the current 25-year baseflow. The natural 25-year baseflow
was calculated by finding the areas of each geologic formation in the PCW.
These areas were then multiplied by the flow rate for each geologic formation.
The sum of these values represents the natural baseflow at the Rhawn St.
Gauge. For more details on these calculations see Appendix A.3.
Three parameters affect the peak flows in watersheds. These are the CN, the
time of concentration, tc, and the percent impervious.
The potential infiltration, S, of a watershed is given by (in inch):
1000 10SCN
=−
(2.3.1.)
21

Where CN is the Curve Number, a number between 0 and 100, but commonly
ranging from 60 to 90. Higher values of CN imply lower potential infiltration, and
thus higher runoff and, most likely, higher peak flows.
The time of concentration represents the time it takes for the water parcel to
propagate from the most hydraulically remote location of the watershed to the
outlet. There is a relation between the time of concentration and the CN. This
relation is:
0.8
0.5
5(1
3 1900
cLS
t)
y
+
= (2.3.2.)
Where L is the length of main channel and y is the average slope of the
watershed (in percent).
Equations (2.3.1.) and (2.3.2.) indicate that the time of concentration is inversely
proportional to the CN, which is intuitive in many aspects; an increase in CN is
commonly a result in an increase in the impervious areas (concrete replacing
open soil) causing water to propagate faster in the watershed. However,
Equation (2.3.1.) should be used with great care, because the quantity length L
and average slope are not pure geomorphic quantities, but they depend rather on
water pathways. Different drainage patterns are expected to affect water flow
differently; dendritic drainage patterns (spatially random) tend to drain
watersheds slower than rectangular patterns. The evaluation of drainage
capability becomes more difficult in urban watersheds, where the geomorphic
significance of the length L and the slope vanishes in favor of hydraulic
significance, mostly due to constructed channels.
The percent impervious that we are referring to herein differs from that would be
incorporated in the CN evaluation. This one relates to impervious areas directly
connected to the channels, and thus their increase causes direct effects
(increase) on peak flows. The US Army Corps of Engineers’ model, HEC-HMS
accounts for these areas explicitly.
22

Results and Interpretation
The calculated current one-year-in-25 annual baseflow at the Rhawn St. gauge
was found to be 6.7292 million gallons per day, while the natural baseflow at that
gauge was found to be 12.8178 millions of gallons per day. Restated as a
percentage, current baseflow is only 52.5% of natural baseflow. This means that
during low-flow conditions, approximately half of the water that would naturally be
available for drinking water, waste assimilation, fish and wildlife habitat, etc. will
not be available. Although this area is water-rich, drought is a regularly recurring
problem, and a decreasingly reliable water supply will only serve to exacerbate
the effects of what might otherwise be minor problems. Additionally, during low-
flow conditions water demand by human beings decreases only slightly. This
means that the proportion of the water supply that is used by human beings
increases greatly, leaving less for the natural communities, particularly aquatic
communities.
Downstream of the sewage treatment plant, the effects of lower baseflow would
appear to be lessened. The stream does have over 90% of its natural baseflow
due to the discharge from the UM-H STP. However, from a regional perspective
the appearance of improvement disappears. The service area for the sewage
treatment plant extends beyond the watershed boundaries, resulting in an inter-
basin transfer of water. Water that is imported to the Pennypack has been
removed from other watersheds, further depleting their water supply. Also, the
water discharged into the stream is not of the same quality as the water already
in the stream.
23

The results for this indicator show that the Pennypack baseflow has decreased
from what it would be in an estimated natural state. However, the lack of stream
Figure 2.3.1. PCW Baseflow Geology
24

Figure 2.3.2. PCW Drainage to the Rhawn St. Gauge
25

Figure 2.3.3. PCW Upstream of the UM-H STP
26

gauges in the watershed imposes serious limitations on the analysis. The effect
of development on the baseflow of the Pennypack Creek is generally clear.
However, continuous and more detailed monitoring is necessary to track carefully
the effects of future development, as well as efforts to remediate the problem.
The peak flows in the PCW for the two-year storm under the current conditions
are shown at the PWD monitoring stations in Figure 2.3.4 and numerically in
Table 2.5.1.
Figure 2.3.4. Peak Flows under Current Conditions
These flows will be compared in Section 2.5.1. with the conditions that result
from the first two residential development scenarios.
27

2.3.2. Water Quality
This indicator seeks to measure the chemical contamination of the water.
Chemicals enter the aquatic system from both point and non-point sources. Point
sources are factories and other facilities that discharge directly into a stream.
These sources are highly regulated. Non-point sources are farms, lawns, parking
lots, etc. Chemicals from these sources are carried by rainwater into streams.
These non-point sources are very difficult to regulate, and so are relatively
uncontrolled.
Regardless of their source, chemicals that contaminate surface and groundwater
impair the water’s ability to act as habitat for wildlife, and make it dangerous for
humans or animals to drink it unless treated. Concentrations of nutrients and
pesticides in streams and shallow groundwater generally increase with
increasing amounts of agricultural and urban land in a watershed. This situation
develops because chemical use increases and less water is available from
undeveloped lands to dilute the chemicals. (USGS 1999)
Data and Methods
The data for this analysis came from the PWD’s Baseline Assessment of PCW
(2002-2003) (Lance et al. 2003). This study assessed the levels of a variety of
contaminants at 20 sampling points along the Pennypack Creek. Figure 2.3.4.
(Map CHEM1) shows these sampling points and the sub-basins to which the
data was attributed. Seven factors that comprise a majority of the impairment in
most streams were chosen to make up this indicator. These factors are
described below.
Alkalinity
Alkalinity is a measure of a stream’s ability to resist changes in pH. It is often
referred to as the buffering capacity of a stream, and is important because it
allows a stream to neutralize acidic pollution or contamination. The target range
for this factor is 100-200 mg CaCO3/L (Lehigh Earth Observatory). The results of
this analysis are on Figure 2.3.5. (Map CHEM2).
Dissolved Oxygen
Dissolved oxygen is absorbed from the atmosphere and from the result of
photosynthesis. If more oxygen is consumed than produced or imported, some
organisms will die. The target for this factor is 6 mg/L (US EPA 2005).
Fecal Coliform
These bacteria are present in the feces and intestinal tracts of humans and other
warm-blooded animals, and can enter water bodies from human and animal
28

waste. If a large number of fecal coliform bacteria (over 200 colonies/100 ml of
water sample) are found in water, it is possible that pathogenic organisms are
also present in the water. High concentrations of the bacteria in water may be
caused by septic tank failure, poor pasture and animal keeping practices, pet
waste, and urban runoff (BASIN 2002). The target for this factor is 200
colonies/100mL (US EPA 2005). The results of this analysis are on Figure 2.3.6.
(Map CHEM3).
Nitrate
Nitrate is the most completely oxidized state of nitrogen commonly found in water
and is the most readily available state utilized for plant growth. High nitrate levels
combined with phosphates cause excessive plant and algae growth, which is a
deteriorating process called eutrophication. Higher concentrations in water are
unsafe to drink due to the possible presence of altered forms of nitrite, which may
cause serious illness to both humans and wildlife. The target for this factor is 1
mg/L (Lehigh Earth Observatory 2005). The results of this analysis are on Figure
2.3.7. (Map CHEM4).
Ortho-phosphate
Ortho-phosphate is the form of phosphate used in fertilizer and applied to
agricultural fields and residential lawns. Like nitrates, phosphates negatively
impact water by causing accelerated rates of eutrophication. The target for this
factor is .03 mg/L (Lehigh Earth Observatory). The results of this analysis are on
Figure 2.3.8. (Map CHEM5).
Suspended Solids
Suspended solids include all particles in water with a diameter of less than 0.45
microns. Typically, suspended solids include items such as soil, algal cells, and
plant particles. High levels of suspended solids smother some aquatic
organisms. The target for this factor is 10 mg/L (Lehigh Earth Observatory). The
results of this analysis are on Figure 2.3.9. (Map CHEM6).
pH
pH is a measure of acidity. Variations in pH affect chemical and biological
processes in water. Low pH increases availability of metals and other toxics for
intake of aquatic life. It is critical to survival, growth, and reproduction of fish and
macro invertebrates to maintain a constant pH. Exposure to very low or high pH
may cause death or reproductive problems for fish and other aquatic life. The
target range for this factor is 6.5-8.5 (US EPA 2005).
The actual values were transformed into percentages of the target. For chemicals
where the actual values were below the target the percentage was calculated by
29

dividing the actual value by the target. For chemicals where exceeding the target
does not have a negative impact, any value exceeding the target was treated as
100%. For chemicals where exceeding the target is negative, the percentage of
the target was calculated by dividing the target by the actual value (an inverse
percentage). Targets for water quality for these contaminants were determined
using information from the State of Massachusetts Division of Water Pollution
Control and Lehigh University (Sherry 2005). For more details on these
calculations see Appendix A.3.
Results and Interpretation
The indicator value for the Pennypack Creek was 47.47%. This means that the
water is less than half of pristine condition. This value would have been much
lower, but both pH and dissolved oxygen were at optimal levels. The values for
Suspended Solids, Ortho-phosphate, and Fecal Coliform were all very far from
the target, in many cases more than ten times the target value. Nitrate values
were also very high. These results implicate the UM-H STP and non-point
sources. No standard for nitrates and phosphates has been set for aquatic life,
but EPA and the New Jersey Dept of Environmental Protection (NJ DEP) have
suggested a level of around 3 mg/L NO3-N . With respect to phosphates,
concentrations exceeding 0.3 mg/L are considered problematic.
The Temple research team evaluated the performance of the STP in removing
nutrients. (Meenar 2006) In September of 2004, automatic samplers were placed
up and downstream of the STP. The upstream sampler was about 2200 ft from
the treatment plant property and the downstream sampler was about 1300 ft from
the STP. The samplers collected water from Pennypack Creek at 10 am every
day for three weeks.
The sampling results highlight the importance of large point sources in
influencing water quality in urban streams. Upstream of the plant, the nitrate
concentrations were steady at 1-2 mg/L, which are typical values for urban
streams as noted by the USGS. Downstream of the plant, however,
concentrations were much higher, typically over 10 mg/L and up to 22 mg/L.
30

Figure 2.3.5. PWD Stations & Associated Sub-Basins: Chemical Data
31

Figure 2.3.6. %-Alkalinity: Sub-Basin Classification
32

Figure 2.3.7. %-Fecal Coliform: Sub-Basin Classification
33

Figure 2.3.8. %-Nitrate: Sub-Basin Classification
34

Figure 2.3.9. %-Phosphate: Sub-Basin Classification
35

Figure 2.3.10. %-Suspended Solids: Sub-Basin Classification
36

Concentrations dipped below 10 mg/L during storm events on 9/8/04 and
9/18/04, and were lower in the hand sample collected after Tropical Storm
Jeanne. With respect to phosphate, high concentrations of this nutrient were also
confirmed by the CSC research team. These concentrations were quite small
above the plant. Downstream, high concentrations, typically 1.5 mg/L, were
found during non-storm events. These sampling results were generally consistent
with previous sampling efforts conducted by the PWD in 2002 at a location near
the CSC’s sampler downstream of the plant.
Non-point sources, however, are responsible for a majority of the impairment in
the Pennypack in terms of geographic context. Non-point-source contamination
comes from many diffuse sources, including fertilizers and pesticides from
agricultural and residential lands, and nutrients from livestock, pet wastes, and
septic systems. Non-point-source contamination is the leading and most
widespread cause of water-quality degradation, and it can have harmful effects
on drinking-water supplies, recreation, fisheries, and wildlife.
2.3.3. Biological Integrity
Data and Methods
This indicator seeks to measure the health of the aquatic ecosystem in the PCW.
Ecosystems rely on the delicate interplay of many different factors including the
availability of food and habitat, the diversity of species, and the chemical
composition, temperature and clarity of the water. This reliance on many factors
makes biological integrity a strong composite indicator of the health of the
watershed. Also, biological criteria are essential to tracking impaired conditions
such as invasive species or loss of biodiversity, that are not directly caused by
chemical factors (Davis, 1995). Upstream land use changes and alterations of
the stream corridors affect the quality of the water delivered to the stream
channel as well as the structure and dynamics of the adjacent riparian
environments (Davis, 1995). The status of the biological community can provide
information about these factors that would not appear in a chemical analysis.
The data for this analysis came exclusively from the PWD Baseline Assessment
(Lance et al. 2003). This study assessed the biological integrity of both the
macroinvertebrate populations and the fish populations in the PCW. The
macroinvertebrates were collected from a period of 4/2/02 to 4/9/02 using Rapid
Bioassessment Protocols (RBPs; Barbour 1999) at 20 locations along
Pennypack Creek. Figure 2.3.10. (Map BIO1) shows these locations and the sub-
basins to which the data were attributed. EPA’s RBPs were designed to provide
cost-effective biological methods for states, and local agencies. The protocols
exist for periphyton, benthic macroinvertebrate, fish, and habitat assessment,
and all of these protocols have been tested in streams in various parts of the
country. The biological integrity at each site was determined by PWD following
RBPs and was compared to a reference site. Table 2.3.1. taken from the PWD
37

study gives a framework for understanding the biological integrity scores. (Lance
et.al. 2003)
Table 2.3.1. Interpreting Biological Integrity Scores
Biological
Integrity
Score
Condition Attributes
> 83% Non-
impaired Comparable to the best situation in an ecoregion.
Balanced trophic structure. Optimum community
structure for stream size and habitat
54-82% Slightly
Impaired Community structure less than expected. Species
composition and dominance lower than expected
due to the loss of some pollution intolerant forms.
Percent contribution of pollution tolerant forms
increases.
21-53% Moderately
Impaired Fewer species due to loss of most of the pollution
intolerant forms.
< 20% Severely
Impaired Few species present. If there are high densities of
organisms then the system is dominated by one or
two taxa.
The fish surveyed in this study were collected in July and August of 2002 at
eleven of their monitoring stations along the Pennypack Creek. Figure 2.3.11.
(Map BIO3) shows these locations and the sub-basins to which the data was
attributed. The fish were collected by electrofishing as described in Barbour
(1999). The biologic integrity of the fish community was then assessed using the
Index of Biological Integrity (IBI) developed by Karr (1981). Table 2.3.2. explains
the meaning of ranges of the IBI score.
A scale of 0 to 100% was established as the common framework for the all of the
indicators in this study. The macroinvertebrate data were already in this
framework. To transform the fish data to match the IBI score, the latter were
multiplied by 2. An average percentage for both fish and macroinvertebrates was
then determined. A weighted average for the two was also calculated in the
interest of having a single score for the indicator. For more details on these
calculations see Appendix A.3.
38

Table 2.3.2. Ranges of the IBI Score
IBI
Score Condition Attributes
45-50 Excellent Comparable to pristine conditions, exceptional
assemblage of species
37-44 Good Decreased species richness, particularly pollution
intolerant species
29-36 Fair Pollution intolerant and sensitive species are absent. The
trophic structure is skewed.
10-28 Poor Top carnivores are absent or rare. Omnivores and
pollution tolerant species dominate.
<10 Very Poor Few species and individuals are present. Pollution
tolerant species are dominant. Diseased fish are
prevalent.
Results and Interpretation
The average biological integrity score of the macroinvertebrate communities
studied in the PCW was 21.67%, which according to the system devised by PWD
is at the lower end of the moderately impaired category. The highest score for
any station was 66.67% - slightly impaired, and this was a station at the extreme
upstream portion of the watershed. Figure 2.3.12. (Map BIO2) presents these
results. In general the stations that monitored tributaries had higher scores than
those stations on the main stem. However, none of the stations monitored had a
non-impaired macroinvertebrate community, and a majority of the communities
were either severely or moderately impaired. These insects are an important link
in the aquatic food web, converting plant and microbial matter into animal tissue
that is then available to fish, and their loss makes it difficult for fish and other
predators to survive. The average biological integrity score for the fish
communities studied in the PCW was 61.20%.This is considerably higher than
the score yielded by the macroinvertebrates.This is at least partially due to the
fact that the fish had greater biodiversity in the tidal areas. The
macroinvertebrates fared poorly there due to the unstable water levels (Lance et.
al. 2003) Figure 2.3.13. (Map BIO4) presents these results.
Combining these scores yielded an overall biological integrity score for the
watershed of 45.39%. This low score strongly suggests that the effects of human
habitation are strongly and negatively impacting the biological communities in the
Pennypack Creek.
39

Figure 2.3.11. PWD Stations & Associated Sub-Basins: Macroinvertebrate Data
40

Figure 2.3.12. PWD Stations & Associated Sub-Basins: Fish Data
41

Figure 2.3.13. Biological Integrity for Macroinvertebrates: Sub-Basin Classification
42

Figure 2.3.14. Biological Integrity for Fish: Sub-Basin Classification
43

2.3.4. Impervious Surface
This indicator seeks to measure the percentage of the land within the watershed
that is impervious. In natural settings, very little annual rainfall is converted to
runoff, and about half of the water that seeps into the underlying soils becomes
groundwater. In urbanized areas, impervious surfaces prevent the rainfall from
seeping into the ground, and as a result the rainfall is converted into runoff. This
water runs directly to the streams and increases stream volume during rainfall
events. Depending on the degree of impervious cover, the annual volume of
stormwater runoff can increase by 2 to 16 times its pre-development rate, with
proportional reductions in groundwater recharge. (Schueler 1994) Impervious
surfaces raise the velocity as well as the volume of stormwater runoff. This
increases erosion and sedimentation, and washes oil and other chemicals from
roadways and parking lots into surface waters. Impervious surfaces also absorb
heat, and often increase stream temperatures during runoff events. These
physical changes are generally accompanied by decreasing water quality and
decreasing biodiversity (Capiella, 2002).
Data and Methods
The Impervious Surface Data originated from The National Land Cover
Database, 2001. The data in this database were generated from satellite
photography. The satellite data provided a percentage of impervious coverage
for each 25 m x 25 m pixel. The raster containing impervious surface data was
clipped to the PCW boundary and then converted to a shapefile. The shapefile
containing the 49 sub-basins was converted to a raster and then back to a
shapefile, to match the transformed raster’s boundary. Using Hawth’s Tool:
Polygon in Polygon Analysis, the average percentage of surface covered by
impervious was calculated for each sub-basin. Then, based on this average of
the impervious surface and the area of each sub-basin, the area covered with
impervious surface (in square miles) was calculated. This can be seen on Table
2.3.14. (Map IMPERVIOUS1). The mean of these percentages provided the
average impervious surface for the PCW. For more details on these calculations
see Appendix A.3.
Results and Interpretation
On average 29.67% of the PCW is covered by impervious surface. Many
different studies from various geographic areas, employing different methods and
concentrating on different variables, have all come to the conclusion that stream
degradation occurs at relatively low levels of imperviousness 10-20% (Schueler,
1994). This suggests that the PCW is experiencing significant impacts to its
water quality, biological integrity, and groundwater/baseflow from the high
percentage of impervious surface in the watershed. The results from the other
three indicators all support this conclusion. The current baseflow levels are at just
over half what they would be if the watershed were not developed. The biological
44

integrity of the aquatic communities is fair at best, and the levels of many of the
pollutants in the stream greatly exceed the acceptable levels, if not the EPA
guidelines. If the trend of development in the watershed continues, then all of
these indicators will continue to worsen unless efforts are put into place to
mitigate the effect of development.
Figure 2.3.15. %-Impervious Surface: Sub-Basin Classification
45

2.4. Two Land-Use Scenarios
In the analysis of the regional environmental future for the Pennypack Creek
Watershed, it is important to project possible future land use scenarios and
futures. In this section, we describe three alternative future land use scenarios.
These scenarios are primarily focused on macro trends in land use change, and
do not reflect site-specific innovations which might occur, such as floodplain
acquisitions or increased use of site-level stormwater management best
practices.
The analysis started with an examination of the demand for land from projected
population growth. The most recent official population forecasts were acquired
from the DVRPC, the designated regional and Metropolitan Planning
Organization (MPO) covering the PCW. We compared the official DVRPC
population forecasts to those utilized by the Montgomery County Planning
Commission and found no significant differences. The official population
forecasts are used by DVRPC for transportation planning and modeling, and
serve as an objective source of population and employment forecasts. The
forecasts have been updated in 2005 to account for changes in municipal
population from 2000-2005. In this analysis, only the projected population growth
rates in the 11 municipalities outside of the city of Philadelphia are examined for
two reasons. First, nearly all the land within the watershed in Philadelphia is
already considered developed. Second, the neighborhoods within Philadelphia in
the PCW are not forecasted to experience any significant population changes,
absent large-scale redevelopment efforts.
The official population forecasts for each of the 11 non-Philadelphia
municipalities which have some or all of their land area in the PCW are then
scaled down to represent the housing and population growth needs of the areas
of the municipalities which lie within the boundaries of the watershed. Using the
land use and demographic databases described in Appendix A.2., it can be
determined how much of a municipality’s population and land area lie within the
PCW using the weighted-average technique. These percentages were used to
apportion future population growth targets to the appropriate watershed. For
example, if a weighted average of 30 percent of a municipality’s housing units
and land area were within the PCW, then 30 percent of the forecasted population
growth rates were apportioned to future growth within the watershed. This
seemed the most reasonable approach. However, it is certainly possible that as
a result of this planning effort, municipalities may choose to allocate a larger
portion of their future housing and employment growth to other watersheds within
their boundaries.
One of the difficulties in watershed planning and land use forecasting within
smaller watersheds in Pennsylvania is that fundamental land use decisions are
made by municipalities and the boundaries of municipalities do not conform to
watershed boundaries. Municipalities are generally required to make adequate
46

provision in their zoning for their projected population growth, but determining
into which watershed the growth will be directed is difficult.
Table 2.4.1. presents the future population needs for PCW municipalities,
representing only those future populations assigned to growth within the
watershed. The first two columns represent the adjusted official population
forecasts for the years 2020 and 2030 for each municipality. Columns 3 and 4
convert population forecasts into an indication of aggregate housing unit needs.
Based on standard practice, these are calculated as future population divided by
average number of persons per occupied housing unit for each municipality in
the most recent Census (2000). Thus, the assumption is made that the average
number of persons per occupied housing unit will remain the same over the 30
year planning horizon. This assumption is the best assumption of household
sizes, even though average household size in the US has been consistently
declining. It would be possible to re-estimate the following scenarios assuming
smaller household sizes, which would only have marginal impacts on residential
land needed. As well, household size will also be a variable somewhat within the
control of municipalities in the zoning policies which control the types and sizes
of housing units constructed. Within the PCW, the average household size is
2.66 persons per household, ranging from a low of 2.2 persons per household in
Jenkintown Borough to a high of 3.5 persons per household in Bryn Athyn
Borough. Housing unit needs were also adjusted upwards by 2 percent to reflect
an estimated average vacancy rate of 2 percent. In the year 2000 within the
watershed, the vacancy rate was 2.4 percent.
Columns 5 and 6 of Table 2.4.1. convert the gross housing unit needs of the
years 2020 and 2030 into the number of new units which need to be constructed
during the planning horizon. These figures are arrived at by subtracting from
housing needs the number of existing units in the year 2020. Overall, the results
of the demographic analysis do not show much growth in the non-Philadelphia
municipalities of the PCW. Population is only expected to grow from a 2000 level
of approximately 100,000 to a 2030 population of 106,000. Only 1855 new
housing units in a 30 year time period would be needed to accommodate this
population growth. As demonstrated below in the land use scenarios, however, if
these housing units are produced at lower densities, the amount of undeveloped
land remaining in the PCW would be significantly reduced.
47

Table 2.4.1. Forecasted Population Growth and Housing Unit Construction Needed
2020
Housing
Unit
Need
2030
Housing
Unit
Need
2020 New
Unit
Construction
2030 New
Unit
Construction
Population
2020 Population
2030
Bucks County
Upper Southampton
Twsp. 4659 4768 1818 1860 157 199
Warminster Twsp. 16056 16803 5923 6198 696 971
Montgomery County
Abingdon Twsp. 28063 28134 11067 11094 -205 -177
Bryn Athyn Borough 1410 1420 401 404 20 23
Hatboro Borough 7470 7500 3134 3147 13 26
Horsham Twsp. 9022 9707 3449 3711 399 661
Jenkintown Borough 523 513 242 238 -10 -14
Lower Moreland Twsp. 9937 10324 3695 3839 73 217
Rockledge Borough 1383 1366 580 573 -28 -35
Upper Dublin Twsp. 892 904 322 327 16 20
Upper Moreland Twsp. 24655 24625 10183 10170 -24 -36
TOTAL PENNYPACK
WATERSHED 104069 106064 40814 41562 1108 1855
2.4.1. Scenario 1: Trend Development
Table 2.4.2. represents the land use analysis associated with Scenario 1: Trend
Development. In this scenario, we assume that each new housing unit will use
the same amount of land as the existing year 2000 average housing unit land
use for each municipality. That is, in this scenario we assume that current
densities (reflecting current zoning and current development practices) predict
future densities. This assumption is still somewhat conservative in terms of land
use, because newer housing units generally are produced at densities lower than
existing average densities.
48

Table 2.4.2. Land Development Rates: Trend Development Scenario
2020
Residential
Need
2030
Residential
Need
2020 Non-
Residential
Need
2030 Non_
Residential
Need
2020
Total
Need
2030
Total
Need
Land Suitable
for
Development
Acres Acres Acres Acres Acres Acres Acres
Bucks County
Upper Southampton
Twsp. 67 85 18 23 84 108 164
Warminster Twsp. 209 292 90 125 299 416 404
Montgomery
County
Abingdon Twsp. -59 -51 -59 -51 432
Bryn Athyn Borough 13 15 3 3 16 18 58
Hatboro Borough 3 5 4 5 6 10 55
Horsham Twsp. 172 284 48 80 220 364 736
Jenkintown Borough -1 -2 -1 -2 0
Lower Moreland
Twsp. 46 135 11 28 56 163 236
Rockledge Borough -4 -5 -4 -5 0
Upper Dublin Twsp. 8 11 2 2 10 13 15
Upper Moreland
Twsp. -6 -10 6 5 0 -5 439
TOTAL
PENNYPACK
WATERSHED 446 758 181 270 627 1029 2539
Using the high-resolution digital land data, gross residential housing unit
densities are determined for each municipality as the number of housing units
divided by land classified as in residential use. (Residential classifications by
DVRPC include streets internal to a development, utility rights-of-way and
stormwater drainage facilities, but do not include commercial facilities or non-
local roads.) Thus, the estimate of gross residential housing unit densities is a
good estimate of the amount of land used per housing unit. Using the figures
from 2000, we project aggregate residential land use in Table 2.4.2., shown in
columns 1 and 2. Development densities across the region range from a low of
1.6 housing units per acre in Bryn Athyn to a high of 7.8 housing units per acre in
Jenkintown.
Estimates of the amount of land needed in non-residential development
(including commercial, industrial, office, utility, and transportation needs) can be
estimated with detailed employment growth forecasts to convert employment
needs into space requirements. Unfortunately, at the small scale of a municipality
or portion of a municipality, employment forecasts are difficult to obtain and of
questionable quality. The alternative approach, common in many planning
applications, is to assume a fixed amount of non-residential land per capita, and
therefore, to assume that non-residential land use needs are driven by local
population growth. In this case, we project that per-capita demand for non-
49

residential land will be approximately 2000 square feet. That is, each person
added will produce a demand for an additional 2000 square feet of non-
residential urban development. These assumptions were parameterized based
on an analysis of aggregate land development uses within the entire Philadelphia
metropolitan area. Based on DVRPC data, we estimate that non-residential
urbanized land uses amount to approximately 2212 square feet per resident in
the region not including Philadelphia and 1833 square feet per resident when
Philadelphia is included. Thus, the assumption of an additional 2000 square feet
of non-residential urban land use per new additional resident seems a
reasonable assumption based on existing development trends. However, in
municipalities which are projected to experience population decline, we assume
that the same amount of urbanized non-residential land is maintained.
The analysis in Table 2.4.2. indicates that, at current trend densities, the PCW
will see an aggregate additional 1029 acres converted to urban development in
the 30 year period between 2000 and 2030. However, the estimate of 1029
acres is aggregated across some municipalities where population growth is
forecasted to be negative. If, as in this scenario, it is assumed that each
municipality must accommodate its own projected growth needs, and that
declining population municipalities retain the same amount of developed land,
then 1092 total additional acres will be needed to service new urban
development by 2030.
For this scenario, in order to apportion future land use growth in the various
scenarios, the suitability and capability of currently undeveloped land to
accommodate land development and growth were analyzed. The first step was to
create a layer of land use which is “potentially developable.” Potentially
developable land was defined as all land currently in use in the agricultural,
wooded, recreation or vacant categories. We then overlaid layers of known
permanently-preserved open space land (state, county and municipal parks,
PERT land, etc.) to remove lands which would not be developed due to
preservation easements or public ownership. All the remaining land is considered
“potentially developable.”
Within the land classified as potentially developable, two criteria were applied to
identify those lands which are most suitable for development. The first criterion
was the absence of environmental constraints and the second criterion was the
availability of sewer infrastructure. For the environmental constraint criteria, we
used data on slopes, streams, floodplains and wetlands. Land was identified as
being environmentally constrained if it were over 15 percent sloped, within a
floodplain, within 100 feet of a stream or within 25 feet of the edge of a wetland.
A data set was used on “sewer service areas” (from DVRPC) to identify those
lands which are within a sewer service area. Thus, those lands which are most
suitable for development are those lands which are within a sewer service area
and which lack environmental constraints. Within the entire PCW (including
Philadelphia), there are nearly 3900 acres considered suitable for development
50

under this methodology, of which over 2500 acres are outside of the city of
Philadelphia.
Of these 3900 acres potentially more suitable for development, over 40 percent
are currently wooded and nearly 13 percent are in agriculture. Thus, one of the
planning challenges facing the watershed is balancing the growth needs with
preserving forested and agricultural landscapes. In this analysis, an area being
classified as potentially suitable for development does not mean that
development of these landscapes is the most appropriate policy choice.
In the future land use development envisioned under this scenario, each
municipality develops land to meet its own projected residential and non-
residential needs. In this scenario, growth needs were compared with lands
designated in the analysis above as suitable for development. As can be seen in
a comparison of the growth needs in columns 5 and 6 of Table 2.4.2. with column
7 (lands suitable for development), nearly every municipality has adequate
suitable land to meet its forecasted growth needs at present densities. The only
exception is Warminster Township, which in this scenario would use 416 acres in
the 30 year planning period, while having only 404 acres available for
development. In the scenarios constructed, it is assumed that all development
for municipalities occurs on lands classified as suitable for development. For
Warminster, the additional 12 acres of development needs are assumed to come
from land designated as un-sewered but not environmentally constrained (of
which 20 acres are available.)
Each municipality accommodating its own projected land development needs in
many ways represents the trend in Pennsylvania land use planning by
municipalities, as each municipality is under an affirmative obligation to
“accommodate reasonable overall community growth, including population and
employment growth” (PDCED; 2005) absent a shared land-use agreement within
a multi-municipal plan.
Thus in the Trend Development scenario, the following rules are used to
accommodate each municipality’s future population growth in the 20 and 30 year
planning horizons. First, if the municipality shows a negative projected population
growth rate, it is assumed that land use patterns in 2020 and 2030 will remain the
same as in 2000. That is, even though population will decline, it is assumed that
the number of housing units and the amount of land in non-residential urban use
will not change. Given the durable nature of infrastructure, housing and urban
development, this is a reasonable assumption. Second, if the municipal growth
can be accommodated on land which has public sewers available and is not
environmentally constrained, all of its development needs were simulated on that
land. As much development as possible was first allocated on land currently
classified as “vacant.” Third, for the one municipality (Warminster) which required
additional land for development, its remaining development needs were allocated
to land which was not environmentally constrained, without public sewers.
51

Figures 2.4.1. shows the projected land use in 2030 under Scenario 1.
Figure 2.4.1. Trend Development Land Use, 2030
Much of the undeveloped land near the various streams of the watershed is
protected in this scenario from development because of the environmental
constraints. Most of the land conversion under this scenario occurs in the
currently less developed townships in the northern portion of the watershed.
2.4.3. Scenario 2: Smart Growth
Municipal Smart Growth
In this scenario, each municipality accommodates its forecasted population
growth needs, but accommodates the residential portion of that growth at
significantly higher gross housing densities and the non-residential portion at
slightly increased intensities. In order to illustrate this scenario, densities of 6
units per gross residential acre were chosen to simulate all new residential
development in the less dense municipalities. Abington Township and Upper
52

Moreland Township were excluded because the analysis in Table 2.4.1. showed
a negative household land-use need.
Depending on the planning decisions of these municipalities accommodating
growth at higher densities in terms of housing mix and design standards (e.g.
cluster subdivisions), some of these housing units could be townhouses and
others would be cluster houses on smaller lots (e.g., 8000 ft2). Further, in this
smart growth scenario, only 1500 square feet of residential land per new resident
was assumed, in that commercial and other uses were developed at higher
intensities. The results are shown in Table 2.4.3.
Table 2.4.3. Land Development, Municipal Smart Growth Scenario
2020
Residential
Need
2030
Residential
Need
2020 Non-
Residential
Need
2030 Non-
Residential
Need
2020
Total
Need
2030
Total
Need
2020
Land
Saved
2030
Land
Saved
Acres Acres Acres Acres Acres Acres Acres Acres
Bucks County
Upper
Southampton
Twsp. 26 33 13 17 39 50 45 58
Warminster
Twsp. 116 68 68 93 184 255 115 161
Montgomery
County
Horsham Twsp. 67 36 36 60 103 170 117 194
Lower
Moreland Twsp. 12 8 8 21 20 57 36 106
Upper Dublin
Twsp. 3 1 1 2 4 5 6 8
TOTALS 224 126 126 193 350 537 320 527
The last column of Table 2.4.3. indicates that, in comparison with the trend
development scenario illustrated in Table 2.4.2., 527 additional acres of forested
and agricultural landscapes would be preserved with accommodation by each
municipality of its future residential needs at reasonably higher densities,
consistent with smart growth. Comparing these figures with the amount of land
which is suitable for development in Table 2.4.2., each municipality has more
than enough land available for this smarter development. Figure 2.4.2. shows the
projected land use futures for 2030 under Scenario 2.
Regional Smart Growth
In this third scenario, the region still accommodates its forecasted population, but
accommodates the development by sharing uses among municipalities. Future
growth needs are targeted to existing vacant (but developed) land as infill
53

development or redevelopment. It is assumed as well that the existing housing
stock of areas forecasted to lose population, are occupied, and therefore, absorb
Figure 2.4.2. Municipal Smart Growth Land Use, 2030
a significant proportion of overall population growth in the region. For example,
the estimates in Table 2.4.1. for Abington Township show an actual population
loss equivalent to 205 households. In this scenario, 205 forecasted new
households from other municipalities would instead move into the existing
housing stock in Abington, reducing the need for new construction. In this
scenario, the number of new households which could be accommodated in
existing housing units was calculated first. For the region as a whole, only 1108
new net housing units need to be constructed to 2020 and 1855 net new housing
units to 2030.
Table 2.4.4. presents an analysis of land available within the region which is
considered “vacant” (not wooded, not agricultural) and also “suitable for
development” (no environmental constraints, public sewer available.)
It should be apparent in examining Table 2.4.4., that all of the PCW’s net new
housing unit development, if accommodated at urban densities, could be easily
accommodated in the currently vacant land in the city of Philadelphia within the
watershed. If the watershed were to undertake something like a regional “transfer
54

of development rights” program, all of the projected growth needs for 30 years
could be accommodated without any additional conversion of currently
undeveloped land to urban development.
Table 2.4.4. Regional Smart Growth Scenario
Vacant Land,
Suitable for
Development (acres)
Bucks County
Upper Southampton Twsp. 44
Warminster Twsp. 77
Montgomery County
Abingdon Twsp. 50
Bryn Athyn Borough 0
Hatboro Borough 14
Horsham Twsp. 62
Jenkintown Borough 0
Lower Moreland Twsp. 23
Rockledge Borough 0
Uper Dublin Twsp. 1
Upper Moreland Twsp. 70
Philadelphia County
Philadelphia 564
TOTAL PENNYPACK
WATERSHED 905
2.5. Differential Impacts of Residential Development Scenarios
Two separate analyses were undertaken to estimate some impacts of traditional
vs. smart residential development. Section 2.5.1. estimates hydrological changes
that result from the trend and municipal smart growth scenarios in Section 2.4
above. Section 2.5.2. assesses impacts based on two slightly different
development scenarios: Smart Growth and Sprawl. In future research, it is hoped
that the analyses in Sections 2.5.1. and 2.5.2. can be unified.
2.5.1. Hydrology
Given the land-use changes forecasted in the development scenarios described
in Section 2.4., Trend Development and Smart Growth, the peak flows were
estimated with the USGS HEC-RAS model. They are shown in Table 2.5.1.
Figures 2.5.1. and 2.5.2.
55

Table 2.5.1. Peak Flows (cfs) for Varying Conditions
Trend Smart Growth
PWD Smart
Growth Flows with Flows with
Station Current Trend tc reduced
10% tc reduced
10% Flows Flows Flows
1 160 235 178 254 193
2 271 403 317 436 343
3 374 518 376 561 408
4 1303 1606 1360 1740 1476
5 403 574 438 622 476
6 1795 2277 1895 2468 2051
7 1900 2394 2010 2594 2180
8 2035 2543 2159 2756 2342
9 253 259 259 280 280
10 2640 3193 2800 3461 3036
11 248 250 250 271 271
12 2691 3250 2856 3524 3098
13 91 115 115 124 124
14 2809 3378 2982 3661 3235
15 3050 3585 3191 3886 3462
16 3141 3671 3277 3980 3556
17 3331 3816 3424 4138 3713
18 3470 3905 3511 4232 3809
19 202 202 202 214 214
20 3690 4087 3697 4436 4016
The Trend Development and Smart Growth flows are shown in Figures 2.5.1.
and 2.5.2.
56

Figure 2.5.1. Flows from Trend Development
2.5.2. Energy Use, Air and Greenhouse Gas Emissions, Water Quality and
Biological Integrity
In this section, the analysis of residential development involved four steps: (1)
desirable attributes of development behavior were transformed into suitability
criteria using GIS software to find areas into which housing can be sustainably
placed; (2) development scenarios were created; (3) the energy and
environmental impacts of the placements were estimated; and (4) the effects on
the value of ecosystem functions were estimated. Housing placed randomly in
57

the most suitable sites represents the Smart Growth scenario. Housing placed
randomly among non-restricted locations in the PCW represents the Sprawl
scenario.
Suitability Analysis for Smart Growth
The criteria used to determine the suitable areas for the Smart Growth scenario
were that development should: (A) be on a suitable site (vacant and low-density
residential land uses, though possible in other areas); (B) not be within the
restricted areas (floodplain boundary; wetland, and other water resources, parks,
open spaces, woodlands, and other protected spaces); (C) minimize the
necessity of new infrastructure and services (minimize distance to existing road
infrastructure, schools, and shopping centers); (D) minimize the impact on air
quality in the airshed (minimize contribution to traffic congestion and minimize
distance to public transit stations); & (E) minimize the impact on water quality in
the PCW (protect the riparian buffer areas, floodplains and open spaces).
More specifically, the criteria were set within the following two broad categories:
(i) Land Features: on lands free from floodplains, wetlands, ponds, and other
water resources; on impervious surface (the goal is to protect pervious surfaces);
on relatively flat land; on suitable land cover; on impermeable soil type
(Impermeable soil should be better for development, as it has minimum impact
on water transmission to soil. Soil types A (high rank) to D (low rank) were
considered.); and (ii) Infrastructure and Facilities: near public transit stops (only
rail station data was used), roads and bike trails, schools, hospitals, and parks
and open spaces. It was assumed for this analysis that the current residential
housing stock stays intact. Though zoning is recognized as an important
determinant of housing placement, it was assumed that it can be changed to
accommodate new development. In addition, the zoning ordinances of the
municipalities in the PCW have different coding systems. This makes it difficult to
create a comprehensive zoning map for this multi-municipal watershed.
The GIS software used for this study was ArcGIS 9.1 (ESRI 2005). The input
data and their sources were:
 Land Cover – US Geological Survey (USGS)
 Landuse – Delaware Valley Regional Planning Commission (DVRPC)
 Floodplains – Center for Sustainable Communities (CSC), Temple
University Ambler
 Stream Bank and Buffer – CSC
 Slope – CSC
 Wetlands, Ponds, and Other Water Resources – CSC
 Roads – ESRI
 Soil – PA Spatial Data Access (PASDA)
 Impervious Surface – PASDA
 Southeastern Pennsylvania Transit Authority (SEPTA) Rail Stations –
DVRPC
58

 Bike Trails – DVRPC
 Municipal Boundaries – DVRPC
 Job Centers – DVRPC
 Schools – ESRI
 Hospitals – ESRI
All of these datasets were available in vector format (points, lines and polygons),
except impervious surface data. Vector data were converted to raster (pixels)
and then those layers were reclassified according to the suitability criteria.
After reclassification, the layer Scale Values were determined. The Scale Values
ranged from 10 to 1 – most suitable to least suitable.
Layer: Floodplains, Wetlands, Ponds, Floodway, Wetland, Pond: Restricted
100-Year Floodplain: 1
500-Year Floodplain: 2
Other Areas: 10
Layer: Impervious Surfaces
Lowest 2 categories (in other words, highest impervious) are restricted
Layer: Slope
Lowest 4 categories (in other words, highest slope) are restricted
Layer: Land Cover
Value 0 is restricted land cover
Layer: Hydrologic Soil Group
Group B: 5
Group C: 9
Group D: 10
Layer: Streets
Value 0 is a restricted area
Finally, Layer Influence (user-chosen %s) was determined before the final raster
calculation. These are shown on Table 2.5.2.. The map of suitable sites for the
Smart Growth scenario is shown in Figure 2.5.2.
Suitability Analysis for Sprawl
In this scenario, only the restricted areas were excluded from the total watershed
in order to keep the other areas open for new development or re-development as
much as possible. Restricted areas are those that are restricted by state or local
municipal law, such as state or county parks and open spaces, floodways, and
other protected lands. The output of this analysis is shown in Figure 2.5.3.
59

Table 2.5.2. Relative Importance of Suitability Criteria
Layer Influence
Floodplains, Wetlands, Ponds .30
Impervious Surfaces .15
Slope .05
Land Cover .10
Soil .10
Transit Stops (rail station) .10
Roads .10
Schools .02
Hospitals .02
Parks and Open Space .02
Trails .04
Figure 2.5.2. Sites for Smart Growth: Darker Is More Suitable
60

Figure 2.5.3. Sites for Sprawl: All but Dark Sites Acceptable
Residential Development
The 1855 units in Table 2.4.1. projected to be needed by 2030 were assumed to
be placed in 2006, and to be distributed over the suburban municipalities and the
part of Philadelphia in the watershed. The building units were placed at various
sites for the two scenarios. Hawth’s Tool, an extension of ArcGIS, was used for
generating random points representing the building units. For the Smart Growth
Scenario, 1855 points were randomly generated in the highest suitable areas
(with ranks 10 and 9). The following steps accomplished this task: create a
polygon file from the suitability output raster file; dissolve according to suitability
categories; choose only areas with 10 and 9 and create a new layer; generate
random points for the whole area (using Hawth’s Tool); select only those points
that are inside areas with 10 and 9 ranking, and randomly choose 1855 points
from the selection. Once the building units were placed, distances were
calculated from these points to the nearest commuter rail stations (points) using
Hawth’s Tool. The method was same for the sprawl scenario. The only difference
61

from the Smart Growth selection process is that random points were generated in
areas suitable for the sprawl scenario, i.e., all unrestricted areas.
Energy and Environmental Impacts
As this work deals with residential housing placement, it was assumed that the
same type of housing would be placed in any location chosen. As the goal was to
compare placement in the Sprawl scenario to that in the Smart Growth scenario,
the assumption of the same type of housing obviates the need to specify the
structural, energy-use and emissions characteristics of the houses themselves. It
was also assumed that residents in any location will drive the same class of
automobile. The energy-use and emissions characteristics of the representative
vehicle are given below.
Auto Energy Use, Greenhouse Gases and Air Quality
To calculate energy use and greenhouse gas and criteria air pollutant emissions
of miles driven by residents in the two scenarios, a beta version of the GREET
1.7 program created by the Argonne National Lab’s Center for Transportation
Research (Wang; 2005) was used. Based on fuel type, technology type, market
share and many other parameters chosen by the user, the program can generate
“well-to-pump,” “well-to-tank” and “well-to-wheels” estimates for energy use and
greenhouse gas and criteria air emissions.
The estimates used here are for a car with a conventional spark-ignition (SI)
engine using conventional gasoline (CG) or reformulated gasoline (RFG). Table
2.5.3. shows some parameters used.
Table 2.5.3. Fuel Economy & Emissions Rates of Baseline Vehicles
Items SI Vehicle: CG & RFG
MPG 24.8
Exhaust VOC (all g/mi) .122
Evaporative VOC .058
CO 3.745
NO
x
.141
Exhaust PM10 .0081
Brake & Tire Wear PM10 .0205
CH4 .0146
N2O .012
The results of the GREET run in terms of energy use and emissions are given in
Table 2.5.4.
62

Table 2.5.4.Energy Use & Emissions from a Typical Automobile
Gasoline Vehicle: CG and RFG
Btu/mile or grams/mile
Vehicle
Operation Total Item Feedstock Fuel
Total Energy 188 981 4,912 6,081.497
Fossil Fuels 181 968 4,835 5,983.208
Petroleum 60 470 4,835 5,364.766
CO2 19 73 368 459.778
CH4 0.448 0.085 0.021 0.553
N2O 0.000 0.005 0.012 0.018
GHGs 29 77 372 477.733
VOC: Total 0.017 0.116 0.218 0.351
CO: Total 0.041 0.040 4.917 4.998
NOx: Total 0.117 0.134 0.268 0.518
PM10: Total 0.010 0.038 0.029 0.077
SOx: Total 0.047 0.109 0.006 0.162
VOC: Urban 0.003 0.074 0.135 0.212
CO: Urban 0.001 0.018 3.058 3.078
NOx: Urban 0.006 0.057 0.167 0.229
PM10: Urban 0.000 0.004 0.018 0.022
SOx: Urban 0.004 0.048 0.004 0.057
In both scenarios, automobile energy use and greenhouse gas and criteria air
pollution emissions are computed by multiplying the GREET numbers in Table
2.5.4. by the number of miles residents would drive when they are placed in
chosen locations. Though this could have been done for any or all of the other
types of infrastructure (e.g., roads, schools), distances from the chosen housing
sites to the nearest commuter rail stations were calculated. Assuming two
trips/day, five days/week and forty-nine weeks/year yielded 1,894,340 miles for
Sprawl and 933,940 miles for Smart Growth. The results are shown in Table
2.5.5. Columns 3 and 4 show the products of the items in column 2 and the miles
computed for the two scenarios. The Smart growth miles were 50.7% of the
Sprawl miles.
Suitability Analysis for Sprawl
In this scenario, only the restricted areas were excluded from the total watershed
in order to keep the other areas open for new development or re-development as
much as possible. Restricted areas are those that are restricted by state or local
municipal law, such as state or county parks and open spaces, floodways, and
other protected lands. The output of this analysis is shown in Figure 2.5.3.
63

Table 2.5.5. Energy Use & Emissions in Sprawl & Smart Growth
Gasoline Vehicle: CG and RFG
Miles in Smart
Growth Btu/mile or Miles in Sprawl
Item grams/mile 1894340 933940
Quantities Per Mile Energy Use/Emissions Difference
Total
Energy 6,081.50 11,520,423,027 5,679,753,308 5,840,669,719
Fossil Fuels 5,983.21 11,334,230,243 5,587,957,280 5,746,272,963
Petroleum 5,364.77 10,162,690,824 5,010,369,558 5,152,321,266
CO2 459.778 870,975,857 429,405,065 441,570,791
CH4 0.553 1,047,570 516,469 531,101
N2O 0.018 34,098 16,811 17,287
Total GHGs 477.733 904,988,731 446,173,958 458,814,773
VOC: Total 0.351 664,913 327,813 337,100
CO: Total 4.998 9,467,911 4,667,832 4,800,079
NOx: Total 0.518 981,268 483,781 497,487
PM10: Total 0.077 145,864 71,913 73,951
SOx: Total 0.162 306,883 151,298 155,585
VOC: Urban 0.212 401,600 197,995 203,605
CO: Urban 3.078 5,830,779 2,874,667 2,956,111
NOx: Urban 0.229 433,804 213,872 219,932
PM10:
Urban 0.022 41,675 20,547 21,129
SOx: Urban 0.057 107,977 53,235 54,743
Water Quality and Biological Integrity
As noted in Sections 2.3.2. and 2.3.3. the PWD (2003) provided a chemical
analysis and a biological integrity assessment of the water for each of their 20
monitoring stations located along the Pennypack Creek. For each station, the
water quality index was computed using the components and their target levels.
Each measurement was divided by the target level for that component. Since
some components are negative to water quality (by their nature, or if they exceed
the target), a minus sign was added. The resulting “signed” ratios were added
over the components. Due to the relative magnitudes of the ratios, the sums are
invariably negative. Hence, water quality is highest at those stations with smaller
negative values. Ratios of the observed components of water quality to target
levels are shown in Table A.3.6. The Water Quality Index in Table 2.5.5.. is
simply the sum of the row sum for each station.
The biological integrity values from Tables A.3.7. and A.3.8. are combined and
presented in Table2.5.7. It should be pointed out that the presence of .0000 in
the table implies different things for macroinvertibrates and fish. For
macroinvertibrates, it means that no significant numbers were found at a site. For
fish, it is due to the fact that only 10 stations were sampled. The Total Biological
64

Integrity was gotten by choosing the only non-zero number in the row, or by
averaging the numbers in the row.
Table 2.5.6. Water Quality Index
Water
Quality
Station Index
1 -38.03
2 -36.10
3 -50.82
4 -50.82
5 -40.47
6 -40.47
7 -43.29
8 -36.36
9 -36.36
10 -47.39
11 -23.04
12 -32.31
13 -32.30
14 -63.55
15 -63.55
16 -31.34
17 -79.12
18 -174.51
19 -174.51
20 -22.55
65

Table 2.5.7. Biological Integrity
Macro- Total
Invertebrates Fish Biological
Integrity
(%)
Biological
Integrity (%) Biological
Integrity (%) Station
1 0.0000 0.6800 0.6800
2 0.4000 0.0000 0.4
3 0.0667 0.0000 0.067
4 0.0000 0.7600 0.76
5 0.0000 0.7600 0.76
6 0.0000 0.0000 0
7 0.0000 0.5600 0.56
8 0.4000 0.0000 0.4
9 0.1333 0.6400 0.38665
10 0.2667 0.0000 0.2667
11 0.4667 0.0000
0.4667
12 0.4000 0.0000 0.4
13 0.0000 0.0000 0
14 0.1333 0.5600 0.34665
15 0.2000 0.5200 0.36
16 0.4000 0.0000 0.4
17 0.0000 0.4800 0.48
18 0.4000 0.0000 0.4
19 0.4000 0.4800 0.44
20 0.6667 0.4800 0.57335
Analytical relationships between various land uses and water quality and
biological integrity were not established. To gauge the water-related impacts of
the Smart Growth and Sprawl scenarios, the Water Quality Index and the
Biological Integrity were “weighted” by the placement of the housing units. The
water quality and biological integrity assigned to the stations were then attributed
to the sub-basins. The number of housing units in each sub-basin served as a
weight by which to multiply the sub-basin water quality and biological integrity.
The result was a Weighted Water Quality and Weighted Biological Integrity. The
results of the calculations are summarized in Table 2.5.9.
Reduction in the Value of Ecosystem Services
Absent an explicit inventory created by the PCW research team, the general list
of these services given by deGroot, Wilson and Boumans (2002) was used.
These authors collected a range of values for these services as estimated in
66

other studies. Table 2.5.8. shows the ecosystem services and the midpoint
$/acre values of the ranges provided by the authors.
Table 2.5.8. Value of Ecosystem Functions/Services
Service Midpoint
Category Service ($/acre)
Regulation Gas regulation 55
Climate regulation 63
Disturbance regulation 1465
Water regulation 1102
Water supply 1538
Soil retention 55
Soil formation 2
Nutrient cycling 4287
Waste treatment 1367
Pollination 8
Biological control 16
Habitat Refugium function 309
Nursery function 68
Production Food 560
Raw materials 206
Genetic resources 24
Medicinal resources n.a.
Ornamental resources 30
Information Aesthetic 358
Recreational & tourist 1214
Cultural & artistic n.a.
Spiritual & historic 5
Scientific & Educational n.a.
Total Ecosystem Service Value $12732
Source: Adapted from deGroot, Wilson and Boumans (2002)
Without a model to link water-related attributes to ecosystem services, the latter
were treated in the aggregate. The percentage relative reduction in total
ecosystem service value (TESV) will be taken as an average of the percentages
by which the Sprawl scenario impacts exceed those of the Smart Growth. This
average, giving Sprawl credit for having a better WWQ, is (.507 + .533 -.212)/3 =
.277. If the sum of the midpoints of the value ranges provided by deGroot, Wilson
& Boumans (2002) are updated from the 1st half of 1994 to the 1st half of 2006 by
the all-item, urban CPI, then the average relative reduction in the TESV is
($12732/acre)*(1.36)*(.277)*(1029 acre) = $4,935,495. Table 2.5.9. contains a
finer breakdown.
67

Table 2.5.9. Impacts Summary
Smart
Growth Scenario Sprawl
Total Energy Use (bbl of oil) 2300 1140
Greenhouse Emissions (short
tons/yr) CO2 960.09 473.34
CH4 1.15 0.57
N2O 0.038 0.019
GHG:
Total 997.58 491.82
VOC:
Total Air Emissions (short tons/yr) 0.73 0.36
CO:
Total 10.44 5.15
NOx:
Total 1.08 0.53
PM10 :
Total 0.16 0.08
SOx:
Total 0.34 0.17
Grnhse & Air % Net Reduction in
TESV 50.70%
$9,033,559
Grnhse & Air $$ Net Reduction in TEFSV
Weighted Water Quality "Index" -104075 -124395
Water % Net Reduction in TESV 19.50%
Water $$ Net Reduction in TESV $3,474,446
Biological Integrity 450 684
Bio % Net Reduction in TESV 52.00%
Bio $$ Net Reduction in TESV $9,265,188
Total $$ Net Reduction in TESV $18,298,747 $3,474,445
The results clearly show that the Sprawl Scenario generates more than five times
the ecosystem value reduction than that caused by Smart Growth.
2.6. Pennypack Ecological Vulnerability Assessment (PEVA)
The PEVA team found three particular sources of vulnerability: the main
Philadelphia intake, the PCW-resident sewage treatment plant and PCW
stormwater management.
68

Philadelphia Water Supply
In 2002, the PWD (2002; p. 14) stated that, “The Baxter Water Treatment Plant
provides treated water that comes from the Delaware River. … Particular
tributaries that require special attention to address polluted runoff from
urban/residential areas and agricultural lands include the Pennypack Creek …”
The 600-mgd Baxter Plant is located just up-stream from the confluence of the
Pennypack Creek and the tidal Delaware River, and is the primary source of
Philadelphia water supply. The water supply is vulnerable to PCW impairment
because at flood tides, the Pennypack water moves up river.
The Sewage Treatment Plant
It is apparent that the UM-H STP is severely impacting aquatic life as
concentrations in its discharges far exceed recommended limits. Considering the
land use scenarios conducted as part of this analysis, increasing wastewater
flows and sewage to this plant, which is operating near capacity, will only
complicate nutrient removal and further impair aquatic life and recreation in and
along the Pennypack Creek downstream of the STP. Publicly Owned Treatment
Works (POTWs) are least efficient when operated at capacity because the
hydraulic retention time in primary and secondary clarifiers is reduced, limiting
the ability of plants to settle out solids, including nutrients. Unless the plant is
expanded and outfitted with nutrient-removal technology, water quality would
continue to degrade. As part of its PCW Study (Meenar 2006), the CSC research
team recommended that the Upper Moreland-Hatboro Joint Sewer Authority
conduct a feasibility study to evaluate possible upgrades to significantly improve
the plant’s performance in reducing nutrient levels in its effluent.
Both the Trend and Smart Growth scenarios assume that 1855 new structures
will be built in the watershed. If they are all built within the service area of the
WTP, this would increase flows to the plant by about 0.3 mgd for both scenarios.
(1855 units x 2.66 people per unit x 60 gallons per capita per day of sanitary
water usage = 296,058 gpd). Additional commercial development would also
increase flows and the need for additional treatment capacity." This combined
with a reduction in baseflow stemming from additional ground water withdrawals
in the watershed would further exacerbate the nutrient problems downstream of
the WTP. The receiving stream would have less flow and the larger volume of
effluent would increase loadings of nitrate and phosphate absent a nutrient
removal program. This would further degrade aquatic habitat in the Pennypack
Creek.
Unless the STP improves its nutrient removal, other efforts to improve stream
water quality will only provide nominal improvements. treatment options include
biological removal or chemical additives.
69

Stormwater Management
It was not until the 1970s that requirements for controlling the quantity or quality
of runoff coming from a developed site were a consideration. Water was routed
from a site to the nearest stream in the most expedient manner. This increased
volume of water accelerates erosion and sedimentation and destroys stream
habitat. From the 1970s until recently, stormwater management relied primarily
on the use of detention basins to manage stormwater. While these basins
controlled the peak flows of water, they did not reduce the overall volumes of
runoff and did nothing to address or improve the water quality of runoff.
Stormwater BMPs and regulatory requirements are improving stormwater
management in new developments. However, these actions do little to address
the sins of the past. Water quality and quantity issues in older developments will
require the retrofitting of existing stormwater facilities or the installation of
stormwater controls where none exist in order to reduce the runoff volumes.
Many existing stormwater management facilities in the PCW have become
completely or partially dysfunctional because of poor or no maintenance,
ineffective design, or a combination of such factors. Some of these were
obviously constructed many years ago, as evidenced by their filling by sediments
and debris and the abundant tree growth within them. However, many have been
constructed in recent years, and some have been observed that are quite new,
but which are already evidencing poor performance.
In the summer of 2004 through spring of 2005, a visual assessment of the entire
PCW was performed by Temple researchers in order to get a full picture of what
was actually happening on the ground within the creek’s watershed and its
surrounding riparian corridors. The reconnaissance was conducted mostly on
foot, but often had to resort to “windshield survey” methods, especially in areas
such as residential subdivisions or industrial properties where access was
severely limited or completely prohibited.
This assessment evaluated the condition and functionality of existing stormwater
facilities, assessed the potential for retrofitting such facilities so as to improve
both their environmental and flood control performance, and sought locations for
recommended new stormwater Best Management Practices (BMPs).
Field observations were made at a total of 421 locations within the seven
suburban sub-basins into which the entire watershed study area was divided for
hydrologic and hydraulic modeling purposes. Whatever their age, many
dysfunctional or poorly functioning facilities, whether with respect to management
of discharge rates, volumes, or water quality, have been identified on the
abovementioned maps as having the recommended “Highest Priority” or “High
Priority” for renovation, redesign, and/or retrofitting.
70

Among the 421 observation locations, the CSC has identified that only 73
locations have some form of stormwater management facilities. However, 48 of
these existing facilities are, in the judgment of the study team, either completely
dysfunctional or performing poorly.
One strategy being employed in urbanized watersheds is to retrofit existing
stormwater structures to better control stormwater volume and to improve water
quality. However, public perception and acceptance of stormwater retrofits
cannot be taken for granted. The recent spread of West Nile virus has raised
public concern regarding perceived mosquito breeding sites; basin naturalization
and changes in basin/site hydrology tend to tap into this anxiety despite
abundant evidence that facilities such as wet ponds or constructed wetlands can
provide habitat for insect predators and are actually less likely to harbor such
disease vectors. New stormwater management approaches need to be carefully
explained and resident concerns must be addressed.
The need for education and demonstration of successful stormwater retrofits is
essential to illustrating the effectiveness of such BMPs and alleviating these
public misconceptions, all while improving the conditions within these urbanized
watersheds. Opportunities that municipalities can take advantage of immediately
are those which they can implement on publicly owned lands. Each municipality
owns and/or manages sites that could be potentially “retrofitted” with some form
of BMPs. These sites would not only reduce stormwater impacts, but also serve
as model sites within the region.
The Urban Storm Water Workgroup of the Chesapeake Bay Program compiled
data on the pollutant removal efficiencies of urban storm water management
BMPs. While the actual performance of specific BMP installations varies, the
Workgroup found that practices that could be used in parking areas such as
porous pavement, bioretention areas and infiltration trenches had pollutant
removal efficiencies for Total Suspended Solids (TSS) of 85 to 90% and for Total
Nitrogen (TN) and Total Phosphorous (TP) of 40 to 70%. Conventional detention
basins have pollutant removal efficiencies for TSS of only 10% and for TN and
TP of 5 to 10%. In contrast, practices that might be used to replace or retrofit dry
detention basins had significantly higher pollutant removal efficiencies. For
example, a dry extended detention basin had a pollutant removal efficiency of
60% for TSS, 20% for TN and 30% for TP. A wet pond had an efficiency of 80%
for TSS, 30% for TN and 50% for TP.
The overall watershed stormwater runoff can be controlled through the effective
control of individual sub-basin stormwater runoff. Retrofitting existing stormwater
facilities as well as areas developed prior to the implementation of any
stormwater management controls with BMPs is the key to reducing water quality
and quantity problems within the Pennypack Watershed. The CSC researchers
recommend that each municipality create a stormwater management utility to
provide sufficient revenues to fund such retrofits, to efficiently operate and
71

maintain all stormwater facilities, and to ensure preservation of critical areas that
perform vital stormwater management functions. Furthermore, each municipality
should concentrate its initial efforts on implementing retrofits at the priority sites
within each sub-basin where BMPs can have a significant and cost-effective
impact on controlling stormwater runoff.
2.7. Relation of PEVA to Pennsylvania’s Sustainability Indicators
The Pennsylvania Consortium for Interdisciplinary Environmental Policy (PCIEP;
2004) created the list of environmental indicators shown in Table 2.7.1.
Indicator #1 clearly includes Pennypack Creek. It was found to be moderately
impaired, and a return to a more natural “designated use” will require some
adjustments. Indicator #3. can be related to the Pennypack in at least two ways:
the UM-H STP and landowners may be considered to be using the Creek’s
capacity to receive waste discharges and/or runoff beyond the “sustainable yield”
of the Creek remaining healthy. Indicator #5 applies directly to the PCW – Trend
or Sprawl development will have greater impacts on air, climate and water than
the Smart development. Indicators #6 and #7 are directly affected by increased
peak flows and impairment of the water in the Pennypack. Indicator #9 directly
addresses the impairment of surface water. Besides the STP, the PCW
experiences considerable non-point source pollution. The fuel-use impacts
estimated in Section 2.5.2. directly address Indicator #13. The CSC has shown
that Indicator #14 related to the PCW with respect to property damage and
flooding. It was determined that there are 738 buildings in the 100-year
floodplain. (Meenar 2006) Finally, Indicators #15 and #16 are relevant to the
PCW because the local municipalities are cooperating on sustainable stormwater
management, and the Pennypack Trust under the Directorship of Dr. David
Robertson is spearheading forest, wetlands and Creek restoration.
The research work presented in the previous sections can help a watershed
management group to define PCW sustainability goals and indicators more
refined than the general ones listed in Table 2.7.1. This work remains, and can
be accomplished with future funding.
72

Table 2.7.1. PA Sustainability Indicators
1: # of lakes & surface stream miles supporting
their designated use for aquatic life
GOAL 1:
Sustain, conserve, protect,
enhance, & restore PA’s
environment, natural resources, &
ecological diversity
2: # of designated groundwater sampling points
for each ground water basin located in a
watershed that meets primary drinking water
standards
3: # of water resources being used beyond their
sustainable yield
4: Acres of land by use
5: # of days & # of Pennsylvanians affected when
air quality does not meet health standards
6: Index of ecological diversity
7: Ecosystems or species threatened by
environmental conditions
8: Quantity of waste (by type) generated,
recycled, reused & eliminated
9: Quantity of pollutants released into air, land &
water
GOAL 2:
Reduce harmful effects from
environmental contaminants &
conditions.
10: Annual mean pH of PA precipitation
11: # of public water supply systems meeting all
drinking water standards & maximum contaminant
levels
12. Quantity of waste disposed (by type)
13: Energy use by fuel type
14: Lives lost & property damage from flooding &
mining
15: # of businesses/commercial activities,
government agencies, communities & individuals
implementing sustainable practices
GOAL3:
Engage all Pennsylvanians as
active & informed stewards of the
environment. 16: # of community-based groups performing
activities towards improving their environments
17: Level of environmental literacy of
Pennsylvanians
3. Potential Applications to Other Watersheds
The methodology of the Pennypack Ecological Vulnerability Assessment (PEVA)
study can be easily applied to other watersheds of similar size. The major steps
followed in the PEVA study are: (a) development of a GIS data inventory; (b)
assessment of the state of the watershed; (c) projection of alternate future
landuse scenarios; and (d) assessment of differential impacts of the scenarios.
The summary of the PEVA study work flow has been graphically represented in
Figure 3.1.
73

Figure 3.1 Generic Ecological Vulnerability Assessment Workflow
74

3.1. Data and Metadata
A watershed study team should include researchers from various disciplines,
including environmental and landuse planning, civil and environmental
engineering, ecology, economics, geology, geography, and landscape
architecture. The research team should consider developing a central GIS
database that will include spatial and non-spatial data and metadata. The
database should be available to every researcher.
The database may include basically two types of data: primary and secondary.
Primary datasets can be created using field surveys, digitizing, aero-triangulation,
or LiDAR technologies. Examples of primary datasets can be high resolution
elevation data, including DEM and contour and high resolution ortho-photos. The
PEVA team hired a consultant for creating the high-resolution (2 ft interval)
elevation data and other features, such as stream, bridges and culverts, dams,
and lakes. The building footprint data can be created by digitizing the footprints
from ortho-photos. Primary data can be also collected from a watershed-wide
BMP survey, a water quality survey, and a biological data survey.
The secondary data may be collected from a number of local, regional, state, or
federal agencies, as well as other organizations and online sources. Such types
of data may include demographic data from the U.S. Census Bureau, soil and
geology data from local/regional river basin commissions, landuse and
transportation data from regional planning agencies, parcel and zoning data from
local municipalities, and land cover and tree canopy density data from the US
Geological Survey (USGS). A number of free GIS datasets are also available
from ESRI or other online data provides, statewide GIS data warehouses (e.g.,
the Pennsylvania Spatial Data Access (PASDA), the New York State GIS
Clearinghouse, and the Virginia Geographic Information Network), university
sponsored GIS data warehouses (e.g., the Cornell University Geospatial
Information Repository), and local non-profit agencies and conservancy groups.
Both primary and secondary datasets should be stored in the central GIS
database with required editing and processing. Metadata should be developed
for each type of dataset. Finally, the datasets can be rearranged according to
some broader categories: biological, land feature, and hydrological.
In order to undertake a more refined assessment of the watershed, the study
team may emulate the use by EPA’s ReVA Program of the USGS HUCs . The
watershed can be subdivided into a number of smaller sub-watersheds or sub-
basins, which would correspond to the size and location of the first-order streams
within its boundaries. Sub-basins can be 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.
75

Once the sub-basins are delineated, all of the datasets can be analyzed and geo-
processed to re-assign watershed wide data in each of the smaller sub-basins.
The sub-basins should be given unique IDs before this analysis is performed.
Metadata should be updated for each type of data.
The PEVA assessment was done for the following ecological indicators: water
volume, water quality, biological integrity, and impervious surface. Depending on
data availability, more indicators can be assessed in other watersheds. Thorough
analyses should be done for each of the indicators along with results and
interpretations. The GIS maps and tables showing the results in each sub-basin
will help in assessing the overall current state of any watershed.
3.2. Models
In conducting the analyses for the project, the PEVA research team used several
hydrologic models. Two were developed by the Army Corps of Engineers, in
particular its Hydrologic Engineering Center (HEC), and are regularly used for
hydrologic analyses around the United States. They are HEC-HMS and HEC-
RAS. (USACE 2007)
The Hydrologic Modeling System (HEC-HMS) is designed to simulate the
precipitation-runoff processes of complex watershed systems. It can be applied
to a wide range of geographic areas for solving a broad range of problems. This
includes larger river basin flood hydrology, and small urban or natural watershed
runoff. Hydrographs produced by the program are used directly or in conjunction
with other software for studies of water availability, urban drainage, flow
forecasting, future urbanization impact, reservoir spillway design, flood damage
reduction, floodplain regulation, and systems operation. (USACE 2007)
The WMS software can be used in conjunction with the Corps software as it has
the ability to interface with GIS data. WMS is a graphical modeling package to be
used for watershed hydrology and hydraulics. WMS has embedded HEC-HMS
and HEC-RAS.
The HEC-RAS system contains four one-dimensional river analysis components
for: (1) steady flow water surface profile computations; (2) unsteady flow
simulation; (3) movable boundary sediment transport computations; and (4)
water quality analysis. All four components use a common geometric data
representation and common geometric and hydraulic computation routines. In
addition to the four river analysis components, HEC RAS contains several
hydraulic design features that can be invoked once the basic water surface
profiles are computed.
While these models are readily available, they require considerable expertise to
operate. Users who are unfamiliar with such models should take at least one
introductory course before attempting to use them. The HEC offers several
76

courses on a regular basis. In Pennsylvania, Villanova University sponsors short
courses taught to introduce these models to prospective users.
Streamflow and Baseflow Information
The lack of stream gauges can pose a problem for conducting ReVA-type
analyses for watersheds without them. In such cases, researchers must use
hydrologic values from adjacent and similar watersheds to predict runoff, both for
low flows and extreme events.
Stream gauge data with extensive periods of record enable users to calibrate
models to actual recorded events. Once this is done, watersheds can be
disaggregated into many smaller ones and calibrated again. This allows for an
accurate representation of hydrologic values at a small scale.
Accurate baseflow information can be secured by conducting hydrograph
separations of recorded streamflow data. There are numerous methods for do
so, including the local minimum method, which was described earlier. In the
absence of stream gauge data, information can be obtained from the USGS or
PA DEP. As part of the Act 220 State Water Planning process, the PA DEP has
extended the work done by the USGS for the DRBC to the rest of the
Commonwealth. Through use of a software tool called WAVE, baseflow and
water withdrawal information can be obtained for all watershed areas. Users then
can estimate natural baseflows and current flows, the latter by subtracting water
withdrawals and inter-basin transfers. While this is far from precise, it can give
decision makers a better understanding of the impacts of land and water
development on low flow conditions.
In predicting the runoff for extreme events like the 100-year storm, precipitation
data can be obtained from NOAA and its Atlas 14, which is widely available
throughout the Commonwealth. These data are preferred to the data available in
the older Technical Paper (TP) 40 study published by the U.S. Weather Bureau.
(Herschfield 1961) As part of its Pennypack Creek Watershed Study, the CSC
learned that the TP 40 values, which are widely used throughout the U.S., are no
longer valid as they systematically underestimated the extreme precipitation
events. This was due to a number of factors: the short average duration of the
precipitation records analyzed; the relatively small number of weather stations;
and the statistical distribution used to analyze the data. The NOAA precipitation
data vary from place to place within watersheds, but are generally 10-20% higher
than the values contained in TP 40.
3.3. Scenario Generation
Based on existing data and projected population data, a number of alternate
future landuse scenarios can be generated. At a minimum level, two scenarios
should be generated: trend development and smart growth.
77

In preparation of these land use futures, the PEVA research team had a
conversation with Ms. Megan Mehaffey of US EPA ReVA office (June 5, 2006)
in regards to the various approaches to land use change modeling undertaken in
other ReVA studies. Ms. Mehaffey indicated that there is no one standard
approach, and that the techniques and approaches vary with the size of the
region, the environmental focus of the study, and the type of data available.
Many of the modeling approaches in ReVA -type studies have been utilized at,
and are more appropriate to, larger-scale watersheds. One of the purposes of the
present project is to evaluate the suitability of ReVA methodologies in smaller
watersheds. Based on the PEVA project team’s review of previous studies and
the academic literature, it was concluded that standard, off-the-shelf land use
forecasting programs and models were unsuited to smaller watersheds. As well,
many of these standard programs and methodologies would not make good use
of the higher quality land use data collected for this project. In smaller scale
projects, ReVA-type analyses have utilized standard planning support systems
software such as “What If?”, “INDEX”, or “CommunityViz”. The methodology
described below utilized similar techniques, but without requiring any of these
programs.
Land use change models can be simplistically broken down into two approaches:
“demand driven” and “rule driven.” Many of the larger land use change models,
including some used by NASA, USGS, and EPA (such as SLEUTH, Gigapolis,
Clarke Growth Models, and Cellular Automota models) forecast land use change
based mostly on the physical attributes of land (slope, wetlands, etc), and
distance to growth inducers/growth repelers (usually roads). Though often quite
complex and sophisticated, these models simulate urban growth as a series of
algorithms or rules without statistical calibration on previous land use change in a
region. One advantage of these models is that they require relatively few inputs,
usually the easily available USGS Digital Elevation Models (DEM) and the
National Land Cover Data. The output of the models is usually a variable
indicating whether a particular pixel is developed or not, with some models
allowing the intensity of development to vary between high, medium and low.
Demand driven models, more common in the urban planning field, start with
population and economic growth forecasts. Residential uses and jobs are the
prime drivers of urbanized land uses, and therefore, are the “demand” factors in
these models. In these approaches, the population and employment forecasts
are converted into demand for urbanized land of different types and at different
densities/intensities. Users and decision makers can, interactively, vary the
assumptions about the density/intensity of land use to accommodate projected
land use demand. The models then allocate future urban land uses based on any
number of criteria specific to the model or approach, including availability of
infrastructure and underlying soil and physical suitability. Most simulation models
allow the users to specify lands excluded from development (such as near
78

streams, wetlands, steep slopes, etc.) and to direct/prioritize the growth
allocation (such as contiguous with existing development, in areas with existing
infrastructure, such as public water supply, sewers, and roads). The allocation of
land use demand is iterative, and allows decision makers to test the viability and
impacts of various scenarios. However, one of the difficulties of most demand-
based simulation software is that the assumptions and allocation techniques of
many software packages are “black box” approaches where the user is not
certain of allocation criteria.
The approach taken in PEVA study was to utilize the approach and techniques of
the “demand driven” modeling strategy, and to do so with transparent models
and techniques. In part, this decision was driven by the large amount of high-
quality, high-resolution data collected for the Pennypack region. The data
collected would not usually be utilized in some off-the-shelf software packages. A
goal in this work is to demonstrate that ReVA-type analyses could be performed
using standard land use planning analysis techniques and basic GIS software.
3.4. Impacts Assessment
In an ideal world, there would be a wealth of natural science, demographic and
economic data to feed a detailed, comprehensive and integrated ecologic-
economic model of the region under study. It is fortunate that the PCW has been
the subject of considerable data accumulation, but there is no detailed ecologic-
economic model that can generate impacts of actions taken to perturbate the
fundamental variables. In the absence of such a model, impacts must be
assessed in a piecemeal fashion. The hydrologic models can estimate changes
in base and peak flows, and a qualitative description of potential impacts beyond
the physical and chemical changes may serve to paint a rough picture for
decision support. In the analysis above, the hydrological impacts were estimated
using one set of residential development scenarios, and the air- and water-
related impacts from two related, but different scenarios. If the resources are
available, then these should be combined.
The GREET model (Wang 2005) was very useful for energy use and air and
greenhouse gas emissions, though there is some sacrifice in specificity with
respect to the geographic locale. For water quality and biological integrity, the
literature provides significant guidance. The weighted water quality index above
was the ReVA method of simple sum, with the adjustment made for negative
impacts. The biological integrity was fairly standard. Both were constrained by
the paucity of PCW data.
79

4. The Involvement of Local Officials
As noted in Section 1. above, the PCW research team included some individuals
from government and local NGOs. While not explicitly including local municipal
officials in the ReVA-related work, the CSC has been cooperating with a group of
local officials on floodplain realignment and best stormwater management
practices. Table 4.1. gives the list of suburban municipalities and the designated
official who participated in the effort. It is expected that these same officials will
be receptive to decision support regarding ecological vulnerabilities in the PCW
that may affect their constituents. Once feedback is gotten on the research
presented above, the next step for the CSC is to make local officials aware of
what was done.
Table 4.1. Municipalities Involved in PCW Stormwater Management
Township/Borough Name Title
Abington Township Burton T. Conway Manager
Bryn Athyn Borough Vikki Trost Manager
Hatboro Borough James Gardner Manager
Horsham Township Michael J. McGee Manager
Jenkintown Borough Edwin Geissler Manager
Lower Moreland Township Alison D. Rudolf Manager
Rockledge Borough Michael J. Hartey Manager
Upper Dublin Township Paul A. Leonard Manager
Upper Moreland Township David Dodies Manager
Upper Southampton Township Joseph W. Golden Manager
Warminster Township Barbara Sultzbach Assistant Manager
5. Conclusions
The PEVA team has concluded that the ReVA modeling approach can be scaled
down so as to be informative and appropriate for smaller watershed assessments
in the Commonwealth of Pennsylvania. The basic steps are: (a) development of
a GIS data inventory; (b) assessment of the state of the watershed; (c) projection
of alternative future land-use scenarios; and (d) assessment of differential
impacts of the scenarios. While many larger-scale ReVA assessment tools and
data variables are too coarse for assessment at the local level, it is feasible to
use such processes with more refined local data to accurately outline the impacts
of alternative land use and resource allocation decisions on ecological and other
watershed attributes.
The approach developed for the Pennypack Creek Watershed (PCW) also can
be used as a template for other watersheds. While the PCW is a seriously
impaired watershed in a dense urban setting in the Greater Philadelphia region,
the assessment protocol outlined in this study can be accurately applied for less
developed and more pristine watersheds as well. The report outlines data needs
80

and analytical tools for this information transfer. The PEVA team urges others
conducting such studies to create a multi-disciplinary study team and consider
developing a central and accessible GIS database that includes both spatial and
non-spatial data.
Finally, the ReVA modeling process also can be used to develop watershed
sustainability indicators. The PEVA team outlined four broad indicator categories
that can be applied elsewhere and aggregated to larger regions in Pennsylvania.
It should be noted, however, that for many water quality and biological variables,
good time series information is not available and linking land-use change to
changes in water quality and biological diversity is difficult. Fortunately, for other
variables, such as water volume and impervious surface, new models and better
topographic and hydrologic information allow researchers to more accurately
assess them under different future conditions.
81

References
Barbour, M.T., J. Gerritsen, B.D. Snyder, and J.B. Stribling. (1999). Rapid
Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton,
Benthic Macroinvertebrates and Fish, Second Edition. EPA 841-B-99-002. US
EPA; Office of Water; Washington, DC.
BASIN. (2002). BASIN Water Quality Terminology. Retrieved from
http://bcn.boulder.co.us/basin/natural/wqterms.html on August 10, 2005.
Boulder Area Sustainability Information Network.
Capiella, Karen and Brown, Kenneth (2002). Impervious Cover and Land Use in
the Chesapeake Bay Watershed. Prepared for the U.S. EPA Chesapeake Bay
Program, Unpublished.
Daniels, Tom and Katherine Daniels. (2003). The Environmental Planning
Handbook for Sustainable Communities and Regions. Planners Press, Chicago,
IL.
Davis, W. S. and T. P. Simon (eds) (1995). Biological assessment and criteria:
tools for water resource planning and decision making. Lewis Publishers, Boca
Raton, FL.
de Groot, Rudolf S., Matthew A. Wilson and Roelof M.J. Boumans. (2002).
A typology for the classification, description and valuation of ecosystem
functions, goods and services. Ecological Economics, Vol. 41 (3): 393 – 408.
Dunne, T. and Leopold, L.B. (1978). Water in Environmental Planning, W.H.
Freeman and Co., NY.
Featherstone, Jeffrey P. (1996). Source Water Protection Programs in the
Delaware River Basin, Source Water Protection: An Ounce of Prevention.
American Water Works Association. Blue Bell, PA.
Gunther, O. (1998) Environmental Information Systems, Springer-Verlag, NY.
Hershfield, D.M., 1961: "Rainfall Frequency Atlas of the United States for
Durations from 30 Minutes to 24 Hours and Return Periods From 1 to 100
Years," Technical Paper No. 40, Weather Bureau, US Dept of Commerce,
Washington, DC.
Hitchings, Benjamin. (1998). A typology of regional growth management
system. The Regionalist 3, 1/2: 1-14.
82

Jones, K. Bruce et al. (1997). An Ecological Assessment of the United States
Mid-Atlantic Region: A Landscape Atlas. EPA/600/R-97/130. Office of Research
& Development, US EPA.
Kneese, Allen V. & Blair T. Bower. (1979). The Lower Delaware Valley Case
Study, in Environmental Quality & Residuals Management. Resources for the
Future. Johns Hopkins University Press, Baltimore, MD.
Kepner, William (2003) “Scenario Analysis for the San Pedro River, Analyzing
Hydrological Consequences for a Future Environment.” ReVA-EPA-MAIA
Conference: Using Science to Assess Environmental Vulnerabilities, Valley
Forge, PA, May 13-15, 2003.
Lance H. Butler, et al. (2003). Baseline Assessment of Pennypack Creek
Watershed (2002-2003). Philadelphia Water Dept. , Philadelphia, PA.
Lehigh Earth Observatory. (2005) Lehigh River Water Quality Project. Retrieved
on July 21, 2005, from Lehigh University Earth Observatory Website:
http://www.leo.lehigh.edu/projects/hydroprobe/wqdef.html .
Meadows, D. (1998). Indicators and Information Systems for Sustainable
Development. Sustainability Institute. Last accessed on 5/30/06 at
http://www.sustainabilityinstitute.org/pubs/Indicators&Information.pdf.
Meenar, Md Mahbubur R., ed. (2006). Pennypack Creek Watershed Study.
Center for Sustainable Communities, Temple University Ambler, for the US
Federal Emergency Management Agency.
Myers, M., “Retrofitting Rocky Branch: Applying Natural Features in Urban
Stream Design” Landscape Architecture, Volume 90, Number 3, March, 2000.
National Land Cover Database. (2007). At http://www.epa.gov/mrlc/nlcd.html
Pennsylvania Consortium for Interdisciplinary Environmental Policy (PCIEP;
2004). The State of the Commonwealth: Is Pennsylvania Moving Toward a
Sustainable Future? at http://www.paconsortium.state.pa.us/StateoftheCommonwealth.pdf .
Last accessed on 5/30/06.
Pennsylvania Department of Community and Economic Development. (PDCED;
2005). Pennsylvania Municipalities Planning Code, Article VI. Zoning, cf. 53 P.S.
§ 10604 [5]. at mpc.landuselawinpa.com/MPCode.pdf, last accessed
on January 15, 2007
Philadelphia Water Department (PWD; 2002). Drinking Water Quality Report at
www.phillywater.org/wqr/wqr2002/wqr2002.pdf . Last accessed 1/28/07.
83

Philadelphia Water Department (PWD; 2005). Pennypack Creek Watershed
River Conservation Plan at
http://www.phillywater.org/pennypack/River Conservation Plan/RCP Final Report.htm . Last
accessed 1/28/07.
Schueler, T. (1994). The Importance of Imperviousness. Watershed Protection
Techniques 1(3): 100-111.
Sherry, Jesse (2005) Sustainability Indicators for the Pennypack Creek
Watershed, Master’s Thesis, Department of Community & Regional Planning,
Temple University Ambler.
Sloto, R.A., and Crouse, M.Y. (1996). HYSEP: A computer program for
streamflow hydrograph separation and analysis: U.S. Geological Survey Water-
Resources Investigations Report 96-4040.
Steinitz, Carl (1993). Geographical Information Systems: A Personal Historical
Perspective, The Framework For A Recent Project, (And Some Questions For
The Future); European Conference on Geographic Information Systems,
Genoa, Italy, March 30. (Process available at
http://www.gsd.harvard.edu/studios/brc/maps/fig_5.html).
US Army Corps of Engineers (USACE 2007). HEC-HMS and HEC-RAS
Programs at http://www.hec.usace.army.mil/software/hec-hms and
http://www.hec.usace.army.mil/software/hec-ras . Last accessed 1/30/07/
US EPA. (1993). Guidance Specifying Management Measures for Sources of
Nonpoint Pollution in Coastal Waters. EPA-840-B-93-001c. Washington, DC:
Office of Water.
US EPA 2000a. Guidelines for Preparing Economic Analyses. National Center
for Environmental Economics, EPA 240-R-00-003.
US EPA 2000b. Environmental Planning for Communities: A Guide to the
Environmental Visioning Process Utilizing a Geographical Information System,
Office of R&D, EPA/625/R-98/003. At
http://www.epa.gov/ORD/WebPubs/gis/625r98003.pdf.
US EPA. (2005a) Regional Vulnerability Assessment for the Mid-Atlantic Region:
Evaluation of Integration Methods and Assessments Results. Office of Research
and Development. EPA 09LCB04.RPT.
US EPA. (2005b) Massachusetts Class B and SA Surface Water Quality
Standards. Retrieved from http://www.ipswatch.sr.unh.edu/314cmrclasses.html
on July 21, 2005. Ipswich-Parker Suburban Watershed Channel.
84

US EPA. (2006a) Regional Vulnerability Assessment Program: A Research
Strategy for 2001-2006 (http://www.epa.gov/reva/reva-strategy.pdf) .
US EPA. (2006b) Regional Vulnerability Assessment Program: Toolkit
(http://amethyst.epa.gov/revatoolkit/Welcome.jsp) .
US EPA (2007) Rapid Bioassessment Protocols for Use in Streams and
Wadeable Rivers: Periphyton, Benthic Macroinvertebrates, and Fish. At
http://www.epa.gov/owow/monitoring/rbp/ . Last accessed on 1/18/07.
US EPA. (2007). Rapid Bioassessment Protocols for Use in Streams and
Wadeable Rivers: Periphyton, Benthic Macroinvertebrates, and Fish. At
http://www.epa.gov/owow/monitoring/rbp/ . Last accessed on 1/18/07.
US Geological Survey. (USGS 1996). Water-Use Analysis Program for the
Neshaminy Creek Basin, Bucks and Montgomery Counties, Pennsylvania.
US Geological Survey. (1999). USGS Circular 1225: The Quality of Our Nation’s
Waters, Nutrients and Pesticides. U.S. Department of the Interior and U.S.
Geological Survey, Washington, D.C.
Wang, Michael. (2005). Greenhouse Gases, Regulated Emissions, and Energy
Use in Transportation. Systems Assessment Section, Center for Transportation
Research, Argonne National Laboratory, DuPage County, IL.
Wolman, M. G. and Miller, J. P. (1960). Magnitude and frequency of forces in
geomorphic processes, Journal of Geology, vol. 68. no. 1. pp. 54-74.
85

Appendices
Appendix A.1. ReVA Web Page Summaries
The ReVA Toolkit helps decision-makers evaluate the vulnerability of
ecological goods and services that are valued by society, using several
types of information such as current resource conditions and distributions,
estimated sensitivity of resources to the various stresses, and estimated
spatial distributions of stressors. Of primary importance is the definition of
vulnerability. Vulnerability is the degree to which a system is likely to
experience harm due to exposure to perturbations or stress. Vulnerability
considers both the quality of the valued resources & the intensity of the
stressors. It should be noted that heavily urbanized urban areas are not
considered to be vulnerable because they have already lost most of their
valued natural resources.
The variables used to estimate vulnerability fall into three basic categories.
Resource distribution variables record the current geographic distribution of
important environmental and human resources. Sensitivity variables are
conditions, or changes in conditions, that modify a resource's response to
stress. Stressor variables indicate the distribution of activities or conditions
that combine to cause environmental degradation.
Although the impacts of each individual stressor are important to consider, the
geographic areas most at risk are those that are subject to multiple stresses.
Although the cumulative impacts of multiple stresses across a region cannot yet
be quantified, areas where the possibility of cumulative impacts is high can be
identified.
Species diversity is a good proxy for overall environmental health, since
degraded environmental condition often results in declines in biological
populations. Information about the geographic distribution of cumulative impacts
and resources can be used to identify areas that are most vulnerable to
environmental degradation. For example, areas with high levels of stress and
with high aquatic species diversity might be considered to have the most
vulnerable aquatic populations. Areas with high levels of stress but low numbers
of species are already degraded.
In selecting between alternative scenarios that simulate the future, it is useful to
be able to compare the likely outcomes of each scenario. All land use change
models have some degree of uncertainty. One way to estimate the probability of
a certain type of change is to use a "weight of evidence" approach, as we have
done here. Responsible decision-making requires balancing among multiple
criteria, and various stakeholders value criteria differently. For example,
decisions about land development may involve changes in air quality, water
quality, economic conditions, and native biodiversity. Stakeholders concerned
about environmental conservation will likely prioritize criteria differently from
those concerned about economic development. The EDT allows one to view how
various stakeholders' preferences (values) would affect decision priorities.
86

Stakeholder values can be viewed using only variables important to the individual
or group (e.g. selecting variables relevant to conserving native aquatic species or
for evaluating human health risks) or in multiple weighting combinations to
illustrate trade-offs.
A.1.1. Data Preparation
One basic operation preceding assessment was putting data on a consistent
scale. Integrating the data to get a watershed summary across all variables
involves a few pre-processing steps. To use the variables in a consistent
manner, the data need to be transformed to the same scale. All variables are
scaled so that they were on a 0 (best) to 1 (worst) scale for each variable. To do
this, the following was applied to all variables:
1. (Rescale) Subtract the minimum value for each variable from the data,
setting the minimum value to zero.
2. (Normalize) Divide the data by the maximum value. This sets the maximum
value to one, and sets all values in between to be on the [0, 1] scale.
3. (Directionalize) If the variable's direction dictates that "higher is better", then
take the variable and reverse its normalized direction.
For example, suppose a variable has values of {3, 5, 7, 10, 11}, where the
"direction" for this variable is "-1" (higher is better). Following our steps, the data
becomes:
1. Rescale: {3, 5, 7, 10, 11} -> {0, 2, 4, 7, 8}
2. Normalize: {0, 2, 4, 7, 8} -> {0.00, 0.25, 0.50, 0.875, 1.00}
3. Directionalize: {0.00, 0.25, 0.50, 0.875, 1.00} -> {1.00, 0.75, 0.50, 0.125,
0.00}
Rescaling makes all data range from a set minimum to a set maximum,
normalizing allows comparisons between variables on a consistent scale,
and directionalizing creates a consistent direction for good and bad values
of a variable. Among groups of variables that were correlated to each other,
one variable was chosen to represent the group.
A few of the variables are fairly skewed (most values are near either zero or one
on the normalized scale). This may slightly affect a couple of the integration
methods, such as PCA. Methods that involve ranking, such as the quintile
methods, remain unaffected. The criticality method and clustering should also be
relatively unaffected since they are based on a distance measure.
In terms of types of data, the ReVA methods focus on a few important
categories. The first category is Sensitive Environmental Resources. This
includes ecosystems already stressed, migratory bird stopovers, regions
87

critical to native species, intact interior forest patches, and ecosystems with
low acid-neutralizing capacity. The second category is Current Conditions,
which contains
forest productivity, air quality, groundwater quality, water
quality, aquatic & terrestrial biodiversity, human health, and fiscal health.
The third group is made up of Drivers of Change. The drivers are
r
resource
extraction, land use, non-indigenous species, pollution, and climate change.
Key generic Stressors are given as agricultural runoff, atmospheric
deposition, loss of habitat, and non-point source pollution. With respect to
EPA Region III, the Mid-Atlantic Region, the Stressors are acid deposition,
coal mining, human population, landscape pattern, agricultural nitrogen,
ground-level ozone, and soil erosion / sedimentation.
The methods used to integrate the data into regional overviews and
environmental assessments covered a relatively wide range of analysis.
The best quintile method, the worst quintile method, and the radar summary give
a high-level glance at overall environmental quality. The summation method, in
combination with the Principle Components Analysis / Distance method, can
highlight sensitivity areas. These two methods have different and complementary
sensitivities. Areas that show well or poor in both have the strongest indications
of environmental quality. The state space method, because of its distance
measure, tends to differentiate between the middle (not worst or best)
watersheds. The weighted sum method allows user defined weights for individual
variables or groups. This method allows for the decision-maker to differentiate
based on specific preferences or interests. The stressor/resource matrix method
indicates the most critical stressors and most stressed resources across the
region. The overlap method compares a hypothetical future scenario to a present
environmental condition. The criticality method attempts to highlight areas at risk
for major change. This method uses a distance measure to a pre-defined "natural
state." Cluster analysis and self-organizing maps can be used as planning tools,
as these methods can be tailored for specific planning objectives.
A.1.2. Examples of Variables Used to Estimate Vulnerability
Resource Distribution
Numbers of native aquatic species are a resource of value to society. High
numbers of these species indicate areas that could be a higher priority for
protection over areas with low numbers of species. Numbers of native species
can also tell us where stresses have been acting for some time (low numbers of
native species) or where stress has historically been low (high numbers).
ReVA uses red to indicate lower resource value and green to indicate higher
resource value.
88

Sensitivities
For example, the percent of forest cover that has been defoliated is an
indication of how susceptible to damage a given area of forest may be to
additional stress, and thus is considered to be a sensitivity variable for forest
condition in general. Forests that have been heavily defoliated are more likely
to experience high levels of mortality when an additional stress, such as air
pollution, is also high. High levels of forest mortality can in turn result in
increased sediment loadings (through reduced uptake of water by trees with
higher runoff) and increased nutrients (through decay of trees) in streams.
In a final integration for vulnerability, a user may chose to weight this factor
more highly. Thinking of it in terms of an if-then scenario may be helpful; for
example, research and experience may indicate that the stress on a forest
jumps significantly as percent defoliation rises above some threshold level. In
this case, the cumulative stress displayed for areas above the threshold level
can be amplified by some appropriate factor. In time and with experience, the
best guesses for appropriate threshold and amplification factors may be better
understood.
89

Stressor Distribution
For example, acid rain stresses plants, contributing to poor forest health. Nitrate
(NO3) and Sulfate (SO4) are the major pollutants contributing to acid rain, and
thus estimates of Nitrate wet deposition are commonly used as an indication of
acid rain. The degree to which Nitrate wet deposition becomes a pollutant
depends on the location and the physiochemistry of the forest soils or
waterbody upon which it falls. Some important variables cannot be measured
directly, but their distribution can be estimated using well-established models.
In this example, the spatial distribution of wet nitrate deposition is estimated
from rainfall data (amount) and proximity to sources of NOx air pollution (from
fossil fuel burning). Nitrate from air pollution contributes to nutrient levels in
streams, which affects aquatic habitat (in particular aquatic plants) and native
species' condition.
Here ReVA uses red to indicate higher presence of stressors and green to
indicate lower presence of stressors.
90

A.1.3. Assessment Questions
Resource managers and other decision-makers are frequently required to make
decisions about priorities. A common question is "Given limited resources, what
environmental problems and what geographic areas are most in need of
attention?" Answering this difficult question requires exploration of a series of
focused assessment questions before actions are taken to protect valued
resources, human health, or quality of life. These questions might focus on
information such as:
• an evaluation of current overall conditions
• risk of future environmental degradation
• sustainability of the system
• current and future value to society
• feasibility of taking some action
All of these assessment questions can be addressed to some degree using
available data and information. Each requires a different integration method (see
the tutorial for more information on this), and each receives a different answer, or
in the case of a visual representation, a different map.
Different assessment questions require different types of variable data and
metrics, and possibly different analysis or integration methods. The selection of
variables and methods is driven by the particular issues of concern and the
questions a user brings to the vulnerability assessment. Further information on
91

selecting data and analysis methods is provided in the tutorial and in the
analytical sections of the tool.
To help you understand how the ReVA EDT can assist in answering these
questions, the next few pages show examples of the types of information that the
web tool can provide.
Cumulative Impacts
This map shows all four stressors: nitrogen, sediment, aquatic exotic species and
nitrate deposition). The individual stressor information has been combined using
an integration method that counts the number of times that a watershed is among
the worst 20% (the Worst Quintiles integration method). All stressors are equally
weighted.
Distribution of Resources
One of the first questions asked in any assessment is "Where are the most
important environmental resources?" It is important to identify areas where
management actions can prevent further damage and improve conditions.
While restoration of degraded areas is valuable, preservation of areas that are
still in good shape is a critical and cost-effective way to maintain a region's
environmental health. Areas with high species diversity have often been less
impacted by stressors while highly impacted areas show little native species
diversity. The duration of stress will also influence condition; unfortunately
historical data on stressor distributions are rarely available.
92

Assess Vulnerability
This map shows the distribution of aquatic native species in relative terms; i.e.
the 25% of watersheds with lowest species counts up to the 25% with highest
counts. This map adds the normalized values of the stressors and breaks them
into four equal bins with the ones with the most stressors in the right most
column (column 4). The single resource uses the normalized value and breaks it
into 4 equal bins.
Interactive Maps
In the analysis tools, some maps like this map are interactive which allows you
to get detailed information about the watersheds and the variables displayed.
You can drill-down into the information and get other maps and visualizations
similar to the radar plots displayed on the next page.
93

As the cursor moves over each watershed, the name is displayed in the upper
right hand corner. A right-click on a watershed brings up a menu with drill-down
options. This screen shot shows the Lower Susquehanna selected and the menu
with available drilldown options. The available options include:
* Watershed Details - For the selected watershed, this displays all the
watershed's raw data for all the indicators.
* All Watersheds - data for TOTALN - This selection displays a table with the
variable (Nitrogen in surface water) values for all watersheds.
* Radar plot for watershed - This displays the radar plot for the selected
watershed (an example is on the next page).
In the upper right hand corner, the name of the watershed is displayed.
Data Visualization
The EDT includes many different types of variables for each watershed and
several methods for viewing and assessing these variables. For example, the
radar plots below provide a quick overview of a specific watershed's condition. A
radar plot can be thought of as a histogram that has been bent into a circle with
each individual spoke representing a variable. Plots with more green indicate
watersheds with less degraded resources or fewer stressors. Each individual
94

spoke displays more green if the value of the variable is good. If the value of a
variable is bad for a watershed, it will show less or no green. In the toolkit, the
variable descriptions show up in the interactive graph so that the user can tell
which variable is represented by each spoke.
The Lower Guyandotte watershed (first plot) exhibits better ecological condition
than the Lower Susquehanna watershed (second plot).
95

A.1.4. Future Vulnerability
We cannot predict the future, but available models do allow us to estimate the
likely distribution of environmental vulnerability in the future based on decisions
made today. For example, the future distribution of stresses on aquatic
biodiversity can be estimated based on land use change models and current
species distribution information. This map shows the distribution of areas that
both have high species diversity today and are likely to experience increased
human development.
This graphic shows where different models of land use change agree:
watersheds where 4-5 models all predict significant change are considered most
likely to experience change.
96

A.1.5. Multiple Criteria Decision-making
Below are two examples of vulnerability maps that highlight multiple criteria. In
one case aquatic species are highlighted. The other case highlights human
health stressors. Decisions about resource allocations will depend on how these
various perspectives are reconciled.
97

A decision criterion of aquatic stress is made up of a combination of individual
variables. These variables can be weighted differently to reflect knowledge about
the relative importance of individual stressors (e.g. nitrogen in surface water may
be more important than risk of forest mortality in defoliated areas), or by
feasibility of reducing the stress (e.g. it may be easier to reduce nonpoint runoff
of nutrients than to reduce regional air pollution). The EDT allows weights to be
interactively set between 0 and 10. The map above was created by setting these
weights as shown below (above).
Another potential decision criterion is human health stressors. Again, this
decision criterion is made up of a combination of variables, with weights selected
for the individual variables.
Trade-offs
Trade-offs between decision options can be evaluated by additionally weighting
the decision criteria based on alternative priorities. The maps below show where
actions would be targeted if reducing stress to aquatic species is a priority (left),
and where reducing stress to human populations is priority (right).
98

Next Steps
This concludes the guided tour. Now that you have some feel for how the EDT
can be used, we invite you to try it out for yourself. Environmental decision-
making is a complex process and no single view of regional conditions or
vulnerabilities can sufficiently represent the many factors that could be
considered. Our goal in creating the EDT has been to help people gain a broader
understanding of the complex interactions that influence environmental health.
To learn more or to use the tool, check out these pages:
* Tutorial - this part of the EDT provides more detailed information about the
statistics used to integrate data and model results as well as which
integration method should be used for different assessment questions. It
also provides information about data preparation.
http://www.waratah.com/revanew/Tutorial.asp
• Online User's Guide - the user's guide provides specific guidance for how to
access the full capabilities of the EDT.
• Glossary - direct links are provided to define highlighted words throughout
the EDT.
• Bottom-line data analysis - this part of the tool is designed for those who
want to see the bottom-line without delving deeply into the data. It has a
number of default variables preselected for different assessment questions.
• Full data analysis - use of this capability requires a good understanding of
the available data and statistical relationships. The analytical part of the tool
can be used to explore possible relationships between and among different
variables, display individual variables, drill-down to individual watersheds,
etc. http://www.waratah.com/revanew/demonstrations.asp
**********************************************************************
99

A.1.6. Demonstrations
The following demonstrations give various graphical views of the MAIA region
data. The data consist of many resources/stressors. The visualizations allow the
user to view data for the subareas of the region. There are 141 watersheds and
733 EMAP hexes in the MAIA region. From this page you can get to the various
visualizations of the data.
Preferences
User can choose region to work with and possibly other items. Current
Preferences: Region - All of MAIA
Variable Summaries
The variable summaries provide graphical summaries of the variable data and
regional maps with single variables displayed. The user can interact with some of
the maps via drill-down into watershed and EMAP hex details and moving
between graphical views.
Integration Methods
The area summaries provide maps of the area of interest either watersheds or
EMAP hexes, displaying results of integration methods and allowing one to
compare two integration methods together. The user gets to make various
choices such as integration methods to compare and whether to display
watersheds or EMAP hexes.
Restoration Opportunities
This demonstration allows one to weight various coverages to see how they
affect restoration in various watersheds.
Overview of region
This visualization allows you to display various land features to get an idea of
where cities, roads, rivers, and other features are in the region. This allows one
to view where roads maybe causing a watershed to have more stressors and
fewer resources.
Data Download
From this page, you can select to download the various data used in these
demonstrations. You can download the raw data, the normalized data, or
information about the variables. This page will also include detailed metadata
about the variables in the future.
100

Appendix A.2. Data & Metadata: Technical Details & Tables
A.2.1. List of GIS Files
49BASIN (Pennypack 49 Sub Basins)
Two shape files:
 BASIN_BOUND: Pennypack Creek Watershed Boundary
 49BASIN: 49 Sub Basin boundaries with specific ID numbers
BASE_DATA
Three shape files:
 MUNI_BOUND: Municipal Boundaries in the watershed
 STREAM: Habitat data of 20 monitoring stations set by PWD
BIO (Biological Data)
Three shape files:
 FISH: Fish data of 20 monitoring stations set by PWD
 HABITAT: Habitat data of 20 monitoring stations set by PWD
 MACRO: Macroinvertebrates data of 20 monitoring stations set by PWD
DEMOG (Census Block Group Level Demographic Data)
Two shape files:
 DEMOG1990
 DEMOG2000
GEOL (Geology and Soil)
Two shape files:
 GEOL_BF: Location of different types of rocks and their base flow amount
 SOIL: Hydrologic Soil types
LANDF (Land Features)
Three shape files:
 LU1990: Landuse by Category in 1990
 LU1995: Landuse by Category in 1995
 LU2000: Landuse by Category in 2000
Six raster files:
 FVC1985: Fractional Vegetative Coverage in 1985
 FVC2000: Fractional Vegetative Coverage in 2000
 LC2001: Land Cover by Category
 IMPV2000: Impervious Surface in 2000
 IMPV1985: Impervious Surface in 1985
 CAN2001: Canopy Coverage
101

WTR_REL (Water Related)
Nine shape files:
 A_EC: Amount of Effluent Concentration for 20 monitoring stations set by PWD
 A_TGWW: Total Groundwater Withdrawals
 BRIDGE_CULVERT: Location of culverts and bridges
 DAM: Location of Dams
 MPD: Maximum Permitted Discharge
 RIP_BUF: Location of banks where riparian buffer is absent either on one side or
both sides.
 WETLAND: Wetland areas
 SSA: Sewer Service area
 WSA: Water service area
A.2.2. Shape File Metadata
Shape File Name: 49BASIN (49 Sub Basins of Pennypack Watershed)
Author Information:
Name: Md Mahbubur Meenar, ASM Bari
Organization: Center for Sustainable Communities (CSC), Temple University, PA
Email: meenar@temple.edu, asmbari@temple.edu
Date: July 15, 2005
Description:
Sub basin is a drainage area or small basin within a large watershed.
Type of feature:
Polygon
Purpose:
The existing models for the Regional Vulnerability Assessment (ReVA) Program —
which may be used by researchers to create future development scenarios — focus
predominantly on large river basins. The CSC study is taking the methodology of ReVA
and downscaling it for smaller watersheds, such as the Pennypack Creek. In order to
assess the vulnerability of the watershed, the whole area needs to be divided into
smaller sub basins. The purpose of this shape file is to display the sub basin boundaries
generated by the researchers for this study.
Original Data Source:
Data originated at the CSC.
Stream centerline file source: CSC
Actions Taken for Data Processing:
Projection information: State Plane NAD 1983 (Feet) Pennsylvania South
Using the stream centerline shape file as a reference, the whole Pennypack Watershed
is divided into 49 Sub Basins based on the stream order, stream flow, and DEM data.
This Sub Basin boundary creation process was administered by Dr. Michel Boufadel of
102

the Department of Civil and Environmental Engineering at Temple University. The
software WMS 7.1 was used to generate the boundaries.
Description of Attribute Table Fields:
No of Fields (not including FID and Shape): 3
No of Records: 49
Field Name Description
BASIN_ID Randomly chosen Basin IDs for every Sub
Basin
AREA_SWM Area of Sub Basin in square mile
AREA_SQF Area of Sub Basin in square feet
Shape File Name: FISH
Data Analyst:
Name: Jesse Sherry
Organization: Center for Sustainable Communities (CSC), Temple University, PA
Email: jsherry@temple.edu
Date: 7/21/2005
Contact Information:
Name: ASM Abdul Bari / MD Mahbubur R Meenar
Organization: CSC, Temple University, PA
Email: asmbari@temple.edu, meenar@temple.edu
Description:
Data describes the Fish species present at various sites and the water quality derived by
analyzing these species.
Type of feature:
Point
Original Data Source:
Philadelphia Water Department (PWD)
Data creation year: 2002
Data acquired by CSC in 2004.
Actions Taken for Data Processing:
Projection information: State Plane NAD 1983 (Feet) Pennsylvania South
Following the twenty monitoring stations set by PWD, the original data was cleaned up
and all categories were arranged by Dr. Peter Petraitis (ppetrait@sas.upenn.edu) of the
University of Pennsylvania.
Description of Fields of Attribute Table:
No. of Fields (except FID and shape): 21
Field Name Description
F_NO_SP Total Number of Fish Species
F_NO_BEN Number of benthic insectivorous species
F_NO_WAT Number of Water Column Species
F_NO_INT Number Of Intolerant/Sensitive Species
103

F_P_WHSK Percent White Sucker
F_P_GEN Percent Generalist
F_P_INSE Percent Insectivores
F_P_CARN Percent Top Carnivores
F_P_DIS Percent of individuals with disease and anomalies
F_P_DOM Percentage of dominant species
F_DEN Density
F_NO_IND Number Of Individuals
F_BIOM Biomass per square meter
F_MODIND Modified Index Of Well-Being
F_SWDI Shannon-Weiner Diversity Index (H')
F_NO_CYP Number of Cyprinid Species
F_P_RES Percent of Resident Species
F_P_EXOT Percent of Introduced/Exotic Species
F_IBI The Index of Biological Integrity Score (IBI) from the PWD Reporti
F_BIO_IN The IBI Score expressed as a percentage
BIO_ID PWD Monitoring Stations From which data for this basin was taken
No. of Records: 20
Shape File Name: HABITAT
Data Analyst:
Name: Jesse Sherry
Organization: Center for Sustainable Communities (CSC), Temple University, PA
Email: jsherry@temple.edu
Date: 7/21/2005
Contact Information:
Name: ASM Abdul Bari / MD Mahbubur R Meenar
Organization: CSC, Temple University, PA
Email: asmbari@temple.edu, meenar@temple.edu
Description:
Habitat is the physical location or type of environment in which an organism or biological
population lives or occurs. (http://www.biology- online.org/dictionary/habitat). This data
describes the habitat present at various sites along the Pennypack Stream.
Type of feature:
Point
Original Data Source:
Philadelphia Water Department (PWD)
Data creation year: 2002
Data acquired by CSC in 2004.
Actions Taken for Data Processing:
Projection information: State Plane NAD 1983 (Feet) Pennsylvania South
Following the twenty monitoring stations set by PWD, the original data was cleaned up
and all of the categories were arranged by Dr. Peter Petraitis (ppetrait@sas.upenn.edu)
of the University of Pennsylvania.
104

Description of Fields of Attribute Table:
No. of Fields (except FID and shape): 17
Field Name Description
L_BANK Bank Stability (Left Bank)
R_BANK Bank Stability (Right Bank)
CH_ALT Channel Alteration
CH_FLOW Channel Flow Status
CH_SIN Channel Sinuosity
EMBED Embeddedness
EPIF_SUB Epifaunal substrate cover
RIF_FREQ Frequency of Riffles (or Bends)
POOL_SUB Pool Substrate Characterization
POOL_VAR Pool Variability
RIP_V_L Riparian Vegetative Zone Width (Left Bank)
RIP_V_R Riparian Vegetative Zone Width (Right Bank)
SED_DEP Sediment Deposition
VEG_P_L Vegetative Protection (Left Bank)
VEG_P_R Vegetative Protection (Right Bank)
VEL_DEPT Velocity/Depth Regime
BIO_ID PWD Monitoring Stations From which data was taken
No. of Records: 20
Shape File Name: MACRO (Macroinvertebrates)
Data Analyst:
Name: Jesse Sherry
Organization: Center for Sustainable Communities (CSC), Temple University, PA
Email: jsherry@temple.edu
Date: 7/21/2005
Contact Information:
Name: ASM Abdul Bari / MD Mahbubur R Meenar
Organization: CSC, Temple University, PA
Email: asmbari@temple.edu, meenar@temple.edu
Description:
Data describes the Macroinvertebrates present at various sites and the water quality
derived by analyzing these invertebrates. A Macroinvertebrate is an animal without
a backbone in at least one stage of its life cycle, usually the nymph or larval stageii.
Benthic macroinvertebrates such as insects, worms, and molluscs are the preferred
group of aquatic organisms monitored in water quality assessment programs (Hellawell
1986) because: (1) they provide an extended temporal perspective (relative to traditional
water samples that are collected periodically) because they have limited mobility and
relatively long life spans (e.g., a few months for some chironomid midges to a year or
more for some insects and molluscs); (2) the group has measurable responses to a wide
variety of environmental changes and stresses; (3) they are an important link in the
aquatic food web, converting plant and microbial matter into animal tissue that is then
105

available to fish; (4) they are abundant; and (5) their responses can be analyzed
statistically (Weber 1973). Thus, the presence or conspicuous absence of certain
macroinvertebrate species at a site is a meaningful record of environmental conditions
during the recent past, including ephemeral events that might be missed by assessment
programs, which only rely on periodic sampling of water chemistry. Most stream
ecosystems have relatively diverse macroinvertebrate assemblages with species from a
number of different orders [e.g., mayflies (Ephemeroptera), and caddisflies (Trichoptera),
stoneflies (Plecoptera), beetles (Coleoptera), true flies (Diptera)]. Likewise, the common
trophic groups (i.e., herbivores, detritivores, and predators) are represented by a number
of different species. Various abiotic factors (e.g., hydrology, substrate, temperature,
oxygen, and pH) and biotic factors (e.g., food quality and quantity, interactions with
competitors or predators) have molded, through natural selection, a unique set of
optimum environmental requirements for each species. These environmental
requirements contribute significantly to the distribution and abundance of these
organisms within and among natural stream ecosystems and influence their response to
environmental perturbation.iii
Type of feature:
Point
Original Data Source:
Philadelphia Water Department (PWD)
Data creation year: 2002
Data acquired by CSC in 2004.
Actions Taken for Data Processing:
Projection information: State Plane NAD 1983 (Feet) Pennsylvania South
Following the twenty monitoring stations set by PWD, the original data was cleaned up
and all of the categories were arranged by Dr. Peter Petraitis (ppetrait@sas.upenn.edu)
of the University of Pennsylvania.
Description of Fields of Attribute Table:
No. of Fields (except FID and shape): 14
Field Name Description
M_NO_SP Number of Species
M_HBI Hilsenhoff Biotic Index
M_P_DOM Percent of the Taxa that is the Dominant Taxa
M_D_TAX Dominant Taxa
M_P_FIL Percent of Filter/Collector Species
M_P_GATH Percent of Gatherer/Collector Species
M_P_SCR Percent of Scraper Species
M_P_SHR Percent of Shredder Species
M_P_MODT Percent of Moderately Tolerant Species
M_P_TOL Percent of Tolerant Species
M_P_INTO Percent of Intolerant Species
M_BIO_AS Biological Assessment of Stream based on the
Macroinvertebrate Population from the PWD Reportiv
M_BIO_IN Percentage representing the Biological Integrity of the
Pennypack from the PWD Reportiii
BIO_ID PWD Monitoring Stations from which the data was taken
106

No. of Records: 20
Shape File Name: DEMOG1990 (Census Block Groups)
Contact Information:
Name: Kurt Paulsen, Ph.D.
Organization: Center for Sustainable Communities, Temple University, PA
Email: kurt.paulsen@temple.edu
Date: 08-18-2005
Description:
1990 Census Block Groups with basic demographic data.
Original Data Source:
Data are from the United States Census Bureau. Shape files are from Census
TIGER/Line Cartographic Boundary files, Census Block Groups for 1990. Census Data
for 1990 (STF3) were accessed from CD-Roms.
Actions Taken for Data Processing:
Shape files: Data for 1990 are originally in unprojected Geographic (lat/lon) format.
Shape files were reprojected into Pennsylvania State Plane Feet South (NAD83).
Census Block Groups for Bucks, Montgomery and Philadelphia Counties were initially
produced.
Block groups which had any portion of their area within the Pennypack Creek watershed
were selected. For population and housing unit estimates, the percent of a Census
Block Group within the Pennypack watershed and/or within subbasins were used to
adjust figures. For example, if a Census Block Group has 10 percent of its area within
the watershed, then 10 percent of its housing units and population are assumed to be
located within the watershed. Similarly, if 10 percent of a block group is located in one
sub-basin, then 10 percent of its population and housing figures were assigned to that
sub-basin.
Data files: Data for 1990 were from the Census SF3 (Summary File 3). The following
tables/variables were collected:
P001001: Total Persons
P080A001: Median household income in 1989
H0010001: Total Housing Units
Description of Fields of Attribute Table:
No. of Fields (except FID and shape): 8
Field Name Description
STATEFP State FIPS Code (Pennsylvania =42)
CNTY County FIPS Code
TRACT Census Tract FIPS Code
BLCKGR Census Block Group Number
BLCKFIPS Census Block FIPS code
P0010001 Total Population
P080A001 Household Median Income, 1999
107

H0010001 Total Housing Units
No. of Records: 221
Additional Information:
Census Bureau estimates of median-household income use 1-year prior to each
decennial census because the question asks respondents to report household income
for the previous year. Note: data for income are in nominal (non-inflation adjusted)
dollars.
Shape File Name: DEMOG (Census Block Groups)
Contact Information:
Name: Kurt Paulsen, Ph.D.
Organization: Center for Sustainable Communities, Temple University, PA
Email: kurt.paulsen@temple.edu
Date: 08-18-2005
Description:
2000 Census Block Groups with basic demographic data.
Original Data Source:
Data are from the United States Census Bureau. Shape files are from Census
TIGER/Line Cartographic Boundary files, Census Block Groups for 2000. Census Data
(SF3) for 2000 were downloaded from http://factfinder.census.gov.
Actions Taken for Data Processing:
Shape files: Data for 2000 are originally in unprojected Geographic (lat/lon) format.
Shape files were reprojected into Pennsylvania State Plane Feet South (NAD83).
Census Block Groups for Bucks, Montgomery and Philadelphia Counties were initially
produced. Block groups which had any portion of their area within the Pennypack Creek
watershed were selected. For population and housing unit estimates, the percent of a
Census Block Group within the Pennypack watershed and/or within subbasins were
used to adjust figures. For example, if a Census Block Group has 10 percent of its area
within the watershed, then 10 percent of its housing units and population are assumed to
be located within the watershed. Similarly, if 10 percent of a block group is located in
one sub-basin, then 10 percent of its population and housing figures were assigned to
that sub-basin.
Data files: Data for 2000 were from the Census SF3 (Summary File 3). The following
tables/variables were collected:
P001001: Total Persons
P053001: Median household income in 1999
H001001: Total Housing Units
Description of Fields of Attribute Table:
No. of Fields (except FID and shape): 8
Field
Name Description
STATE State FIPS Code (Pennsylvania =42)
108

COUNTY County FIPS Code
TRACT Census Tract FIPS Code
GROUP Census Block Group Number
Standard Tape File Identification
(=Census Block Group FIPS) STFID
P001001 Total Persons
P053001 Household Median Income in 1989
H001001 Total Housing Units
No. of Records: 302
Additional Information:
Census Bureau estimates of median-household income use 1-year prior to each
decennial census because the question asks respondents to report household income
for the previous year. Note: data for income are in nominal (non-inflation adjusted)
dollars.
Shape File Name: GEOLOGY & BASE FLOW
Contact Information:
Name: Md Mahbubur R Meenar, GIS Coordinator
Organization: Center for Sustainable Communities (CSC), Temple University, PA
Email: meenar@temple.edu
Date: 07/15/05
Description:
Bed rock refers to the rock underlying other unconsolidated material, i.e. soil. This file
displays the percentages of different types of generalized geology in each of the 49 sub
basins of Pennypack Watershed Area. Each type reflects a designation of certain
hydrologic properties.
Type of feature:
Polygon
Purpose:
Increased development in major parts of the Pennypack Creek Watershed has
increased public, industrial, and commercial demand for water. Further withdrawals may
reduce groundwater availability and stormflow. This database will help conduct any
groundwater assessment for the Pennypack Watershed Area.
Original Data Source:
Sources: Philadelphia Water Department (PWD) and Delaware River Basin Commission
(DRBC)
Year of Publication: 10/01/98 for DRBC data
Data acquired by CSC in 2005.
Actions Taken for Data Processing:
Projection information: State Plane NAD 1983 (Feet) Pennsylvania South
The original files were collected and/or processed into GIS shape file format. The PWD
shape file covers the geology of the watershed area inside Montgomery and Bucks
109

Counties and DRBC shape file covers the watershed area inside Philadelphia County.
These shape files are coded differently for different rock type. Following Dr. Jeffrey
Featherstone’s (Director, CSC) suggestion the DRBC rock type coding was taken as
standard and these codes were incorporated in the PWD shape file. Once these files
were merged together, the final shape file was clipped by Pennypack Watershed Area.
Rock codes have been assigned to different types of rocks. This coding is consistent
with the Geology shape file that the CSC has developed.
Description of Fields of Attribute Table:
No of Fields (not including FID and Shape): 7
Field Name Description
ROCK_TYPE This is an attribute used to assign a general geologic class. It reflects a
designation of certain hydrologic properties. Five types of rocks are listed.
ROCK_CODE A numeric code for each type of rock, randomly assigned by CSC
1 = Crystalline Rock other than Diabase
2 = Unconsolidated Sediment
3 = Carbonate Rock
4 = Sedimentary other than Carbonates
5 = Diabase
AREA Square mile area of the polygons for each rock type
MEDIAN This attribute assigns the median baseflow rate in mgal/day/sqm for each of
the five geologies listed above
TEN_YR This attribute assigns the ten year recurrence baseflow rate in mgal/day/sqm
for each of the five geologies listed above
TWENTYFIVE This attribute assigns the twenty five year recurrence baseflow rate in
mgal/day/sqm for each of the five geologies listed above
FIFTY_YR This attribute assigns the fifty year recurrence baseflow rate in mgal/day/sqm
for each of the five geologies listed above
No of Records: 12
Additional Information:
In order to get the contact information for the original metadata or any other relevant
information from DRBC, please visit their web site at www.state.nj.us/drbc.
Shape File Name: SOIL (Hydrologic Group)
Contact Information:
Name: Md Mahbubur R Meenar, GIS Coordinator
Organization: Center for Sustainable Communities (CSC), Temple University, PA
Email: meenar@temple.edu
Date: 08/02/05
Description:
A hydrologic group is a group of soils having similar runoff potential under similar storm
and cover conditions. Soil properties that influence runoff potential are those that
influence the minimum rate of infiltration for a bare soil after prolonged wetting and when
not frozenv. The soils are placed into four groups, A, B, C and D, and three dual
classes, A/D, B/D, and C/D. According to the National Soil Survey Handbook of the
Natural Resources Conservation Services, the definitions of the hydrologic soil classes
are as foll
ows:
110

A. (Low runoff potential). The soils have a high infiltration ratevi even when thoroughly
wetted. They chiefly consist of deep, well drained to excessively drained sands or
gravels. They have a high rate of water transmissionvii.
B. The soils have a moderate infiltration rate when thoroughly wetted. They chiefly are
moderately deep to deep, moderately well drained to well drained soils that have
moderately fine to moderately coarse textures. They have a moderate rate of water
transmission.
C. The soils have a slow infiltration rate when thoroughly wetted. They chiefly have a
layer that impedes downward movement of water or have moderately fine to fine texture.
They have a slow rate of water transmission.
D. (High runoff potential). The soils have a very slow infiltration rate when thoroughly
wetted. They chiefly consist of clay soils that have a high swelling potential, soils that
have a permanent high water table, soils that have a claypan or clay layer at or near the
surface, and shallow soils over nearly impervious material. They have a very slow rate of
water transmission.
Dual hydrologic groups, A/D, B/D, and C/D, are given for certain wet soils that can be
adequately drained. The first letter applies to the drained condition, the second to the
undrained. Only soils that are rated D in their natural condition are assigned to dual
classes.
Type of feature:
Polygon
Purpose:
This file might be used in planning watershed-protection and flood-prevention projects.
Hydrologic groups are used in equations that estimate runoff from rainfall needed for
solving hydrologic problems (NRCS web site, see end note I). The purpose of this data
is to display the percentages of different types of hydrologic soil types present in each
sub basin of the Pennypack Watershed area.
Original Data Source:
Sources: Natural Resources Conservation Services (NRCS) and Delaware River Basin
Commission (DRBC)
Year of Publication: Unknown
Data acquired by CSC in 2005.
Actions Taken for Data Processing:
Projection information: State Plane NAD 1983 (Feet) Pennsylvania South
Soil data at the scale of 1:24,000 for Montgomery, Bucks, and Philadelphia Counties
were collected from Soil Survey Geographic (SSURGO) Database of NRCS. Their other
lower resolution dataset is called STATSGO, which is available at 1:250,000 scale and
was not used. Other soil data from the Philadelphia Water Department (PWD) was
available at a much smaller resolution and was not used.
The following steps were taken by the Department of Civil and Environmental
Engineering at Temple University, PA.
Soil data (in GIS shape file format) for each county was clipped to the region belonging
to the watershed. Information for soil groups A, B, C, and D was added to the clipped
files. This information was originally available as database files and was imported in GIS
111

as dbf files. Some soil types lacked a HYDGRP (hydrologic group) value or had multiple
HYDGRP values were edited. The attribute MUSYM (Map Unit Symbol) was used to join
the soil type shape files. MUSYM is a soil code that changes from county to county.
Hence a single dbf file could not be used for all three counties. All three clipped shape
files were then appended or merged to create the final soil shape file.
Description of Fields of Attribute Table:
No of Fields (not including FID and Shape): 3
Field Name Description
MUSYM Stands for Map Unit Symbol, which is an alphanumeric code.
MUSYM is a soil code that changes from county to county.
COMPNAME A single Map Unit may contain up to three different soil
components or soil types which are too small or intermixed to
represent graphically on a map. COMPNAME contains the name
of the soil component for each Map Unit.
HYDGRP Hydrologic soil types (A= Sandy, free draining soil, D = clayey,
poorly drained soil, B and C are intermediate soil groups).
No of Records: 1544
Additional Information:
In order to get the contact information for the original metadata or any other relevant
information from NRCS, please visit their web site at http://www.nrcs.usda.gov/ or NRCS
Soils web site at http://soils.usda.gov/.
Shape File Name: CAN2001 (Canopy Coverage)
Contact Information:
Name: Kurt Paulsen
Organization: Center for Sustainable Communities, Temple University, PA
Email: kurt.paulsen@temple.edu
Date: August 18, 2005
Description:
Estimates of tree canopy density for each 30-meter pixel, based on satellite imagery.
Purpose:
Land cover and land use maps designate areas as “forested” but do not estimate canopy
density. Additionally, tree canopy coverage may occur in pixels not classified as
“forested” in land cover or land use classifications. Tree canopy cover data is useful in a
number of ecological and hydrological models.
Original Data Source:
National Land Cover Database Zone 60 Tree Canopy Layer. A product of the United
States Geological Survey (USGS). Data were extracted from http://seamless.usgs.gov
web server. Data server allows user to identify geographic coordinates for downloading
files. Data were extracted based on Pennypack Creek watershed boundaries.
Original Citation Details:
112

References: Homer, C., C. Huang, L. Yang, B. Wylie and M. Coan, Development of a
2001 national land cover database for the United States. Photogrammetric Engineering
and Remote Sensing (in press).
Huang, C., L. Yang, B. Wylie, and C. Homer, 2001. A strategy for estimating tree
canopy density using Landsat 7 ETM+ and high resolution images over large areas. In:
Third International Conference on Geospatial Information in Agriculture and Forestry;
November 5-7, 2001; Denver, Colorado. CD-ROM, 1 disk.
The National Land Cover Database 2001 land cover layer for mapping zone 60 was
produced through a cooperative project conducted by the Multi-Resolution Land
Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of federal
agencies (www.mrlc.gov) that consist of the U.S. Geological Survey (USGS), the
National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental
Protection Agency (EPA), the U.S. Department of Agriculture (USDA), the Forest
Service (USFS), the National Park Service (NPS), the U.S. Fish and Wildlife Service
(FWS), the Bureau of Land Management (BLM) and the USDA Natural Resources
Conservation Service (NRCS). One of the primary goals of the project is to generate a
current, consistent, seamless and accurate National Land Cover Database (NLCD) circa
2001 for the United States at medium spatial resolution. For a detailed definition and
discussion on MRLC and the NLCD 2001 products, refer to Homer et al. (2003) and
<http://www.mrlc.gov/mrlc2k.asp>.
Actions Taken for Data Processing:
Data were originally projected in Albers Conical Equal Area (NAD83) and subsequently
reprojected into Pennsylvania State Plane Feet South (NAD83). Data were then clipped
to the boundary of the Pennypack Creek watershed using Hawth’s Tool: Clip Raster by
Polygon.
Description of Fields of Attribute Table:
No. of Fields (not including FID and Shape): 2
Field
Name Description
Value Percent of pixel canopy density, 2001
Field created by Hawth’s tools in clipping process. Count is number of
pixels within clip area with same value. Count
No. of Records: 95
Additional Information:
Detailed accuracy assessment of the tree-canopy density estimation algorithm is
contained in: Homer, C., C. Huang, L. Yang, B. Wylie and M. Coan, Development of a
2001 national land cover database for the United States. Photogrammetric Engineering
and Remote Sensing (in press).
Database Name: FVC2000 (Fractional Vegetative Coverage, 2000)
Contact Information:
Name: Kurt Paulsen
Organization: Temple University, Center for Sustainable Communities
113

Email: kurt.paulsen@temple.edu
Date: 08-16-2005
Description:
Estimate of the percentage of a pixel covered by vegetation. Vegetative coverage was
estimated based on satellite imagery.
Purpose:
Vegetation serves many important ecological functions related to species habitat and
water quality. Fractional vegetation data is a biophysical variable that describes the
percent of vegetation covering the area of a raster cell. Fractional vegetation is used as
input to hydrologic, meteorologic and plant growth models. Hydrologically, plant cover
reduces the amount and velocity of rainfall hitting the surface, thus reducing erosional
forces. Plant cover also intercepts sun light reducing thermal emission from the soil
surface.
Original Data Source:
Downloaded from Pennsylvania Spatial Data Archive (www.pasda.psu.edu). Data
created by Dr. Toby Carlson, Pennsylvania State University Department of Meteorology.
Title: Fractional Vegetation Cover for Southeast Pennsylvania, 2000
Full Metadata online at:
http://www.pasda.psu.edu/documents.cgi/isa_pa/pa2000fvca_se.xml
Actions Taken for Data Processing:
Data were originally projected in Albers Conical Equal Area (NAD27) and reprojected
into Pennsylvania State Plane Feet South (NAD83). Data were then clipped to the
boundary of the Pennypack Creek watershed using Hawth’s Tool: Clip Raster by
Polygon.
Description of Fields of Attribute Table:
No. of Fields (not including FID and Shape): 2
Field
Name Description
Value Percent of pixel covered by vegetative growth, 2000
Field created by Hawth’s tools in clipping process. Count is number of
pixels within clip area with same value. Count
No. of Records: 57
Additional Information:
Original estimates of pixel vegetative coverage by Dr. Toby Carlson were based on the
NDVI (Normalized Difference Vegetation Index) method. Fractional vegetative
coverage, the percent of a pixel covered by vegetation (where zero is bare soil and one
is dense vegetation) is the NDVI squared.
Database Name: IMPERV2000 (Impervious Cover, 2000)
Contact Information:
Name: Kurt Paulsen
114

Organization: Temple University, Center for Sustainable Communities
Email: kurt.paulsen@temple.edu
Date: 08-18-2005
Description:
Estimate of the percent of a pixel covered by impervious surfaces. Impervious surfaces
were estimated based on satellite imagery.
Purpose:
“Impervious cover is an important indicator of watershed health… [and] is a critically
important variable in most hydrologic and water quality models used to analyze urban
watersheds.” (Center for Watershed Protection: Impervious Cover and Land Use in the
Chesapeake Bay Watershed. January 2001, p. iii)
Original Data Source:
Downloaded from Pennsylvania Spatial Data Archive (www.pasda.psu.edu). Data
created by Dr. Toby Carlson, Pennsylvania State University Department of Meteorology.
Title: Impervious surface area for Southeast Pennsylvania, 2000
Full metadata online at:
http://www.pasda.psu.edu/documents.cgi/isa_pa/pa2000isaa_se.xml
Actions Taken for Data Processing:
Data were originally projected in Albers Conical Equal Area (NAD27) and reprojected
into Pennsylvania State Plane Feet South (NAD83). Data were then clipped to the
boundary of the Pennypack Creek watershed using Hawth’s Tool: Clip Raster by
Polygon.
Description of Fields of Attribute Table:
No. of Fields (not including FID and Shape): 2
Field Name Description
Value Percent of pixel covered by impervious surface, 2000
Field created by Hawth’s tools in clipping process. Count is
number of pixels within clip area with same value. Count
No of Records: 58
Additional Information:
Accuracy of original satellite imagery classification was verified visually using high-
resolution digital orthophotography available from Delaware Valley Regional Planning
Commission. A comparison of the Impervious Surface layer available from the USGS
National Land Cover Database and the Impervious Surface coverage from Dr. Toby
Carlson at Penn State with the digital orthophotography revealed that the Penn State
data was of superior quality and higher resolution, and hence was used in this analysis.
Shape File Name: LU1990.SHP (Land Use, 1990)
Contact Information:
Name: Kurt Paulsen
Organization: Temple University, Center for Sustainable Communities
Email: kurt.paulsen@temple.edu
115

Date: 08-18-2005
Description:
Digital land use layer for Pennypack Creek watershed in 1990. Interpretation of land use
from aerial photography by Delaware Valley Regional Planning Commission.
Purpose:
To document and describe land use patterns and land use change in the Pennypack
Creek Watershed.
Original Data Source:
DVRPC Land Use Digital Data for 1990
Actions Taken for Data Processing:
Data were originally projected in UTM Zone 18N (NAD83) and subsequently reprojected
into Pennsylvania State Plane Feet South (NAD83). Data were clipped to Pennypack
Creek watershed boundary.
Description of Fields of Attribute Table:
No. of Fields (not including FID and Shape): 2
Field Name Description
LU_CODE Numeric code representing land use.
DESCRIPTIO Land use description
No. of Records: 1968
Additional Information:
Data was collected from aerials flown in Spring, 1990. Data digitized from non-
orthocorrected photography at a scale of 1 inch = 400 feet.
Shape File Name: LU1995.SHP (Land Use, 1995)
Contact Information:
Name: Kurt Paulsen
Organization: Temple University, Center for Sustainable Communities
Email: kurt.paulsen@temple.edu
Date: 08-18-2005
Description:
Digital land use layer for Pennypack Creek watershed in 1995. Interpretation of land use
from aerial photography by Delaware Valley Regional Planning Commission.
Purpose:
Document and describe land use patterns and land use change in the Pennypack Creek
Watershed.
Original Data Source:
DVRPC Land Use Digital Data for 1995
Actions Taken for Data Processing:
116

Data were originally projected in UTM Zone 18N (NAD83) and subsequently reprojected
into Pennsylvania State Plane Feet South (NAD83). Data were clipped to Pennypack
Creek watershed boundary.
Description of Fields of Attribute Table:
No. of Fields (not including FID and Shape): 2
Field Name Description
LU_CODE Numeric code representing land use.
CATEGORY Land use description.
No. of Records: 2361
Additional Information:
Data was collected from aerials flown in Spring, 1995. Data digitized from non-
orthocorrected photography at a scale of 1 inch = 400 feet.
Shape File Name: LU2000.SHP (Land Use, 2000)
Contact Information:
Name: Kurt Paulsen
Organization: Temple University, Center for Sustainable Communities
Email: kurt.paulsen@temple.edu
Date: 08-18-2005
Description:
Digital land use layer for Pennypack Creek watershed in 2000. Interpretation of land use
from high-resolution digital orthophotography by Delaware Valley Regional Planning
Commission.
Purpose:
Document and describe land use patterns and land use change in the Pennypack Creek
Watershed.
Original Data Source:
DVRPC Land Use Digital Data for 1990
Actions Taken for Data Processing:
Data were originally projected in UTM Zone 18 (NAD83) and subsequently reprojected
into Pennsylvania State Plane Feet South (NAD83). Data were clipped to Pennypack
Creek watershed boundary.
Description of Fields of Attribute Table:
No. of Fields (not including FID and Shape): 4
Field Name Description
LU_CODE Numeric code representing land use
DESCRIPTIO Land use description
No. of Records: 2915
117

Additional Information:
Data was collected from aerials flown in Spring, 2000. Data digitized from digital
orthophotography. The imagery has a pixel resolution of 1.5 square feet, a positional
accuracy of +/- 5 feet and is designed for use at a scale of 1 inch = 200 feet.
Shape File Name: FVC1985 (Fractional Vegetative Coverage, 1985)
Contact Information:
Name: Kurt Paulsen
Organization: Center for Sustainable Communities, Temple University, PA
Email: kurt.paulsen@temple.edu
Date: August 18, 2005
Description:
Estimate of the percentage of a pixel covered by vegetation. Vegetative coverage was
estimated based on satellite imagery.
Purpose:
Vegetation serves many important ecological functions related to species habitat and
water quality. Fractional vegetation data is a biophysical variable that describes the
percent of vegetation covering the area of a raster cell. Fractional vegetation is used as
input to hydrologic, meteorologic and plant growth models. Hydrologically, plant cover
reduces the amount and velocity of rainfall hitting the surface, thus reducing erosional
forces. Plant cover also intercepts sun light reducing thermal emission from the soil
surface.
Original Data Source:
Downloaded from Pennsylvania Spatial Data Archive (www.pasda.psu.edu). Data
created by Dr. Toby Carlson, Pennsylvania State University Department of Meteorology.
Title: Fractional Vegetation Cover for Southeast Pennsylvania, 1985
Full Metadata online at:
http://www.pasda.psu.edu/documents.cgi/isa_pa/pa1985fvca_se.xml
Actions Taken for Data Processing:
Data were originally projected in Albers Conical Equal Area (NAD27) and reprojected
into Pennsylvania State Plane Feet South (NAD83). Data were then clipped to the
boundary of the Pennypack Creek watershed using Hawth’s Tool: Clip Raster by
Polygon.
Description of Fields of Attribute Table:
No. of Fields (not including FID and Shape): 2
Field Name Description
Value Percent of pixel covered by vegetative growth, 1985
Field created by Hawth’s tools in clipping process. Count is
number of pixels within clip area with same value. Count
No. of Records: 99
Additional Information:
118

Original estimates of pixel vegetative coverage by Dr. Toby Carlson were based on the
NDVI (Normalized Difference Vegetation Index) method. Fractional vegetative
coverage, the percent of a pixel covered by vegetation (where zero is bare soil and one
is dense vegetation) is the NDVI squared.
Database Name: IMPERV1985 (Impervious Cover, 1985)
Contact Information:
Name: Kurt Paulsen
Organization: Temple University, Center for Sustainable Communities
Email: kurt.paulsen@temple.edu
Date: 08-18-2005
Description:
Estimate of the percent of a pixel covered by impervious surfaces. Impervious surfaces
were estimated based on satellite imagery.
Purpose:
“Impervious cover is an important indicator of watershed health… [and] is a critically
important variable in most hydrologic and water quality models used to analyze urban
watersheds.” (Center for Watershed Protection: Impervious Cover and Land Use in the
Chesapeake Bay Watershed. January 2001, p. iii)
Original Data Source:
Downloaded from Pennsylvania Spatial Data Archive (www.pasda.psu.edu). Data
created by Dr. Toby Carlson, Pennsylvania State University Department of Meteorology.
Title: Impervious surface area for Southeast Pennsylvania, 1985
Full metadata online at:
http://www.pasda.psu.edu/documents.cgi/isa_pa/pa1985isaa_se.xml
Actions Taken for Data Processing:
Data were originally projected in Albers Conical Equal Area (NAD27) and reprojected
into Pennsylvania State Plane Feet South (NAD83). Data were then clipped to the
boundary of the Pennypack Creek watershed using Hawth’s Tool: Clip Raster by
Polygon.
Description of Fields of Attribute Table:
No. of Fields (not including FID and Shape): 2
Field Name Description
Value Percent of pixel covered by impervious surface, 1985
Field created by Hawth’s tools in clipping process. Count is
number of pixels within clip area with same value. Count
No of Records: 97
Additional Information:
Accuracy of original satellite imagery classification was verified visually using high-
resolution digital orthophotography available from Delaware Valley Regional Planning
Commission. A comparison of the Impervious Surface layer available from the USGS
119

National Land Cover Database and the Impervious Surface coverage from Dr. Toby
Carlson at Penn State with the digital orthophotography revealed that the Penn State
data was of superior quality and higher resolution, and hence was used in this analysis.
Shape File Name: LC2001 (Land Cover, 2001)
Contact Information:
Name: Kurt Paulsen
Organization: Temple University, Center for Sustainable Communities
Email: kurt.paulsen@temple.edu
Date: 08-18-2005
Description:
Classification of land cover for each 30-meter pixel, based on satellite imagery.
Purpose:
Description of land cover characteristics.
Original Data Source:
National Land Cover Database Zone 60 Land Cover Layer. A product of the United
States Geological Survey (USGS). Data were extracted from http://seamless.usgs.gov
web server. Data server allows user to identify geographic coordinates for downloading
files. Data were extracted based on Pennypack Creek watershed boundaries.
Original Citation Details:
References: Homer, C., C. Huang, L. Yang, B. Wylie and M. Coan, Development of a
2001 national land cover database for the United States. Photogrammetric Engineering
and Remote Sensing (in press).
The National Land Cover Database 2001 land cover layer for mapping zone 60 was
produced through a cooperative project conducted by the Multi-Resolution Land
Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of federal
agencies (www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National
Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection
Agency (EPA), the U.S. Department of Agriculture (USDA) Forest Service (USFS), the
National Park Service (NPS), the U.S. Fish and Wildlife Service (FWS), the Bureau of
Land Management (BLM) and the USDA Natural Resources Conservation Service
(NRCS). One of the primary goals of the project is to generate a current, consistent,
seamless, and accurate National Land cover Database (NLCD) circa 2001 for the United
States at medium spatial resolution. For a detailed definition and discussion on MRLC
and the NLCD 2001 products, refer to Homer et al. (2003) and
<http://www.mrlc.gov/mrlc2k.asp>.
Actions Taken for Data Processing:
Data were originally projected in Albers Conical Equal Area (NAD83) and subsequently
reprojected into Pennsylvania State Plane Feet South (NAD83). Data were then clipped
to the boundary of the Pennypack Creek watershed using Hawth’s Tool: Clip Raster by
Polygon.
Description of Fields of Attribute Table:
No. of Fields (not including FID and Shape): 2
120

Field Name Description
Value Classification of pixel land cover (see Additional Information)
Field created by Hawth’s tools in clipping process. Count is
number of pixels within clip area with same value. Count
No. of Records: 13
ADDITIONAL INFORMATION:
Land Cover Codes and Explanations, from National Land Cover Database:
11. Open Water – All areas of open water, generally with less than 25% cover of
vegetation or soil.
21. Developed, Open Space - Includes areas with a mixture of some constructed
materials, but mostly vegetation in the form of lawn grasses. Impervious surfaces
account for less than 20 percent of total cover. These areas most commonly include
large-lot single-family housing units, parks, golf courses, and vegetation planted in
developed settings for recreation, erosion control, or aesthetic purposes.
22. Developed, Low Intensity - Includes areas with a mixture of constructed materials
and vegetation. Impervious surfaces account for 20-49 percent of total cover. These
areas most commonly include single-family housing units.
23. Developed, Medium Intensity - Includes areas with a mixture of constructed
materials and vegetation. Impervious surfaces account for 50-79 percent of the total
cover. These areas most commonly include single-family housing units.
24. Developed, High Intensity - Includes highly developed areas where people reside
or work in high numbers. Examples include apartment complexes, row houses and
commercial/industrial. Impervious surfaces account for 80 to100 percent of the total
cover.
31. Barren Land (Rock/Sand/Clay) - Barren areas of bedrock, desert pavement,
scarps, talus, slides, volcanic material, glacial debris, sand dunes, strip mines, gravel
pits and other accumulations of earthen material. Generally, vegetation accounts for less
than 15% of total cover.
41. Deciduous Forest - Areas dominated by trees generally greater than 5 meters
tall, and greater than 20% of total vegetation cover. More than 75 percent of the tree
species shed foliage simultaneously in response to seasonal change.
42. Evergreen Forest - Areas dominated by trees generally greater than 5 meters
tall, and greater than 20% of total vegetation cover. More than 75 percent of the tree
species maintain their leaves all year. Canopy is never without green foliage.
43. Mixed Forest - Areas dominated by trees generally greater than 5 meters tall,
and greater than 20% of total vegetation cover. Neither deciduous nor evergreen
species are greater than 75 percent of total tree cover.
81. Pasture/Hay - Areas of grasses, legumes, or grass-legume mixtures planted for
livestock grazing or the production of seed or hay crops, typically on a perennial cycle.
Pasture/hay vegetation accounts for greater than 20 percent of total vegetation.
82. Cultivated Crops - Areas used for the production of annual crops, such as corn,
soybeans, vegetables, tobacco, and cotton, and also perennial woody crops such as
orchards and vineyards. Crop vegetation accounts for greater than 20 percent of total
vegetation. This class also includes all land being actively tilled.
90. Woody Wetlands - Areas where forest or scrubland vegetation accounts for
greater than 20 percent of vegetative cover and the soil or substrate is periodically
saturated with or covered with water.
121

95. Emergent Herbaceous Wetlands - Areas where perennial herbaceous vegetation
accounts for greater than 80 percent of vegetative cover and the soil or substrate is
periodically saturated with or covered with water.
Shape File Name: A_EC (Amount of Effluent Concentrations)
Data Analyst:
Name: Jesse Sherry
Organization: Center for Sustainable Communities (CSC), Temple University, PA
Email: jsherry@temple.edu
Date: 08/09/2005
Contact Information:
Name: ASM Abdul Bari / MD Mahbubur R Meenar
Organization: CSC, Temple University, PA
Email: asmbari@temple.edu, meenar@temple.edu
Description:
This data contains concentrations of common and important dissolved chemicals. This
data was collected at 20 different stations by the Philadelphia Water Department during
the summer of 2002. No data is represented by -999.
Type of feature:
Point
Original Data Source:
Source: Philadelphia Water Department (PWD)
Year of Publication: 2003, Data acquired by CSC in 2005.
Actions Taken for Data Processing:
Projection information: State Plane NAD 1983 (Feet) Pennsylvania South
Following the twenty monitoring stations set by PWD, the original data was cleaned up
and all of the categories were arranged by Dr. Peter Petraitis (ppetrait@sas.upenn.edu)
of the University of Pennsylvania.
Description of Fields of Attribute Table:
No of Fields (not including FID and Shape): 10
Field Name Description
ALKAL Alkalinity (mg CaCO3/L)
AMMON Ammonia (mg/L)
DIS_02 Dissolved O2 (mg/L)
E_COLI E. coli (colony forming units per 100mL)
FEC_COL Fecal Coliform (colony forming units per 100mL)
NITRATE Nitrate (mg/L)
NITRITE Nitrite (mg/L)
ORTHOPHO Orthophosphate (mg/L)
TOT_PHOS Total Phosphorus (mg/L)
CHLOR_A Chlorophyll A (mg/L)
No of Records: 20
122

Shape File Name: A_TGWW (Groundwater Withdrawals)
Data Analyst:
Name: Jesse Sherry
Organization: Center for Sustainable Communities (CSC), Temple University, PA
Email: jsherry@temple.edu
Date: 7/21/2005
Contact Information:
Name: ASM Abdul Bari / MD Mahbubur R Meenar
Organization: CSC, Temple University, PA
Email: asmbari@temple.edu, meenar@temple.edu
Description:
Groundwater Withdrawals are a major source of drinking water for the Pennypack
watershed. This data provides the amount of groundwater withdrawn at each major well
in the watershed.
Type of feature:
Point
Purpose:
The purpose of this data is to gain a better picture of the water balance in each of the
sub basins.
Original Data Source:
Source: Delaware River Basin Commission (DRBC)
Year of Publication: 1996
Data acquired by CSC in 2005.
Actions Taken for Data Processing:
Projection information: State Plane NAD 1983 (Feet) Pennsylvania South
The original shape file was clipped according to Pennypack Watershed boundary.
Description of Fields of Attribute Table:
No of Fields (not including FID and Shape): 6
Field Name Description
NAME The owner and designation of the well
ZIP The zipcode the well is located in
DAYS_OPER The number of days per year the well is operational
HRS_OPER The number of hours per day the well is operational
MGYRTOTAL The total (in millions of gallons) that the well withdraws in a year
AVERAGEMGD The average withdrawal in millions of gallons per day
No of Records: 99
Shape File Name: BRIDGES & CULVERTS
Data Analyst:
Name: ASM ABDUL BARI
123

Organization: Center for Sustainable Communities, Temple University, PA
Email: asmbari@temple.edu
Date: July 15, 2005
Contact Information:
Name: ASM Abdul Bari / MD Mahbubur R Meenar
Organization: Center for Sustainable Communities (CSC), Temple University, PA
Email: asmbari@temple.edu, meenar@temple.edu
Date: 7/22/2005
Description:
A bridge is a structure built to span a gorge, valley, road, railroad track, river or any other
physical obstacle. A culvert is a closed conduit built to convey surface drainage water
under a roadway or other impediment.
Purpose:
The purpose of this shape file is to determine the number of culverts and bridges in each
sub basin. Points were generated at the intersections of road and stream centerlines.
Original Data Source:
Data originated at the Center for Sustainable Communities.
Aerial images (2000) street centerline file source: DVRPC
Stream centerline file source: CSC
Actions Taken for Data Processing:
Input GIS shape files were street centerline and stream centerline of Pennypack
Watershed boundary. Hawths Toolbar was used to generate the intersection points of
stream centerlines and street centerlines. The tool’s name is Intersect Lines (Make
Points). Once the points were generated, random quality checking was done with
reference to DVRPC 2000 aerial images. No field verification could be made because of
time constraint. The other tool used from Hawths Toolbar was Count Points in Polygons
in order to get the number of bridges and culverts in each sub basin.
Description of Attribute Table Fields:
No of Fields (not including FID and Shape): 3
Field Name Description
POINT_X Location of X coordinate of Bridge/ culvert
POINT_Y Location of Y coordinate of Bridge/ culvert
BASIN_ID Sub basin ID number where the bridge/ culvert is located
No of Records: 339
Shape File Name: DAM
Contact Information:
Name: ASM Abdul Bari / MD Mahbubur R Meenar
Organization: Center for Sustainable Communities (CSC), Temple University, PA
Email: asmbari@temple.edu, meenar@temple.edu
Date: 7/22/2005
Description:
124

This shape file displays the dams located in Pennypack Watershed Area. A dam is a
structure for impounding and storing available water as a reservoir for further use. The
dams indicated here are line features, with two points on either side of the waterway
each dam impedes.
Type of feature:
Line
Purpose:
The purpose of this shape file is to show the number of dams within each sub basin.
Original Data Source:
Philadelphia Water Department (PWD).
Data creation year: 1999
Data acquired by Center for Sustainable Communities in 2002.
Actions Taken for Data Processing:
Projection information: State Plane NAD 1983 (Feet) Pennsylvania South
Shape file was clipped by Pennypack Watershed Area.
Some of the original fields were deleted because of unavailability of metadata.
Description of Fields of Attribute Table:
No of Fields (not including FID and Shape): 5
Field Name Description
LENGTH Length of the dam
GPSDATE Date of data collection using GPS device
GPSTIME Time when field data were collected using GPS device
MATERIAL Construction material of the dam
CONDITION Condition of dam according to field survey
No of Records: 9
Note: Some field information for one record was not available
Shape File Name: MPD (Maximum Permitted Discharge)
Data Analyst:
Name: Jesse Sherry
Organization: Center for Sustainable Communities (CSC), Temple University, PA
Email: jsherry@temple.edu
Date: 7/21/2005
Contact Information:
Name: ASM Abdul Bari / MD Mahbubur R Meenar
Organization: CSC, Temple University, PA
Email: asmbari@temple.edu, meenar@temple.edu
Description:
Discharges into streams are a major source of water pollution, but are also the most
regulated type of discharge. Non-point sources are largely unregulated and so often
125

have a powerful impact on water quality. This data provides water discharge amounts
for each point source in the watershed.
Type of feature:
Point
Original Data Source:
Source: Delaware River Basin Commission (DRBC)
Year of Publication: 1996
Data acquired by CSC in 2005.
Actions Taken for Data Processing:
Projection information: State Plane NAD 1983 (Feet) Pennsylvania South
The original shape file was clipped according to Pennypack Watershed boundary.
Description of Fields of Attribute Table:
No of Fields (not including FID and Shape): 3
Field Name Description
FAC_NAME The owner of the discharge point
MGAL_YR The amount of water discharged at this point in millions of gallons
per year
MGAL_DAY The amount of water discharged at this point in millions of gallons
per day
No of Records: 18
Shape File Name: RIP_BUF (Riparian Buffer)
Contact Information:
Name: Md Mahbubur R Meenar, GIS Coordinator
Organization: Center for Sustainable Communities (CSC), Temple University, PA
Email: meenar@temple.edu
Date: 07/22/05
Description:
A Riparian Buffer is a zone of protection made up of trees and other vegetation that grow
along the banks of a waterway. Riparian Buffers help keep a stream healthy by reducing
stream bank erosion and acting as a natural soil filterviii.
The Philadelphia Water Department (PWD) classified the forest buffer according to a fifty
foot standard, and digitized sections of the stream bank lacking a forest buffer using
aerial photography taken in 2000 and provided by the Delaware Valley Regional
Planning Commission. The term “Lacking Forest Buffer” is defined as a stream bank
with less than fifty foot wide layer of forest cover and less than 50% canopy closure.
Where the stream bank appeared to be lacking a forest buffer on both sides, the section
was classified as such. Otherwise, each side of the creek was treated separately.
Larger pond or lake areas that result from the damming of the main stem creek or major
tributary were assessed; small water bodies, such as man-made farm ponds, were not.
126

Type of feature:
Line
Purpose:
The purpose of this data is to identify stream banks within Pennypack Watershed Area
lacking riparian forest buffers.
Original Data Source:
Source: Heritage Conservancy
Year of Publication: 2002 (Data created from 2000 aerial photography. Field checks
performed in 2002)
Data acquired by CSC in 2003.
Actions Taken for Data Processing:
Projection information: State Plane NAD 1983 (Feet) Pennsylvania South
The original shape file was clipped according to Pennypack Watershed boundary.
Description of Fields of Attribute Table:
No of Fields (not including FID and Shape): 2
Field Name Description
TYPE This field informs whether the stream bank is lacking Riparian Buffer on
one side or both sides. Each line feature is represented by either “one”
or “both”
LENG_FT Length of the line feature
No of Records: 302
Additional Information:
Heritage Conservancy has published the original Riparian Buffer Status shape file for
Southeastern Pennsylvania region. Contact information for Heritage Conservancy:
Heritage Conservancy
85 Old Dublin Pike, Doylestown, Pa 18901
Ph: 215-345-7020, Fax: 215-345-4328
www.heritageconservancy.org
Shape File Name: WETLAND
Author Information:
Name: ASM ABDUL BARI
Organization: Center for Sustainable Communities, Temple University, PA
Email: asmbari@temple.edu
Date: July 15, 2005
Description:
Wetlands are land areas seasonally or permanently waterlogged by either fresh or salt
water. These include lakes, rivers, estuaries and freshwater marshes. Wetlands are
areas where water saturation is the dominant factor that determines the nature of soil
development and the types of plant and animal communities living in the soil and on its
surface. Most wetlands contain soil or substrate that is at least periodically saturated
with or covered by water. The water creates severe physiological problems for plants
and animals that are not adapted for life in water or in saturated soil.
127

Type of feature:
Polygon
Purpose:
The purpose of this file is to calculate the percentage of wetland area in each sub basin.
Original Data Source:
Delaware Valley Regional Planning Commission (DVRPC)
Data creation year: 1981
Scale: 1:80000 roughly as indicated in the original metadata.
DVRPC converted this data from National Wetlands Inventory (NWI) data by U.S. Fish &
Wildlife Service.
Data acquired by Center for Sustainable Communities in 2002.
Actions Taken for Data Processing:
Projection information: State Plane NAD 1983 (Feet) Pennsylvania South
Shape file was clipped by Pennypack Watershed Area.
Some of the original fields (AREA and PERIMETER) were deleted because those were
in MKS (Meter, kilogram, Second) unit. Instead a new field called AREA_SQM has been
created to store AREA information in FPS (Foot, Pound, Second) unit.
Description of Fields of Attribute Table:
No of Fields (not including FID and Shape): 2
Field Name Description
ATTRIBUTE Wetland codes (34 unique codes in this database)
AREA_SQM Area in square miles
No of Records: 180
Additional Information:
The code explanation was not given with the original data National Wetlands Inventory
Mapping Code Description http://www.nwi.fws.gov/atx/atx.html does not have all the
code listed.
Shape File Name: SSA (Sewer Service Area)
Data Analyst:
Name: Jesse Sherry
Organization: Center for Sustainable Communities (CSC), Temple University, PA
Email: jsherry@temple.edu
Date: 7/21/2005
Contact Information:
Name: ASM Abdul Bari / MD Mahbubur R Meenar, GIS Coordinator
Organization: Center for Sustainable Communities (CSC), Temple University, PA
Email: asmbari@temple.edu, meenar@temple.edu
Date: 07/25/05
Description:
128

These polygons show the sewer service areas for the Pennypack Watershed. Original
file only covers the suburban portion of the watershed. The southern portions, which is
the Philadelphia portion is covered by the Philadelphia Water Dept was later added by
the CSC.
Type of feature:
Polygon
Original Data Source:
Source: Delaware River Basin Commission (DRBC)
Year of Publication: 1996
Data acquired by CSC in 2005.
Actions Taken for Data Processing:
Projection information: State Plane NAD 1983 (Feet) Pennsylvania South
The original shape file was clipped according to Pennypack Watershed boundary.
Philadelphia portion of the service area was added using union function of overlay
analysis tools of ArcToolbox.
Description of Fields of Attribute Table:
No of Fields (not including FID and Shape): 1
Field Name Description
SSA The provider of Sewer Service for this area. NO DATA means there is
no public sewer in this area.
No of Records: 14
Shape File Name: WSA (Water Service Area)
Data Analyst:
Name: Jesse Sherry
Organization: Center for Sustainable Communities (CSC), Temple University, PA
Email: jsherry@temple.edu
Date: 7/21/2005
Contact Information:
Name: Md Mahbubur R Meenar, GIS Coordinator
Organization: Center for Sustainable Communities (CSC), Temple University, PA
Email: meenar@temple.edu
Date: 07/25/05
Description:
These polygons show the water service areas for the Pennypack Watershed. This file
only covers the suburban portion of the watershed. The southern portions, which is the
Philadelphia portion is covered by the Philadelphia Water Dept.
Type of feature:
Polygon
129

Original Data Source:
Source: Delaware River Basin Commission (DRBC)
Year of Publication: 1996
Data acquired by CSC in 2005.
Actions Taken for Data Processing:
Projection information: State Plane NAD 1983 (Feet) Pennsylvania South
The original shape file was clipped according to Pennypack Watershed boundary.
Description of Fields of Attribute Table:
No of Fields (not including FID and Shape): 1
Field Name Description
WSA The provider of Water Service for this area. NO DATA means there
is no public water service in this area.
No of Records: 11
130

Section 3: List of Database
49BASIN (49 Sub Basins of Pennypack Watershed)
Field Name Field Type Description
BASIN_ID Short
Integer Sub Basin ID Created by Temple University
BIO (Biological Data)
FISH
Field Name Field Type Description
F_NO_SP Double Total Number of Fish Species
F_NO_BEN Double Number of benthic insectivorous species
F_NO_WAT Double Number of Water Column Species
F_NO_INT Double Number Of Intolerant/Sensitive Species
F_P_WHSK Double Percent White Sucker
F_P_GEN Double Percent Generalist
F_P_INSE Double Percent Insectivores
F_P_CARN Double Percent Top Carnivores
F_P_DIS Double Percent of individuals with disease and anomalies
F_P_DOM Double Percentage of dominant species
F_DEN Double Density
F_NO_IND Double Number Of Individuals
F_BIOM Double Biomass per square meter
F_MODIND Double Modified Index Of Well-Being
F_SWDI Double Shannon-Weiner Diversity Index (H')
F_NO_CYP Double No. Of Cyprinid Species
F_P_RES Double Percent Resident Species
F_P_EXOT Double Percent Introduced/Exotic Species
F_IBI Double Index of Biological Integrity1
F_BIO_IN Double Percentage representing the Biological Integrity of the
Pennypack based on the Fish populations arrived at by
dividing the IBI score by the max IBI score (50).
F_M_STAT Text PWD Monitoring Stations From which data for this
basin was taken
HABITAT
Field Name Field Type Description
L_BANK Double Bank Stability (Left Bank)
R_BANK Double Bank Stability (Right Bank)
CH_ALT Double Channel Alteration
CH_FLOW Double Channel Flow Status
CH_SIN Double Channel Sinuosity
EMBED Double Embeddedness
EPIF_SUB Double Epifaunal substrate cover
RIF_FREQ Double Frequency of Riffles (or Bends)
POOL_SUB Double Pool Substrate Characterization
POOL_VAR Double Pool Variability
RIP_V_L Double Riparian Vegetative Zone Width (Left Bank)
131

RIP_V_R Double Riparian Vegetative Zone Width (Right Bank)
SED_DEP Double Sediment Deposition
VEG_P_L Double Vegetative Protection (Left Bank)
VEG_P_R Double Vegetative Protection (Right Bank)
VEL_DEPT Double Velocity/Depth Regime
H_M_STAT Text PWD Monitoring Stations from which data for this
basin was taken
MICROINVERTEBRATE
Field Name Field Type Description
M_NO_SP Integer Number of Macroinvertebrate Species
M_HBI Double Hilsenhoff Biotic Index
M_P_DOM Double Percent of the Taxa that is the Dominant Taxa
M_D_TAX Text Dominant Taxa
M_P_FIL Double Percent of Filter/Collector Species
M_P_GATH Double Percent of Gatherer/Collector Species
M_P_SCR Double Percent of Scraper Species
M_P_SHR Double Percent of Shredder Species
M_P_MODT Double Percent of Moderately Tolerant Species
M_P_TOL Double Percent of Tolerant Species
M_P_INTO Double Percent of Intolerant Species
M_BIO_AS Text Biological Assessment of Stream based on the
Macroinvertebrate Population from the PWD Reportix
M_BIO_IN Double Percentage representing the Biological Integrity of the
Pennypack based on the Macroinvertebrate
populations from the PWD Report1
M_M_STAT Text PWD Monitoring Stations From which data for this
basin was taken
DEMOG (Census Block Group Level Demographic Data)
Field Name Field Type Description
POP_1990 Double Sub-basin estimated population, 1990
HU_1990 Double Sub-basin estimated number of housing units, 1990
AHHMI_89 Double Sub-basin average household median income, 1989
POP_2000 Double Sub-basin estimated population, 2000
HU_2000 Double Sub-basin estimated number of housing units, 2000
AHHMI_99 Double Sub-basin average household median income, 1999
PDENS90 Double Sub-basin population density, 1990. Calculated as
persons per square mile of residentially-classified land.
PDENS00 Double Sub-basin population density, 2000. Calculated as
persons per square mile of residentially-classified land.
HUDENS90 Double Sub-basin housing unit density, 1990. Calculated as
housing units per square mile of residentially-classified
land.
HUDENS00 Double Sub-basin housing unit density, 2000. Calculated as
housing units per square mile of residentially-classified
land.
132

FP_REL (Flood Plain Related)
Field Name Field Type Description
P_FP_100 Double Percentage of Land Area within 100-year Floodplain
P_FP_500 Double Percentage of Land Area within 500-year Floodplain
NBLFP100 Short
Integer Number of Buildings in 100 Year Floodplain
NBLFP500 Short
Integer Number of Buildings in 500 Year Floodplain
GEOL (Geology, Baseflow, and Soil)
Field Name Field Type Description
P_GEO_R1 Double Percentage of Rock Type 1
Note: This code is created by CSC. There are 5 types
of Rock formations. Roc Codes are generated as 1 to
5 and the information will be provided in separate
lookup table.
P_GEO_R2 Double Percentage of Rock Type 2
P_GEO_R3 Double Percentage of Rock Type 3
P_GEO_R4 Double Percentage of Rock Type 4
P_GEO_R5 Double Percentage of Rock Type 5
P_SOIL_A Double Percentage of Soil Type 1
Note: This code is created by CSC. There are 98 Soil
Types. Therefore, soil code will be inserted as 1 to 98.
Soil information will be provided in separate lookup
table
P_SOIL_B Double Percentage of Soil Type 2
P_SOIL_C Double Percentage of Soil Type 3
P_SOIL_D Double Percentage of Soil Type 4
A_MBF Double Amount of Median Base Flow in GPD/SQM
Note: GPD= Gallon per day and SQM= Square Mile
LANDF (Land Features)
Field Name Field Type Description
Double Percent of sub-basin covered by impervious surface,
1985 P_IMP85 Double Percent of sub-basin covered by impervious surface,
2000 P_IMP00
FVC_85 Double Sub-basin fractional vegetative coverage, 1985
FVC_00 Double Sub-basin fractional vegetative coverage, 2000
P_CNP01 Double Sub-basin percent canopy density, 2001
P_LC_11 Double Percent of sub-basin land cover in Open Water, 2001
P_LC_21 Double Percent of sub-basin land cover in Developed, Open
Space, 2001
P_LC_22 Double Percent of sub-basin land cover in Developed, Low
Intensity, 2001
P_LC_23 Double Percent of sub-basin land cover in Developed, Medium
Intensity, 2001
133

P_LC_24 Double Percent of sub-basin land cover in Developed, High
Intensity, 2001
P_LC_31 Double Percent of sub-basin land cover in Barren Land, 2001
P_LC_41 Double Percent of sub-basin land cover in Deciduous Forest,
2001
P_LC_42 Double Percent of sub-basin land cover in Evergreen Forest,
2001
P_LC_43 Double Percent of sub-basin land cover in Mixed Forest, 2001
P_LC_81 Double Percent of sub-basin land cover in Pasture/Hay, 2001
P_LC_82 Double Percent of sub-basin land cover in Cultivated Crops,
2001
P_LC_90 Double Percent of sub-basin land cover in Woody Wetlands,
2001
P_LC_95 Double Percent of sub-basin land cover in Emergent
Herbaceous Wetlands, 2001
P_LC_DEV Double Percent of sub-basin land cover “Developed” (sum of
land cover codes 21, 22, 23, and 24)
P_LC_FOR Double Percent of sub-basin land cover “Forested” (sum of
land cover codes 41, 42, 43)
P_LC_AG Double Percent of sub-basin land cover “Agriculture” (sum of
land cover codes 81 and 82)
P_LC_WWL Double Percent of sub-basin land cover “Water or Wetlands”
(Sum of land cover codes 11, 90 and 95)
P_LU00_01 Double Percent of sub-basin land use in residential: single
family detached, 2000
P_LU00_02 Double Percent of sub-basin land use in residential: multi-
family and row-homes, 2000
P_LU00_03 Double Percent of sub-basin land use in manufacturing/light
industrial, 2000
P_LU00_04 Double Percent of sub-basin land use in transportation, 2000
P_LU00_05 Double Percent of sub-basin land use in utility, 2000
P_LU00_06 Double Percent of sub-basin land use in commercial, 2000
P_LU00_07 Double Percent of sub-basin land use in community services,
2000
P_LU00_08 Double Percent of sub-basin land use in military, 2000
P_LU00_09 Double Percent of sub-basin land use in recreation, 2000
P_LU00_10 Double Percent of sub-basin land use in agriculture, 2000
P_LU00_12 Double Percent of sub-basin land use in wooded, 2000
P_LU00_13 Double Percent of sub-basin land use in water, 2000
P_LU00_14 Double Percent of sub-basin land use in vacant, 2000
P_LU00_15 Double Percent of sub-basin land use in non-residential
parking, 2000
P_LU95_01 Double Percent of sub-basin land use in residential: single
family detached, 1995
P_LU95_02 Double Percent of sub-basin land use in residential: multi-
family and row-homes, 1995
P_LU95_03 Double Percent of sub-basin land use in manufacturing/light
industrial, 1995
P_LU95_04 Double Percent of sub-basin land use in transportation, 1995
P_LU95_05 Double Percent of sub-basin land use in utility, 1995
P_LU95_06 Double Percent of sub-basin land use in commercial, 1995
134

P_LU95_07 Double Percent of sub-basin land use in community services,
1995
P_LU95_08 Double Percent of sub-basin land use in military, 1995
P_LU95_09 Double Percent of sub-basin land use in recreation, 1995
P_LU95_10 Double Percent of sub-basin land use in agriculture, 1995
P_LU95_12 Double Percent of sub-basin land use in wooded, 1995
P_LU95_13 Double Percent of sub-basin land use in water, 1995
P_LU95_14 Double Percent of sub-basin land use in vacant, 1995
P_LU95_15 Double Percent of sub-basin land use in non-residential
parking, 1995
P_LU90_01 Double Percent of sub-basin land use in residential: single
family detached, 1990
P_LU90_02 Double Percent of sub-basin land use in residential: multi-
family and row-homes, 1990
P_LU90_03 Double Percent of sub-basin land use in manufacturing/light
industrial, 1990
P_LU90_04 Double Percent of sub-basin land use in transportation, 1990
P_LU90_05 Double Percent of sub-basin land use in utility, 1990
P_LU90_06 Double Percent of sub-basin land use in commercial, 1990
P_LU90_07 Double Percent of sub-basin land use in community services,
1990
P_LU90_08 Double Percent of sub-basin land use in military, 1990
P_LU90_09 Double Percent of sub-basin land use in recreation, 1990
P_LU90_10 Double Percent of sub-basin land use in agriculture, 1990
P_LU90_12 Double Percent of sub-basin land use in wooded, 1990
P_LU90_13 Double Percent of sub-basin land use in water, 1990
P_LU90_14 Double Percent of sub-basin land use in vacant, 1990
P_LU90_15 Double Percent of sub-basin land use in non-residential
parking, 1990
Double Sub-basin Simpson Index of Forest Fragmentation,
1990 SIFF_90 Double Sub-basin Simpson Index of Forest Fragmentation,
2000 SIFF_00 Double Sub-basin mean shape index of forested patched,
1990 MSIF_90 Double Sub-basin mean shape index of forested patched,
2000 MSIF_00 Double Sub-basin average nearest neighbor distance of
forested patches, in feet, 1990 ANNDF_90 Double Sub-basin average nearest neighbor distance of
forested patches, in feet, 2000 ANNDF_00
PSL0_2 Double Percentage of Slope 0-2
PSL2_5 Double Percentage of Slope 2-5
PSL5_15 Double Percentage of Slope 5-15
PSL15_25 Double Percentage of Slope 15-25
PSLG25 Double Percentage of Slope greater than 25
R_DEN_M Double Road Density in Mile per Square Mile
135

WTR_REL (Water Related)
Field Name Field Type Description
P_WL Double Percentage of Wet Land
N_BC Short
Integer Number of Bridges and Culverts
N_DAM Short
Integer Number of Dams
P_RB_1SL Double Percentage of Riparian Buffer Lacking on one side
P_RB_2SL Double Percentage of Riparian Buffer Lacking on both sides
P_RB_2SE Double Percentage of Riparian Buffer Existing on both sides
MPD Double Maximum Permitted Discharge in MGD
Note: MGD= Million Gallon per Day
ALKAL Double Alkalinity (mg CaCO3/L)
AMMON Double Ammonia (mg/L)
DIS_02 Double Dissolved O2 (mg/L)
E_COLI Double E. coli (colony forming units per 100mL)
FEC_COL Double Fecal Coliform (colony forming units per 100mL)
NITRATE Double Nitrate (mg/L)
NITRITE Double Nitrite (mg/L)
ORTHOPHO Double Orthophosphate (mg/L)
TOT_PHOS Double Total Phosphorus (mg/L)
CHLOR_A Double Chlorophyll A (mg/L)
A_TGWW Double Amount of Total Groundwater Withdrawal in MGD
Note: MGD= Million Gallon per Day
CN_AWM Double Area Weighted Mean Curve Number for each Sub
Basin for storm water runoff potential
L_STRM_M Double Length of Stream in Mile
PS_30FR Double Proportion of total Stream Length that has Road within
30 Feet
PS_100FR Double Proportion of total Stream Length that has Road within
100 Feet
SSA (Sewer Service Area)
Field Name Field Type Description
S_ABING Double The percentage of the basin served by the Abington
Township STP.
S_BUCKS Double The percentage of the basin served by the Bucks
County Water and Sewer Authority
S_DELC Double The percentage of the basin served by the Delcora
S_HORSH Double The percentage of the basin served by the Horsham
Township Sewer Authority
S_UP_MOR Double The percentage of the basin served by the Upper
Moreland - Hatboro Joint Authority
S_WARM Double The percentage of the basin served by the Warminster
Township Municipal Authority
S_PWD Double The percentage of the basin served by the
Philadelphia Water Dept.
S_NO_SERV Double The percentage of the basin not served by any sewer
service
136

T_SSA Double The Percentage of the basin served by the Total
Sewer Service Area
WSA (Water Service Area)
Field Name Field Type Description
W_HATBOR Double The percentage of the basin served by the Hatboro
Water Authority
W_HORSHA Double The percentage of the basin served by the Horsham
Township Water Authority
W_NWALES Double The percentage of the basin served by the North
Wales Water Authority
W_PHLSUB Double The percentage of the basin served by the
Philadelphia Suburban Water Company
W_UPPSTH Double The percentage of the basin served by the Upper
Southampton Township Municipal Authority
W_WARMIN Double The percentage of the basin served by the Warminster
Township Municipal Authority
W_WILGRO Double The percentage of the basin served by the Willow
Grove USNAS
W_PWD Double The percentage of the basin served by the
Philadelphia Water Dept.
T_WSA Double The Percentage of the basin served by the Total Water
Service Area
Note: Any missing data, if text then it will be typed as NO DATA and if number then will
be typed as -999.
137

Section 4: DBF File Metadata
Database Name: FISH (Fish Data for Pennypack Watershed)
Data Analyst:
Name: Jesse Sherry
Organization: Center for Sustainable Communities (CSC), Temple University, PA
Email: jsherry@temple.edu
Date: 7/21/2005
Contact Information:
Name: ASM Abdul Bari / MD Mahbubur R Meenar
Organization: CSC, Temple University, PA
Email: asmbari@temple.edu, meenar@temple.edu
Description:
Data describes the Fish species present at various sites and the water quality derived by
analyzing these species.
Original Data Source:
Philadelphia Water Department (PWD)
Data creation year: 2002
This data was originally in point form and the attributes were ascribed to the basins that
drain to these points. Following the twenty monitoring stations set by PWD, the original
data was cleaned up and all of the categories were arranged by Dr. Peter Petraitis
(ppetrait@sas.upenn.edu) of University of Pennsylvania.
Data acquired by CSC in 2004.
Actions Taken for Data Processing:
The data for twenty monitoring stations were transferred to the 49 sub basins by the
following method. The data for each monitoring point was attributed to the basins
upstream from the monitoring station and downstream from any other monitoring point.
Major branches without a monitoring station were not attributed data.
Description of Fields:
No. of Fields: 21
No. of Records: 49
Field Name Description
F_NO_SP Total Number of Fish Species
F_NO_BEN Number of benthic insectivorous species
F_NO_WAT Number of Water Column Species
F_NO_INT Number Of Intolerant/Sensitive Species
F_P_WHSK Percent White Sucker
F_P_GEN Percent Generalist
F_P_INSE Percent Insectivores
F_P_CARN Percent Top Carnivores
F_P_DIS Percent of individuals with disease and anomalies
F_P_DOM Percentage of dominant species
138

F_DEN Density
F_NO_IND Number Of Individuals
F_BIOM Biomass per square meter
F_MODIND Modified Index Of Well-Being
F_SWDI Shannon-Weiner Diversity Index (H')
F_NO_CYP Number of Cyprinid Species
F_P_RES Percent of Resident Species
F_P_EXOT Percent of Introduced/Exotic Species
The Index of Biological Integrity Score (IBI) from the PWD
ReportF_IBI x
F_BIO_IN The IBI Score expressed as a percentage
PWD Monitoring Stations From which data for this basin was
taken F_M_STAT
Database Name: HABITAT (Habitat Data for Pennypack Watershed)
Data Analyst:
Name: Jesse Sherry
Organization: Center for Sustainable Communities (CSC), Temple University, PA
Email: jsherry@temple.edu
Date: 7/21/2005
Contact Information:
Name: ASM Abdul Bari / MD Mahbubur R Meenar
Organization: CSC, Temple University, PA
Email: asmbari@temple.edu, meenar@temple.edu
Description:
Habitat is the physical location or type of environment in which an organism or biological
population lives or occurs. (http://www.biology- online.org/dictionary/habitat). This data
describes the habitat present at various sites along the Pennypack Stream.
Original Data Source:
Philadelphia Water Department (PWD)
Data creation year: 2002
This data was originally in point form and the attributes were ascribe to the basins that
drain to these points. Following the twenty monitoring stations set by PWD, the original
data was cleaned up and all of the categories were arranged by Dr. Peter Petraitis
(ppetrait@sas.upenn.edu) of the University of Pennsylvania.
Data acquired by CSC in 2004.
Actions Taken for Data Processing:
The data for twenty monitoring stations were collected from the 49 sub basins by
attributing data for each monitoring point to the basin immediately upstream from the
monitoring point.
Description of Fields:
No. of Fields: 17
No. of Records: 49
Field Name Description
L_BANK Bank Stability (Left Bank)
139

R_BANK Bank Stability (Right Bank)
CH_ALT Channel Alteration
CH_FLOW Channel Flow Status
CH_SIN Channel Sinuosity
EMBED Embeddedness
EPIF_SUB Epifaunal substrate cover
RIF_FREQ Frequency of Riffles (or Bends)
POOL_SUB Pool Substrate Characterization
POOL_VAR Pool Variability
RIP_V_L Riparian Vegetative Zone Width (Left Bank)
RIP_V_R Riparian Vegetative Zone Width (Right Bank)
SED_DEP Sediment Deposition
VEG_P_L Vegetative Protection (Left Bank)
VEG_P_R Vegetative Protection (Right Bank)
VEL_DEPT Velocity/Depth Regime
PWD Monitoring Stations from which data for
this basin was taken H_M_STAT
Database Name: MACROINVERTEBRATES (Macroinvertebrates Data for
Pennypack Watershed)
Data Analyst:
Name: Jesse Sherry
Organization: Center for Sustainable Communities (CSC), Temple University, PA
Email: jsherry@temple.edu
Date: 7/21/2005
Contact Information:
Name: ASM Abdul Bari / MD Mahbubur R Meenar
Organization: CSC, Temple University, PA
Email: asmbari@temple.edu, meenar@temple.edu
Description:
Data describes the Macroinvertebrates present at various sites and the water quality
derived by analyzing these invertebrates. A Macroinvertebrate is an animal without
a backbone in at least one stage of its life cycle, usually the nymph or larval stage. xi
Benthic macroinvertebrates such as insects, worms, and molluscs are the preferred
group of aquatic organisms monitored in water quality assessment programs (Hellawell
1986) because: (1) they provide an extended temporal perspective (relative to traditional
water samples that are collected periodically) because they have limited mobility and
relatively long life spans (e.g., a few months for some chironomid midges to a year or
more for some insects and molluscs); (2) the group has measurable responses to a wide
variety of environmental changes and stresses; (3) they are an important link in the
aquatic food web, converting plant and microbial matter into animal tissue that is then
available to fish; (4) they are abundant; and (5) their responses can be analyzed
statistically (Weber 1973). Thus, the presence or conspicuous absence of certain
macroinvertebrate species at a site is a meaningful record of environmental conditions
during the recent past, including ephemeral events that might be missed by assessment
programs, which only rely on periodic sampling of water chemistry. Most stream
ecosystems have relatively diverse macroinvertebrate assemblages with species from a
140

number of different orders [e.g., mayflies (Ephemeroptera), and caddisflies (Trichoptera),
stoneflies (Plecoptera), beetles (Coleoptera), true flies (Diptera)]. Likewise, the common
trophic groups (i.e., herbivores, detritivores, and predators) are represented by a number
of different species. Various abiotic factors (e.g., hydrology, substrate, temperature,
oxygen, and pH) and biotic factors (e.g., food quality and quantity, interactions with
competitors or predators) have molded, through natural selection, a unique set of
optimum environmental requirements for each species. These environmental
requirements contribute significantly to the distribution and abundance of these
organisms within and among natural stream ecosystems and influence their response to
environmental perturbation. xii
Original Data Source:
Philadelphia Water Department (PWD)
Data creation year: 2002
This data was originally in point form and the attributes were attributed to the basins that
drain to these points. Following to the twenty monitoring stations, as set by PWD, the
original data was cleaned up and all of the categories were arranged by Dr. Peter
Petraitis (ppetrait@sas.upenn.edu) of Philadelphia University, PA.
Data acquired by CSC in 2004.
Actions Taken for Data Processing:
The data for twenty monitoring stations were collected from the 49 sub basins by
attributing data for each monitoring point to the basin immediately upstream from the
monitoring point.
Description of Fields of Attribute Table:
No. of Fields: 14
No. of Records: 49
Field Name Description
M_NO_SP Number of Species
M_HBI Hilsenhoff Biotic Index
M_P_DOM Percent of the Taxa that is the Dominant Taxa
M_D_TAX Dominant Taxa
M_P_FIL Percent of Filter/Collector Species
M_P_GATH Percent of Gatherer/Collector Species
M_P_SCR Percent of Scraper Species
M_P_SHR Percent of Shredder Species
M_P_MODT Percent of Moderately Tolerant Species
M_P_TOL Percent of Tolerant Species
M_P_INTO Percent of Intolerant Species
M_BIO_AS Biological Assessment of Stream based on the Macroinvertebrate
Population from the PWD Report1
M_BIO_IN Percentage representing the Biological Integrity of the Pennypack
from the PWD Report1
M_M_STAT PWD Monitoring Stations From which data for this basin was taken
Database Name: DEMOGRAPHY
Contact Information:
Name: Kurt Paulsen, Ph.D.
Organization: Center for Sustainable Communities, Temple University, PA
141

Email: kurt.paulsen@temple.edu
Date: 08-18-2005
Description:
Estimates of sub-basin population counts, housing units, median household income,
population density and housing unit density
Purpose:
Describe human and population influences on watershed.
Original Data Source:
Data are from the United States Census Bureau. Shape files are from Census
TIGER/Line Cartographic Boundary files, Census Block Groups for both 1990 and 2000.
Census Data (SF3) for 2000 were downloaded from http://factfinder.census.gov.
Census Data for 1990 (STF3) were accessed from CD-Roms.
Actions Taken for Data Processing:
Shape files: Data for both 1990 and 2000 are originally in unprojected Geographic
(lat/lon) format. Shape files were reprojected into Pennsylvania State Plane Feet South
(NAD83). Census Block Groups for Bucks, Montgomery and Philadelphia Counties
were initially produced.
Block groups which had any portion of their area within the Pennypack Creek watershed
were selected. For population and housing unit estimates, the percent of a Census
Block Group within the Pennypack watershed and/or within subbasins were used to
adjust figures. For example, if a Census Block Group has 10 percent of its area within
the watershed, then 10 percent of its housing units and population are assumed to be
located within the watershed. Similarly, if 10 percent of a block group is located in one
sub-basin, then 10 percent of its population and housing figures were assigned to that
sub-basin.
Data files: Data for both 1990 and 2000 were from the Census SF3 (Summary File 3).
The following tables/variables were collected:
1990: P001001: Total Persons
P080A001: Median household income in 1989
H0010001: Total Housing Units
2000: P001001: Total Persons
P053001: Median household income in 1999
H001001: Total Housing Units
Description of Fields:
No. of Fields (not including FID and Shape): 10
Field Name Description
POP_1990 Sub-basin estimated population, 1990
HU_1990 Sub-basin estimated number of housing units, 1990
AHHMI_89 Sub-basin average household median income, 1989
POP_2000 Sub-basin estimated population, 2000
HU_2000 Sub-basin estimated number of housing units, 2000
142

AHHMI_99 Sub-basin average household median income, 1999
PDENS90 Sub-basin population density, 1990. Calculated as persons per
square mile of residentially-classified land.
PDENS00 Sub-basin population density, 2000. Calculated as persons per
square mile of residentially-classified land.
HUDENS90 Sub-basin housing unit density, 1990. Calculated as housing
units per square mile of residentially-classified land.
HUDENS00 Sub-basin housing unit density, 2000. Calculated as housing
units per square mile of residentially-classified land.
No. of Records: 49
Additional Information:
Census Bureau estimates of median-household income use 1-year prior to each
decennial census because the question asks respondents to report household income
for the previous year. Note: data for income are in nominal (non-inflation adjusted)
dollars.
Database Name: FP_REL (Floodplain Related)
Contact Information:
Name: ASM Abdul Bari / MD Mahbubur R Meenar
Organization: Center for Sustainable Communities, Temple University, PA
Email: asmbari@temple.edu, meenar@temple.edu
Date: 08/02/2005
GENERAL INFORMATION:
No of fields: 4 (Excluding BASIN_ID3)
No of Records: 49
CATEGORY: FLOODPLAIN
Description:
The floodplain is an area of land that is normally dry but that will be under water during a
flood. A 100-year flood is a flood with a 1% chance of happening within any given year.
A 500-year flood is a flood with a 0.2% chance of happening within any given year. The
Q3 Flood Data are derived from the Flood Insurance Rate Maps (FIRMS) published by
the Federal Emergency Management Agency (FEMA). The file is in geographic
projection and decimal degree coordinate system with a scale of 1:24000.
Purpose:
The purpose of this dataset is to calculate the percentages of areas inside 100 year and
500 year floodplains in each sub basin of Pennypack Creek Watershed Area.
Original Data Source:
Sources: FEMA
Year of Publication: 1996
Data acquired by CSC in 2003.
3 BASIN_ID is the key field that ties each record with 49 sub basin boundaries generated by CSC.
143

Actions Taken for Data Processing:
FEMA 100 year and 500 year floodplain data were converted in GIS shape file format.
Data were clipped by Pennypack Watershed boundary. Percentages of areas inside 100
year and 500 year floodplains in each sub basin was calculated using Polygon in
Polygon Analysis tool of Hawth’s Analysis Tool for ArcGIS.
Description of Fields:
Field Name Description
P_FP_100 Percentage of area of 100 year floodplain in each sub basin
P_FP_500 Percentage of area of 500 year floodplain in each sub basin
CATEGORY: BUILDINGS IN FLOODPLAIN
Description:
Buildings those would be inundated by flood at any given year over the every 100 or 500
years time period. Generally flood means a general and temporary condition of partial or
complete inundation of two or more acres of normally dry land area or of two or more
properties4.
Purpose:
The purpose of this dataset is to calculate the number of buildings/structures within the
100 year and 500 year floodplains in each sub basin of Pennypack Creek Watershed
Area.
Original Data Source:
Sources: CSC / DVRPC.
Year of Publication: 2000
Data acquired by CSC in 2002.
Data digitized by CSC in 2005.
Actions Taken for Data Processing:
Building footprints were digitized using the DVRPC 2000 aerial/ortho photograph.
Selection by location command of ArcGIS was used to select the buildings that are
intersected by the 100 and 500 year floodplain. Centroids of the selection were
calculated by each sub basin using Count Points in Polygons tool of Hawth’s Analysis
Tool for ArcGIS.
Description of Fields:
Field Name Description
NBLFP100 Number of Buildings in 100 Year Floodplain
NBLFP500 Number of Buildings in 500 Year Floodplain
Database Name: GEOL (Geology, Base Flow, and Soil)
Contact Information:
Name: ASM Abdul Bari / MD Mahbubur R Meenar
Organization: Center for Sustainable Communities, Temple University, PA
4 Source: FEMA
144

Email: asmbari@temple.edu, meenar@temple.edu
Date: 08/02/2005
GENERAL INFORMATION:
No of fields: 9 (Excluding BASIN_ID5)
No of Records: 49
CATEGORY: GEOLOGY & BASE FLOW
Description:
Bed rock refers to the rock underlying other unconsolidated material, i.e. soil. This file
displays the percentages of different types of generalized geology in each of the 49 sub
basins of Pennypack Watershed Area. Each type reflects a designation of certain
hydrologic properties.
Purpose:
Increased development in major parts of the Pennypack Creek Watershed has
increased public, industrial, and commercial demand for water. Further withdrawals may
reduce groundwater availability and stormflow. This database will help conduct any
groundwater assessment for the Pennypack Watershed Area.
Original Data Source:
Sources: Philadelphia Water Department (PWD) and Delaware River Basin Commission
(DRBC)
Year of Publication: 10/01/98 for DRBC data
Data acquired by CSC in 2005.
Actions Taken for Data Processing:
The original files were collected and/or processed into GIS shape file format. The PWD
shape file covers the geology of the watershed area inside Montgomery and Bucks
Counties and DRBC shape file covers the watershed area inside Philadelphia County.
These shape files are coded differently for different rock type. Following Dr. Jeffrey
Featherstone’s (Director, CSC) suggestion the DRBC rock type coding was taken as
standard and these codes were incorporated in the PWD shape file. Once these files
were merged together, the final shape file was clipped by Pennypack Watershed Area.
Using the Union tool of ArcToolBox, the sub basin shape file and the geology shape file
were overlaid to combine attribute information of geology and 49 sub basins. From the
output shape file, polygons with the same rock type in each sub basin were merged
together using the Editor Toolbar. After merging, rock type areas in each sub basin were
calculated. Finally, a Pivot Table was created and areas of all rock types were arranged
as separate fields and Basin IDs were placed as rows.
The Pivot Table was joined with the original sub basin shape file attribute table (based
on the common field – Basin ID) to get the area information for each sub basin.
Percentages of areas of different types of rocks present in each sub basin were
calculated. Finally, amount of Base Flow was calculated. This is the weighted average of
Median Base Flow according to percentages of different type of rocks present in each
sub basin.
5 BASIN_ID is the key field that ties each record with 49 sub basin boundaries generated by CSC.
145

Description of Fields:
Field Name Description
P_GEO_R1 Percentage of area covered by Rock Type 1 (Crystalline Rock other
than Diabase) in each sub basin
P_GEO_R2 Percentage of area covered by Rock Type 2 (Unconsolidated
Sediments) in each sub basin
P_GEO_R3 Percentage of area covered by Rock Type 3 (Carbonate Rock) in
each sub basin
P_GEO_R4 Percentage of area covered by Rock Type 4 (Sedimentary other
than Carbonates) in each sub basin
P_GEO_R5 Percentage of area covered by Rock Type 5 (Diabase) in each sub
basin
A_MBF Amount of Base Flow. Weighted average of Median Base Flow
according to percentages of different rock types present in each sub
basin. Unit is GPD/SQM (Gallon per day per square mile).
Additional Information:
In order to get the contact information for the original metadata or any other relevant
information from DRBC, please visit their web site at www.state.nj.us/drbc.
CATEGORY: SOIL
Description:
A hydrologic group is a group of soils having similar runoff potential under similar storm
and cover conditions. Soil properties that influence runoff potential are those that
influence the minimum rate of infiltration for a bare soil after prolonged wetting and when
not frozenxiii. The soils are placed into four groups, A, B, C and D, and three dual
classes, A/D, B/D, and C/D. According to the National Soil Survey Handbook of the
Natural Resources Conservation Services, the definitions of the hydrologic soil classes
are as follows:
A. (Low runoff potential). The soils have a high infiltration ratexiv even when thoroughly
wetted. They chiefly consist of deep, well drained to excessively drained sands or
gravels. They have a high rate of water transmissionxv.
B. The soils have a moderate infiltration rate when thoroughly wetted. They chiefly are
moderately deep to deep, moderately well drained to well drained soils that have
moderately fine to moderately coarse textures. They have a moderate rate of water
transmission.
C. The soils have a slow infiltration rate when thoroughly wetted. They chiefly have a
layer that impedes downward movement of water or have moderately fine to fine texture.
They have a slow rate of water transmission.
D. (High runoff potential). The soils have a very slow infiltration rate when thoroughly
wetted. They chiefly consist of clay soils that have a high swelling potential, soils that
have a permanent high water table, soils that have a claypan or clay layer at or near the
surface, and shallow soils over nearly impervious material. They have a very slow rate of
water transmission.
146

Dual hydrologic groups, A/D, B/D, and C/D, are given for certain wet soils that can be
adequately drained. The first letter applies to the drained condition, the second to the
undrained. Only soils that are rated D in their natural condition are assigned to dual
classes.
Purpose:
This file might be used in planning watershed-protection and flood-prevention projects.
Hydrologic groups are used in equations that estimate runoff from rainfall needed for
solving hydrologic problems (NRCS web site, see end note I). The purpose of this data
is to display the percentages of different types of hydrologic soil types present in each
sub basin of the Pennypack Watershed area.
Original Data Source:
Sources: Natural Resources Conservation Services (NRCS) and Delaware River Basin
Commission (DRBC)
Year of Publication: Unknown
Data acquired by CSC in 2005.
Actions Taken for Data Processing:
Soil data at the scale of 1:24,000 for Montgomery, Bucks, and Philadelphia Counties
were collected from Soil Survey Geographic (SSURGO) Database of NRCS. Their other
lower resolution dataset is called STATSGO, which is available at 1:250,000 scale and
was not used. Other soil data from the Philadelphia Water Department (PWD) was
available at a much smaller resolution and was not used.
1. The following steps were taken by the Department of Civil and Environmental
Engineering at Temple University, PA.
Soil data (in GIS shape file format) for each county was clipped to the region belonging
to the watershed.
Information for soil groups A, B, C, and D was added to the clipped files. This
information was originally available as database files and was imported in GIS as dbf
files. Some soil types lacked a HYDGRP (hydrologic group) value or had multiple
HYDGRP values were edited. The attribute MUSYM (Map Unit Symbol) was used to join
the soil type shape files. MUSYM is a soil code that changes from county to county.
Hence a single dbf file could not be used for all three counties.
All three clipped shape files were then appended or merged to create the final soil shape
file.
2. Using the Intersect tool of ArcToolBox, the soil shape file and the Pennypack
Watershed sub basin shape file were intersected to combine the attribute information of
49 sub basins and soil. From the intersected shape file, polygons with the same Basin ID
and hydrologic soil code (HYDGRP) were combined using the Dissolve tool of
ArcToolBox. After dissolving, the areas for each soil type in each sub basin were
calculated. Finally, a Pivot Table was created and areas of all the soil types were
arranged as separate fields and Basin IDs were placed as rows.
3. The Pivot Table was joined with the original sub basin shape file attribute table (based
on the common field – Basin ID) to get the area information for each sub basin.
147

4. Percentages of areas of different types of soil present in each sub basin were
calculated.
Description of Fields:
Field Name Description
P_SOIL_B Percentage of areas of hydrologic soil type B in each sub basin
P_SOIL_C Percentage of areas of hydrologic soil type C in each sub basin
P_SOIL_D Percentage of areas of hydrologic soil type D in each sub basin
Additional Information:
In order to get the contact information for the original metadata or any other relevant
information from NRCS, please visit their web site at http://www.nrcs.usda.gov/ or NRCS
Soils web site at http://soils.usda.gov/.
Database Name: LANDF (Pennypack Land Features)
Contact Information:
Name: Kurt Paulsen, Ph.D.
Organization: Temple University, Center for Sustainable Communities
Email: kurt.paulsen@temple.edu
Date: 08-16-2005
GENERAL INFORMATION:
No of fields: 76 (Excluding BASIN_ID6)
No of Records: 49
CATEGORY: IMPERVIOUS SURFACE
Description:
Estimate of the percentage that a sub-basin is covered by impervious surfaces.
Impervious surfaces were estimated based on satellite imagery.
Purpose:
“Impervious cover is an important indicator of watershed health… [and] is a critically
important variable in most hydrologic and water quality models used to analyze urban
watersheds.” (Center for Watershed Protection: Impervious Cover and Land Use in the
Chesapeake Bay Watershed. January 2001, p. iii)
Original Data Source:
Downloaded from Pennsylvania Spatial Data Archive (www.pasda.psu.edu). Data
created by Dr. Toby Carlson, Pennsylvania State University Department of Meteorology.
Title: Impervious surface area for Southeast Pennsylvania, 2000
Title: Impervious surface area for Southeast Pennsylvania, 1985
Full metadata online at:
http://www.pasda.psu.edu/documents.cgi/isa_pa/pa2000isaa_se.xml
http://www.pasda.psu.edu/documents.cgi/isa_pa/pa1985isaa_se.xml
6 BASIN_ID is the key field that ties each record with 49 sub basin boundaries generated by CSC.
148

Actions Taken for Data Processing:
Data were originally projected in Albers Conical Equal Area (NAD27) and reprojected
into Pennsylvania State Plane Feet South (NAD83).
Data were then clipped to the boundary of the Pennypack Creek watershed using
Hawth’s Tool: Clip Raster by Polygon.
Description of Fields:
Field Name Description
P_IMP85 Percent of sub-basin covered by impervious surface, 1985
P_IMP00 Percent of sub-basin covered by impervious surface, 2000
Additional Information:
Accuracy of original satellite imagery classification was verified visually using high-
resolution digital orthophotography available from Delaware Valley Regional Planning
Commission. A comparison of the Impervious Surface layer available from the USGS
National Land Cover Database and the Impervious Surface coverage from Dr. Toby
Carlson at Penn State with the digital orthophotography revealed that the Penn State
data was of superior quality and higher resolution, and hence was used in this analysis.
CATEGORY: VEGETATIVE COVERAGE
Description:
Estimate of the percentage that a sub-basin is covered by vegetation. Vegetative
coverage was estimated based on satellite imagery.
Purpose:
Vegetation serves many important ecological functions related to species habitat and
water quality. Fractional vegetation data is a biophysical variable that describes the
percent of vegetation covering the area of a raster cell. Fractional vegetation is used as
input to hydrologic, meteorologic and plant growth models. Hydrologically, plant cover
reduces the amount and velocity of rainfall hitting the surface, thus reducing erosional
forces. Plant cover also intercepts sun light reducing thermal emission from the soil
surface.
Original Data Source:
Downloaded from Pennsylvania Spatial Data Archive (www.pasda.psu.edu). Data
created by Dr. Toby Carlson, Pennsylvania State University Department of Meteorology.
Title: Fractional Vegetation Cover for Southeast Pennsylvania, 2000
Title: Fractional Vegetation Cover for Southeast Pennsylvania, 1985
Full Metadata online at:
http://www.pasda.psu.edu/documents.cgi/isa_pa/pa2000fvca_se.xml
http://www.pasda.psu.edu/documents.cgi/isa_pa/pa1985fvca_se.xml
Actions Taken for Data Processing:
Data were originally projected in Albers Conical Equal Area (NAD27) and reprojected
into Pennsylvania State Plane Feet South (NAD83).
Data were then clipped to the boundary of the Pennypack Creek watershed using
Hawth’s Tool: Clip Raster by Polygon.
149

Description of Fields:
Field Name Description
FVC_85 Sub-basin fractional vegetative coverage, 1985
FVC_00 Sub-basin fractional vegetative coverage, 2000
Additional Information:
Original estimates of pixel vegetative coverage by Dr. Toby Carlson were based on the
NDVI (Normalized Difference Vegetation Index) method. Fractional vegetative
coverage, the percent of a pixel covered by vegetation (where zero is bare soil and one
is dense vegetation) is the NDVI squared.
CATEGORY: TREE CANOPY DENSITY
Description:
Estimates of tree canopy density for each sub-basin. Estimates were based on satellite
imagery.
Purpose:
Land cover and land use maps designate areas as “forested” but do not estimate canopy
density. Additionally, tree canopy coverage may occur in pixels not classified as
“forested” in land cover or land use classifications. Tree canopy cover data is useful in a
number of ecological and hydrological models.
Original Data Source:
National Land Cover Database Zone 60 Tree Canopy Layer. A product of the United
States Geological Survey (USGS). Data were extracted from http://seamless.usgs.gov
web server. Data server allows user to identify geographic coordinates for downloading
files. Data were extracted based on Pennypack Creek watershed boundaries.
Original Citation Details:
References: Homer, C., C. Huang, L. Yang, B. Wylie and M. Coan, Development of a
2001 national land cover database for the United States. Photogrammetric Engineering
and Remote Sensing (in press).
Huang, C., L. Yang, B. Wylie, and C. Homer, 2001. A strategy for estimating tree
canopy density using Landsat 7 ETM+ and high resolution images over large areas. In:
Third International Conference on Geospatial Information in Agriculture and Forestry;
November 5-7, 2001; Denver, Colorado. CD-ROM, 1 disk.
The National Land Cover Database 2001 land cover layer for mapping zone 60 was
produced through a cooperative project conducted by the Multi-Resolution Land
Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of federal
agencies (www.mrlc.gov) that consist of the U.S. Geological Survey (USGS), the
National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental
Protection Agency (EPA), the U.S. Department of Agriculture (USDA), the Forest
Service (USFS), the National Park Service (NPS), the U.S. Fish and Wildlife Service
(FWS), the Bureau of Land Management (BLM) and the USDA Natural Resources
Conservation Service (NRCS). One of the primary goals of the project is to generate a
current, consistent, seamless and accurate National Land Cover Database (NLCD) circa
2001 for the United States at medium spatial resolution. For a detailed definition and
150

discussion on MRLC and the NLCD 2001 products, refer to Homer et al. (2003) and
<http://www.mrlc.gov/mrlc2k.asp>.
Actions Taken for Data Processing:
Data were originally projected in Albers Conical Equal Area (NAD83) and subsequently
reprojected into Pennsylvania State Plane Feet South (NAD83).
Data were then clipped to the boundary of the Pennypack Creek watershed using
Hawth’s Tool: Clip Raster by Polygon.
Description of Fields:
Field Name Description
P_CNP01 Sub-basin percent canopy density, 2001
Additional Information:
Detailed accuracy assessment of the tree-canopy density estimation algorithm is
contained in: Homer, C., C. Huang, L. Yang, B. Wylie and M. Coan, Development of a
2001 national land cover database for the United States. Photogrammetric Engineering
and Remote Sensing (in press).
CATEGORY: LAND COVER
Description:
Estimates of percent of sub-basin in various land cover classes. Estimates are based on
satellite imagery.
Purpose:
Detailed description of the land cover characteristics of each sub-basin.
Original Data Source:
National Land Cover Database Zone 60 Land Cover Layer. A product of the United
States Geological Survey (USGS). Data were extracted from http://seamless.usgs.gov
web server. Data server allows user to identify geographic coordinates for downloading
files. Data were extracted based on Pennypack Creek watershed boundaries.
Original Citation Details:
References: Homer, C., C. Huang, L. Yang, B. Wylie and M. Coan, Development of a
2001 national land cover database for the United States. Photogrammetric Engineering
and Remote Sensing (in press).
The National Land Cover Database 2001 land cover layer for mapping zone 60 was
produced through a cooperative project conducted by the Multi-Resolution Land
Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of federal
agencies (www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National
Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection
Agency (EPA), the U.S. Department of Agriculture (USDA) Forest Service (USFS), the
National Park Service (NPS), the U.S. Fish and Wildlife Service (FWS), the Bureau of
Land Management (BLM) and the USDA Natural Resources Conservation Service
(NRCS). One of the primary goals of the project is to generate a current, consistent,
seamless, and accurate National Land cover Database (NLCD) circa 2001 for the United
States at medium spatial resolution. For a detailed definition and discussion on MRLC
151

and the NLCD 2001 products, refer to Homer et al. (2003) and
<http://www.mrlc.gov/mrlc2k.asp>.
Actions Taken for Data Processing:
Data were originally projected in Albers Conical Equal Area (NAD83) and subsequently
reprojected into Pennsylvania State Plane Feet South (NAD83). Data were then clipped
to the boundary of the Pennypack Creek watershed using Hawth’s Tool: Clip Raster by
Polygon.
Description of Fields:
Field Name Description
P_LC_11 Percent of sub-basin land cover in Open Water, 2001
P_LC_21 Percent of sub-basin land cover in Developed, Open Space, 2001
P_LC_22 Percent of sub-basin land cover in Developed, Low Intensity, 2001
P_LC_23 Percent of sub-basin land cover in Developed, Medium Intensity,
2001
P_LC_24 Percent of sub-basin land cover in Developed, High Intensity, 2001
P_LC_31 Percent of sub-basin land cover in Barren Land, 2001
P_LC_41 Percent of sub-basin land cover in Deciduous Forest, 2001
P_LC_42 Percent of sub-basin land cover in Evergreen Forest, 2001
P_LC_43 Percent of sub-basin land cover in Mixed Forest, 2001
P_LC_81 Percent of sub-basin land cover in Pasture/Hay, 2001
P_LC_82 Percent of sub-basin land cover in Cultivated Crops, 2001
P_LC_90 Percent of sub-basin land cover in Woody Wetlands, 2001
P_LC_95 Percent of sub-basin land cover in Emergent Herbaceous Wetlands,
2001
P_LC_DEV Percent of sub-basin land cover “Developed” (sum of land cover
codes 21, 22, 23, and 24)
P_LC_FOR Percent of sub-basin land cover “Forested” (sum of land cover codes
41, 42, 43)
P_LC_AG Percent of sub-basin land cover “Agriculture” (sum of land cover
codes 81 and 82)
P_LC_WWL Percent of sub-basin land cover “Water or Wetlands” (Sum of land
cover codes 11, 90 and 95)
Additional information:
Land Cover Codes and Explanations, from National Land Cover Database:
11. Open Water – All areas of open water, generally with less than 25% cover of
vegetation or soil.
21. Developed, Open Space - Includes areas with a mixture of some constructed
materials, but mostly vegetation in the form of lawn grasses. Impervious surfaces
account for less than 20 percent of total cover. These areas most commonly include
large-lot single-family housing units, parks, golf courses, and vegetation planted in
developed settings for recreation, erosion control, or aesthetic purposes.
22. Developed, Low Intensity - Includes areas with a mixture of constructed materials
and vegetation. Impervious surfaces account for 20-49 percent of total cover. These
areas most commonly include single-family housing units.
152

23. Developed, Medium Intensity - Includes areas with a mixture of constructed
materials and vegetation. Impervious surfaces account for 50-79 percent of the total
cover. These areas most commonly include single-family housing units.
24. Developed, High Intensity - Includes highly developed areas where people reside
or work in high numbers. Examples include apartment complexes, row houses and
commercial/industrial. Impervious surfaces account for 80 to100 percent of the total
cover.
31. Barren Land (Rock/Sand/Clay) - Barren areas of bedrock, desert pavement,
scarps, talus, slides, volcanic material, glacial debris, sand dunes, strip mines, gravel
pits and other accumulations of earthen material. Generally, vegetation accounts for less
than 15% of total cover.
41. Deciduous Forest - Areas dominated by trees generally greater than 5 meters
tall, and greater than 20% of total vegetation cover. More than 75 percent of the tree
species shed foliage simultaneously in response to seasonal change.
42. Evergreen Forest - Areas dominated by trees generally greater than 5 meters
tall, and greater than 20% of total vegetation cover. More than 75 percent of the tree
species maintain their leaves all year. Canopy is never without green foliage.
43. Mixed Forest - Areas dominated by trees generally greater than 5 meters tall,
and greater than 20% of total vegetation cover. Neither deciduous nor evergreen
species are greater than 75 percent of total tree cover.
81. Pasture/Hay - Areas of grasses, legumes, or grass-legume mixtures planted for
livestock grazing or the production of seed or hay crops, typically on a perennial cycle.
Pasture/hay vegetation accounts for greater than 20 percent of total vegetation.
82. Cultivated Crops - Areas used for the production of annual crops, such as corn,
soybeans, vegetables, tobacco, and cotton, and also perennial woody crops such as
orchards and vineyards. Crop vegetation accounts for greater than 20 percent of total
vegetation. This class also includes all land being actively tilled.
90. Woody Wetlands - Areas where forest or scrubland vegetation accounts for
greater than 20 percent of vegetative cover and the soil or substrate is periodically
saturated with or covered with water.
95. Emergent Herbaceous Wetlands - Areas where perennial herbaceous vegetation
accounts for greater than 80 percent of vegetative cover and the soil or substrate is
periodically saturated with or covered with water.
CATEGORY: LAND USE
Description:
These are estimates of the percent of sub-basin land use distribution for 1990, 1995 and
2000.
Purpose:
Document and describe land use patterns and land use change in the Pennypack Creek
watershed.
Original Data Source:
DVRPC Land Use Digital Data for 1990, 1995 and 2000
Actions Taken for Data Processing:
Data were originally projected in UTM Zone 18N (NAD83) and subsequently reprojected
into Pennsylvania State Plane Feet South (NAD83).
Data were clipped to Pennypack Creek watershed boundary.
153

Description of Fields:
Field Name Description
P_LU00_01 Percent of sub-basin land use in residential: single family detached,
2000
P_LU00_02 Percent of sub-basin land use in residential: multi-family and row-
homes, 2000
P_LU00_03 Percent of sub-basin land use in manufacturing/light industrial, 2000
P_LU00_04 Percent of sub-basin land use in transportation, 2000
P_LU00_05 Percent of sub-basin land use in utility, 2000
P_LU00_06 Percent of sub-basin land use in commercial, 2000
P_LU00_07 Percent of sub-basin land use in community services, 2000
P_LU00_08 Percent of sub-basin land use in military, 2000
P_LU00_09 Percent of sub-basin land use in recreation, 2000
P_LU00_10 Percent of sub-basin land use in agriculture, 2000
P_LU00_12 Percent of sub-basin land use in wooded, 2000
P_LU00_13 Percent of sub-basin land use in water, 2000
P_LU00_14 Percent of sub-basin land use in vacant, 2000
P_LU00_15 Percent of sub-basin land use in non-residential parking, 2000
P_LU95_01 Percent of sub-basin land use in residential: single family detached,
1995
P_LU95_02 Percent of sub-basin land use in residential: multi-family and row-
homes, 1995
P_LU95_03 Percent of sub-basin land use in manufacturing/light industrial, 1995
P_LU95_04 Percent of sub-basin land use in transportation, 1995
P_LU95_05 Percent of sub-basin land use in utility, 1995
P_LU95_06 Percent of sub-basin land use in commercial, 1995
P_LU95_07 Percent of sub-basin land use in community services, 1995
P_LU95_08 Percent of sub-basin land use in military, 1995
P_LU95_09 Percent of sub-basin land use in recreation, 1995
P_LU95_10 Percent of sub-basin land use in agriculture, 1995
P_LU95_12 Percent of sub-basin land use in wooded, 1995
P_LU95_13 Percent of sub-basin land use in water, 1995
P_LU95_14 Percent of sub-basin land use in vacant, 1995
P_LU95_15 Percent of sub-basin land use in non-residential parking, 1995
P_LU90_01 Percent of sub-basin land use in residential: single family detached,
1990
P_LU90_02 Percent of sub-basin land use in residential: multi-family and row-
homes, 1990
P_LU90_03 Percent of sub-basin land use in manufacturing/light industrial, 1990
P_LU90_04 Percent of sub-basin land use in transportation, 1990
P_LU90_05 Percent of sub-basin land use in utility, 1990
P_LU90_06 Percent of sub-basin land use in commercial, 1990
P_LU90_07 Percent of sub-basin land use in community services, 1990
P_LU90_08 Percent of sub-basin land use in military, 1990
P_LU90_09 Percent of sub-basin land use in recreation, 1990
P_LU90_10 Percent of sub-basin land use in agriculture, 1990
P_LU90_12 Percent of sub-basin land use in wooded, 1990
P_LU90_13 Percent of sub-basin land use in water, 1990
P_LU90_14 Percent of sub-basin land use in vacant, 1990
154

P_LU90_15 Percent of sub-basin land use in non-residential parking, 1990
Additional Information:
Land use code 01: Single family includes mobile homes
Land use code 02: Multi-family and row-homes includes associated parking.
Land use code 08: Military includes associated parking
Land use code 15: Non-residential parking includes parking associated with:
Manufacturing, utility, commercial, community services and recreation
CATEGORY: FOREST FRAGMENTATION
Description:
Calculations of various metrics to describe forest fragmentation within the watershed
Purpose:
Document and describe forest fragmentation patterns in the Pennypack Creek
watershed. Forest fragmentation is an important indicator of ecosystem health,
landscape integrity and has played an important role in ReVA analyses.
Original Data Source:
DVRPC Land Use Digital Data for 1990, 1995 and 2000. For this analysis, the
processed Pennypack land use files for 1990 and 2000 were used. Detailed calculations
below.
Actions Taken for Data Processing:
Calculations of the Simpson Index of Forest Fragmentation and of the Shape Index were
performed using the LandUseAnalysis Wizard, a landscape analysis extenstion for
ArcGIS 8.3, written by Dr. Kurt Paulsen.
Calculations of Nearest Neighbor distance were performed utilizing Hawth’s tools:
Distance Between Points (within layer)
Description of Fields:
Field Name Description
SIFF_90 Sub-basin Simpson Index of Forest Fragmentation, 1990
SIFF_00 Sub-basin Simpson Index of Forest Fragmentation, 2000
MSIF_90 Sub-basin mean shape index of forested patched, 1990
MSIF_00 Sub-basin mean shape index of forested patched, 2000
Sub-basin average nearest neighbor distance of forested patches, in
feet, 1990 ANNDF_90 Sub-basin average nearest neighbor distance of forested patches, in
feet, 2000 ANNDF_00
Additional Information:
Simpson Index of Forest Fragmentation:
The Simpson Fragmentation index is patch-size and scale independent measure of
fragmentation, based on entropy theory. The “intuitive” interpretation of this index is:
what percent “fragmented” is a land use class. The index is:
155

2
1
1
2
1
⎟
⎠
⎞
⎜
⎝
⎛
−=
∑
∑
=
=
n
i
n
i
Area
Area
IndexgmentationSimpsonFra
Where i=1…..n indexes the number of patches of a certain land use class within a
defined area (here, forested patches within a sub-basin), and area indicates the
calculated area of each forest patch. Similar to other entropy measures, the Simpson
Fragmentation index is 1 minus the sum of squares over the square of sums. The index
is defined to range from 0 to 1 with 0 indicating perfect consolidation (only one patch of
forest in a sub-basin) and 1 indicating perfect fragmentation. Higher numbers indicate a
higher percentage of fragmentation. The calculation results in one fragmentation for
each sub-basin for each year. Note that the calculation is based on area and not on the
distance between forested patches.
Shape Index:
The shape index is also a measure of fragmentation with a particular emphasis on
patch-shape morphology. Patch shape is a critical measure of ecological integrity and
species habitat, with some applications to watershed analysis. Generally speaking, the
more “square” is a patch of forest, the less “edge” effect and the greater “core” area.
Greater core area is associated with greater species diversity and with improved
ecological function.
The ReVA Mid-Atlantic assessment presented measures of watershed forest habitat
shape in terms of edge and interior metrics. The Shape Index incorporates both edge
and interior concepts, and makes use of the higher-resolution vector land use data
available for this watershed. The shape index measures the deviation of a patch of land
from a perfectly square patch and is given by:
i
i
iArea
Perimeter
IndexPatchShape *25.
=
That is, for each patch “i” the Shape Index measures its deviation from a perfect square.
The Shape Index equals 1 when the patch is a square, and increases with increasing
shape complexity. For each subbasin, the measure reported is the mean (average)
shape index for all forested patches within the sub-basin.
Nearest-Neighbor Distance:
The distance between patches of forest is an additional indicator of fragmentation.
When distinct patches of forest are further apart, there is less ecological integrity. For
each unique patch of forested land within the Pennypack watershed, the distance to the
nearest forest patch is calculated (in feet.) These values are then averaged over each
sub-basin.
CATEGORY: SLOPE
Description:
This shape file has five classifications of slope values. The classifications are 0-2%, 2-
5%, 5-15%, 15-25% and more than 25%.
156

Purpose:
This slope information will help to determine the areas suitable for development within
the watershed.
Original Data Source:
The slope data was derived from a Digital Elevation Model created for the Center for
Sustainable Communities in 2004.
Actions Taken for Data Processing:
Slope file was generated using 50 feet pixel resolution because of the ArcGIS 9 inability
to convert 2 feet pixel resolution DEM (Digital Elevation Model) to slope. Spatial Analyst
extension was used to derive the slope. After that this file was reclassified with
percentage classification determined by Professor Kurt Paulsen and Mary Myers from
Temple University. This reclassified grid file was converted into a shape file and later
intersected with the sub basin file and dissolved according to the classification code.
Simultaneously area for the classified slope area was calculated. All of these are done
using a model developed by ASM Bari, GIS coordinator or CSC, Temple University.
After that pivot table functionality of ArcGIS was used to determine the percentage slope
for each category for each sub basin.
Description of Fields:
Field Name Description
PSL0_2 Percentage of Slope 0-2
PSL2_5 Percentage of Slope 2-5
PSL5_15 Percentage of Slope 5-15
PSL15_25 Percentage of Slope 15-25
PSLG25 Percentage of Slope greater than 25
CATEGORY: ROAD DENSITY
Description:
Road density is the average total road length per unit of landscape. Many ecological
phenomena, from wildlife to flooding to biodiversity are related to road density.7
Purpose:
To determine the impacts of the road network on the Watershed.
Original Data Source:
Road file from the DVRPC was used to calculate the road density (2000/2005).
Actions Taken for Data Processing:
Sum Line Lengths in Polygon functionality of Hawth’s tools was used to determine the
road length by sub basin.
After that, it was divided by total area by each sub basin to determine the density.
Description of Field:
7 Forman, Richard, et al. Road Ecology: Science and Solutions. Island Press: 2003
157

Field Name Description
R_DEN_M Road Density in Mile per Square Mile
Database Name: WTR_REL (Water Related Data for Pennypack Watershed)
Contact Information:
Name: ASM Abdul Bari / MD Mahbubur R Meenar
Organization: Center for Sustainable Communities (CSC), Temple University, PA
Email: asmbari@temple.edu, meenar@temple.edu
Date: 7/22/2005
GENERAL INFORMATION:
No of fields: 23 (Excluding BASIN_ID8)
No of Records: 49
CATEGORY: WETLAND
Description:
Wetlands are land areas seasonally or permanently waterlogged by either fresh or salt
water. These include lakes, rivers, estuaries and freshwater marshes. Wetlands are
areas where water saturation is the dominant factor that determines the nature of soil
development and the types of plant and animal communities living in the soil and on its
surface. Most wetlands contain soil or substrate that is at least periodically saturated
with or covered by water. The water creates severe physiological problems for plants
and animals that are not adapted for life in water or in saturated soil.
Purpose:
The purpose of this data is to calculate the percentage of wetland area in each sub
basin.
Original Data Source:
Delaware Valley Regional Planning Commission (DVRPC)
The data was created in 1981.
Scale: 1:80,000 roughly, as indicated in the original metadata.
DVRPC converted this data from National Wetlands Inventory (NWI) data by U.S. Fish &
Wildlife Service.
Data acquired by CSC in 2002.
Actions Taken for Data Processing:
Original data was available in GIS shape file format. The shape file was clipped by
Pennypack Watershed Area.
From the attribute table, some of the original fields (AREA and PERIMETER) were
deleted, because those were in MKS (Meter, kilogram, Second) unit. Instead, a new field
called AREA_SQM has been created to store AREA information in FPS (Foot, Pound,
Second) unit.
8 BASIN_ID is the key field that ties each record with 49 sub basin boundaries generated by CSC.
158

Using Polygon in Polygon tool of Hawth’s Analysis Tool for ArcGIS, areas of wetland in
each sub basin (49 in total) were calculated in a new shape file. A new dbf (database)
file was exported from the shape file. The dbf file was joined with sub basin file in order
to get the areas of each sub basin. Percentages of areas of wetland in each sub basin
were calculated.
Description of Fields:
Field Name Description
P_WL Percentages of areas of wetland in each sub basin
Additional Information:
The code explanation was not given with the original data. The National Wetlands
Inventory Mapping Code Description at http://www.nwi.fws.gov/atx/atx.html does not
have all the code listed.
CATEGORY: BRIDGES & CULVERTS
Description:
A bridge is a structure built to span a gorge, valley, road, railroad track, river or any other
physical obstacle. A culvert is a closed conduit built to convey surface drainage water
under a roadway or other impediment.
Purpose:
The purpose of this data is to determine the number of culverts and bridges in each sub
basin. Points were generated at the intersections of road and stream centerlines.
Original Data Source:
Data originated at the CSC.
Aerial images (2000) street centerline file source: DVRPC
Stream centerline file source: CSC
Actions Taken for Data Processing:
Input GIS shape files were street centerline and stream centerline of Pennypack
Watershed boundary. Hawth’s Analysis Tool for ArcGIS was used to generate the
intersection points of stream centerlines and street centerlines. The tool’s name is
Intersect Lines (Make Points). Once the points were generated, random quality checking
was done with reference to DVRPC 2000 aerial images. No field verification could be
made because of time constraint. The other tool used from Hawth’s Analysis Tool was
Count Points in Polygons in order to get the number of bridges and culverts in each sub
basin.
Description of Fields:
Field Name Description
N_BC The number of bridges and culverts in the sub basin
CATEGORY: DAMS
Description:
159

A dam is a structure that impounds and stores water in a reservoir, making it available
for future use. The dams indicated here are line files, with two points on either side of
the waterway each dam impedes.
Purpose:
The purpose of this data is to show the number of dams within each subdivision.
Original Data Source:
Philadelphia Water Department.
Data creation year: 1999
Data acquired by CSC in 2002.
Actions Taken for Data Processing:
The original shape file was clipped by Pennypack Watershed Area. The sub basin
boundary shape file was overlaid on top of Dam shape file. No of dams present in each
sub basin was counted and recorded in the database file.
Description of Fields:
Field Name Description
N_DAM No of dams in each sub basin
CATEGORY: RIPARIAN BUFFER
Description:
A Riparian Buffer is a zone of protection made up of trees and other vegetation that grow
along the banks of a waterway. Riparian Buffers help keep a stream healthy by reducing
stream bank erosion and acting as a natural soil filterxvi.
The Philadelphia Water Department (PWD) classified the forest buffer according to a fifty
foot standard, and digitized sections of the stream bank lacking a forest buffer using
aerial photography taken in 2000 and provided by the Delaware Valley Regional
Planning Commission. The term “Lacking Forest Buffer” is defined as a stream bank
with less than fifty foot wide layer of forest cover and less than 50% canopy closure.
Where the stream bank appeared to be lacking a forest buffer on both sides, the section
was classified as such. Otherwise, each side of the creek was treated separately.
Larger pond or lake areas that result from the damming of the main stem creek or major
tributary were assessed; small water bodies, such as man-made farm ponds, were not.
Purpose:
The purpose of this data is to identify stream banks within Pennypack Watershed Area
lacking riparian forest buffers.
Original Data Source:
Source: Heritage Conservancy
Year of Publication: 2002 (Data created from 2000 aerial photography. Field checks
performed in 2002)
Data acquired by CSC in 2003.
Actions Taken for Data Processing:
160

In order to calculate the percentage of streams with Riparian Buffers in each sub basin,
knowing the total length of each stream and the length of stream segments lacking
Riparian Buffers on one side or both sides was essential. The original Riparian Buffer
assessment data was created for the banks, but not for the stream, and creating
Riparian Buffer assessment data for the stream itself was beyond the scope of this
project. The solution was to calculate the percentages by comparing the length of the
banks to the length of the stream. Below are the major data processing steps:
The original file was available in GIS shape file format and the feature type was line. In
the attribute table, two fields were created from the original field providing information
about a lack of buffer on one or both sides. Data was rearranged according to the 49 sub
basins. To accomplish this, Sum Length of Lines in Polygons tool from Hawth’s Analysis
Tool for ArcGIS was used. Using this tool, an extension of ArcMap designed for spatial
analysis, the line feature was clipped for each of the 49 sub basins and the total sum of
length for the clipped lines was added as a value in a new field in the attribute table.
Total length of streams in each sub basin was added in a new field. Percentages were
calculated.
Description of Fields:
Field Name Description
P_RB_1SL Percentage of stream lacking Riparian Buffer on one side (in mile)
P_RB_2SL Percentage of stream lacking Riparian Buffer on both sides (in mile)
P_RB_2SE Percentage of stream having Riparian Buffer on both sides (in mile)
Additional Information:
Heritage Conservancy has published the original Riparian Buffer Status shape file for
Southeastern Pennsylvania region. Contact information for Heritage Conservancy:
Heritage Conservancy
85 Old Dublin Pike
Doylestown, Pa 18901
Ph: 215-345-7020
Fax: 215-345-4328
www.heritageconservancy.org
CATEGORY: DISCHARGES AND WITHDRAWALS
Description:
This data indicates the amount of groundwater withdrawn in each sub basin on an
average daily basis in millions of gallons per day, as well as the amount of water
released back into streams in each sub basin on an average daily basis in millions of
gallons per day.
Purpose:
The purpose of this data is to gain a better picture of the water balance in each of the
sub basins.
Original Data Source:
Delaware River Basin Commission (DRBC)
Data creation year: 1996
This data was originally in point form and the attributes were attributed to sub basins.
161

Data acquired by CSC in 2005.
Actions Taken for Data Processing:
Originally, the data was connected to points indicating the locations of withdrawals and
discharges. This data was then transferred to the 49 sub basins by means of attributing
each point to the basin the point lies within. If there was more than one point in a basin,
the values for those points were summed and that sum was attributed to the basin.
Description of Fields:
Field Name Description
A_TGWW Ground Water Withdrawals (Million Gallons Per Day)
MPD Discharges into Stream (Million Gallons Per Day)
CATEGORY: CURVE NUMBER
Description:
The Curve Number or ‘CN’ is a hydrologic constant factor used to measure the storm
water runoff potential for drainage area or sub basin. The CN is a function of the soil and
landuse of a drainage basin. Therefore, estimation of a CN requires processing of the
soil and landuse data based on unique soil types and unique land use categories within
the drainage basin boundaries. This CN index was developed by the Soil Conservation
Service (SCS), which is now called the Natural Resource Conservation Service (NRCS).
9
Purpose:
The purpose of this data is to map the storm water runoff potential for the sub basin.
Original Data Source:
Sources: Natural Resources Conservation Services (NRCS), Delaware River Basin
Commission (DRBC), and Delaware Valley Regional Planning Commission (DVRPC).
Year of Publication: 2000 for Landuse, unknown for Soil
Data acquired by CSC in 2003/2005.
Actions Taken for Data Processing:
Soil data and Landuse data (in GIS shape file format) for each county was clipped to the
region belonging to the watershed.
An index of Runoff Curve Numbers for Urban areas for each hydrologic soil group and
Landuse type was developed by modifying the parameters developed by NRCS (NRCS
SCS TR-55). The Anderson Landuse Classification type was associated with similar
Landuse type.
Intersect function of ArcToolbox was used to combine the Soil and Landuse GIS data so
that the attribute table of this new data will have a Soil group code for each Landuse
category. Similar Landuse category and Soil group was dissolved to minimize the
geoprocessing type of the ArcGIS software. ArcCN-Runoff extension developed by Min-
Lang Huang and Xiaoyong Zhan (Kansas Geological Survey, The University of Kansas)
was downloaded from the ESRI web site to calculate the Curve Number. Area weighted
mean value of CN was then calculated for each sub basin by using Polygon in Polygon
9 Source: United States Department of Agriculture (USDA), Natural Resource Conservation Service Website (http://www.nrcs.usda.gov/),
last accessed on August 5, 2005.
162

Analysis of Hawth’s Tools. This tool is a freeware and can be downloaded from
http://www.spatialecology.com/htools/digxy.php .
Description of Fields:
Field Name Description
CN_AWM Area Weighted Mean Curve Number for each Sub Basin for
storm water runoff potential
CATEGORY: STREAM LENGTH
Purpose:
The purpose of this data is to calculate the length, in miles, of stream centerlines in each
sub basin. There are two additional fields: Proportion of total stream length that has
road within thirty feet and proportion of total stream length that has road within one
hundred feet.
Original Data Source:
Data originated at the CSC.
Actions Taken for Data Processing:
In order to find out the stream length, Sum Length of Lines in Polygons tool from
Hawth’s Analysis Tool for ArcGIS was used. In order to get the proportion of total stream
length that has roads within 30 feet and 100 feet, a buffer of 30 feet and a buffer of 100ft
around the streets were drawn. Then the stream was clipped and the length of stream
was calculated by each sub basin using Sum Length of Lines in Polygons tool. After that
the proportion of stream length was calculated by dividing the clipped stream length with
the total stream length for each sub basin.
Description of Fields:
Field Name Description
L_STRM_M Length of stream in miles
PS_30FR Proportion of total stream length that has road within 30 feet
PS_100FR Proportion of total stream length that has road within 100 feet
CATEGORY: EFFLUENT CONCENTRATION
Description:
This data contains concentrations of common and important dissolved chemicals. This
data was collected at 20 different stations by the Philadelphia Water Department during
the summer of 2002. No data is represented by -999.
Type of feature:
Point
Original Data Source:
Source: Philadelphia Water Department (PWD)
Year of Publication: 2003
Data acquired by CSC in 2005.
163

Actions Taken for Data Processing:
Projection information: State Plane NAD 1983 (Feet) Pennsylvania South
Following the twenty monitoring stations set by PWD, the original data was cleaned up
and all of the categories were arranged by Dr. Peter Petraitis (ppetrait@sas.upenn.edu)
of the University of Pennsylvania.
Description of Fields:
Field Name Description
ALKAL Alkalinity (mg CaCO3/L)
AMMON Ammonia (mg/L)
DIS_02 Dissolved O2 (mg/L)
E_COLI E. coli (colony forming units per 100mL)
FEC_COL Fecal Coliform (colony forming units per 100mL)
NITRATE Nitrate (mg/L)
NITRITE Nitrite (mg/L)
ORTHOPHO Orthophosphate (mg/L)
TOT_PHOS Total Phosphorus (mg/L)
CHLOR_A Chlorophyll A (mg/L)
Database Name: SSA (Sewer Service Area Data for Pennypack Watershed)
Data Analyst:
Name: Jesse Sherry
Organization: Center for Sustainable Communities (CSC), Temple University, PA
Email: jsherry@temple.edu
Date: 7/21/2005
Contact Information:
Name: ASM Abdul Bari / MD Mahbubur R Meenar
Organization: Center for Sustainable Communities (CSC), Temple University, PA
Email: asmbari@temple.edu, meenar@temple.edu
Date: 7/22/2005
GENERAL INFORMATION:
No of fields: 9 (Excluding BASIN_ID10)
No of Records: 49
CATEGORY: SEWER SERVICE AREA
Description:
For each basin, the percentage of its area served by the various local Sewer Authorities
is presented.
Purpose:
The purpose of this data is to calculate how much of the sub basins have sewer service.
Original Data Source:
Delaware River Basin Commission (DRBC)
10 BASIN_ID is the key field that ties each record with 49 sub basin boundaries generated by CSC.
164

Data creation year: 1996
Data acquired by CSC in 2005.
Actions Taken for Data Processing:
The sewer service areas were overlaid by the basins and the percentage of each basin
that is served by each service was calculated.
Description of Fields:
Field Name Description
S_ABING The percentage of the basin served by the Abington Township STP.
The percentage of the basin served by the Bucks County Water and
Sewer Authority S_BUCKS
S_DELC The percentage of the basin served by the Delcora
The percentage of the basin served by the Horsham Township Sewer
Authority S_HORSH The percentage of the basin served by the Upper Moreland - Hatboro
Joint Authority S_UP_MOR The percentage of the basin served by the Warminster Township
Municipal Authority S_WARM
S_PWD The percentage of the basin served by the Philadelphia Water Dept.
S_NO_SERV The percentage of the basin not served by any sewer service
T_SSA The Percentage of the basin served by the Total Sewer Service Area
Database Name: WSA (Water Service Area Data for Pennypack Watershed)
Data Analyst:
Name: Jesse Sherry
Organization: Center for Sustainable Communities (CSC), Temple University, PA
Email: jsherry@temple.edu
Date: 7/21/2005
Contact Information:
Name: ASM Abdul Bari / MD Mahbubur R Meenar
Organization: Center for Sustainable Communities (CSC), Temple University, PA
Email: asmbari@temple.edu, meenar@temple.edu
Date: 7/22/2005
GENERAL INFORMATION:
No of fields: 9 (Excluding BASIN_ID11)
No of Records: 49
CATEGORY: WATER SERVICE AREA
Description:
For each basin, the percentage of its area provided with water service from the various
local water authorities is presented.
11 BASIN_ID is the key field that ties each record with 49 sub basin boundaries generated by CSC.
165

Purpose:
The purpose of this data is to calculate how much of each sub basin is provided with
water service.
Original Data Source:
Delaware River Basin Commission (DRBC)
Data creation year: 1996
Data acquired by CSC in 2005.
Actions Taken for Data Processing:
The sewer service areas were overlaid by the basins and the percentage of each
basin that is served with water was calculated.
Description of Fields:
Field Name Description
W_HATBOR The percentage of the basin served by the Hatboro Water Authority
The percentage of the basin served by the Horsham Township
Water Authority W_HORSHA The percentage of the basin served by the North Wales Water
Authority W_NWALES The percentage of the basin served by the Philadelphia Suburban
Water Company W_PHLSUB The percentage of the basin served by the Upper Southampton
Township Municipal Authority W_UPPSTH The percentage of the basin served by the Warminster Township
Municipal Authority W_WARMIN
W_WILGRO The percentage of the basin served by the Willow Grove USNAS
W_PWD The percentage of the basin served by the Philadelphia Water Dept.
T_WSA The Percentage of the basin served by the Total Water Service Area
166

A.3. Ecological Indicators: Technical Details & Tables
A.3.1. Graphs and Tables for the Hydrological Modeling
,
0
500
1000
1500
2000
2500
0 6 12 18 24 30 36 42 48 54 60
Time,hours
Discharge, cfs
Simulated
Observed
Figure A.3.1. Storm 1 - October 08, 1996
Time, hours
Discharge, cfs
010 20 30 40 50
0
2000
4000
6000
Simulated
Observed
Figure A.3.2. Storm 2 - October 18, 1996
167

Time, hours
Discharge, cfs
30 40 50
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
Simulated
Figure A.3.3. Storm 3 - September 3, 1999 (Floyd)
Time ,hours
Discharge, cfs
020 40 60 80
0
200
400
600
800
Simulated
Observed
Figure A.3.4. Storm 4 - November 1999
168

time, hour
Discharge, cfs
010 20 30 40
0
500
1000
1500
Simulated
Observed
Figure A.3.5. Storm 5 - December, 1999
Time, hours
Discharge, cfs
010 20 30 40
0
200
400
600
800
1000
Simulated
Observed
Figure A.3.6. Storm 6 - March 2002
169

Time , hours
Discharge, cfs
010 20 30 40 50
0
500
1000
1500
2000
2500
Simulated
Observed
Figure A.3.7. Storm 7 - May 2002
Time, hours
Discharge, cfs
010 20 30 40 50 60
0
500
1000
1500
2000
Simulated
Observed
Figure A.3.8. Storm 8 - June 2002
170

Reach Properties
Table A.3.1. Reach One
Discharge Volume Travel Time
(cfs) (Acre-foot) (hour)
100 27.42 2.52
250 51.42 2.05
500 94.69 1.81
1000 191.09 1.75
2000 422.08 2.23
4000 914.35 2.57
7000 1398.22 2.27
10000 1835.37 2.09
15000 2593.17 1.96
20000 3369.15 1.9
25000 4102.78 1.85
30000 4786.73 1.81
Table A.3.2. Reach Two
Discharge Volume Travel Time
(cfs) (Acre-foot) (hour)
100 26.71 2.99
250 37.93 1.72
500 53.86 1.23
1000 80.72 0.93
2000 166.91 0.95
4000 347.12 0.93
7000 655.66 1.01
10000 996.55 1.09
15000 1481.48 1.08
20000 1823.04 1.02
25000 2158.92 0.97
30000 2511.59 0.96
171

Table A.3.3. Reach Three
Discharge Volume Travel Time
(cfs) (Acre-foot) (Hour)
100 11.49 1.32
250 15.15 0.71
500 20.5 0.48
1000 31.44 0.37
2000 87.98 0.49
4000 259.84 0.56
7000 572.1 0.68
10000 827.52 0.73
15000 1257.24 0.83
20000 1492.83 0.76
25000 1809.15 0.76
30000 1926.97 0.68
Table A.3.1. Reach Four
Discharge Volume Travel Time
(cfs) (Acre-foot) (Hour)
100 39.28 4.03
250 58.65 2.57
500 82.91 1.88
1000 121.26 1.4
2000 186.73 1.08
4000 341.1 1
7000 588.5 0.99
10000 813.91 0.96
15000 1205.42 0.92
20000 1463.39 0.83
25000 1739.85 0.78
30000 2001.36 0.75
172

Table A.3.5. Reach Five
Discharge Volume Travel Time
(cfs) (Acre-foot) (Hour)
100 88.95 9.94
250 130.46 5.95
500 228.48 5.28
1000 360.36 4.21
2000 457.72 2.52
4000 769.35 2.19
7000 1306.06 2.13
10000 1794.46 2.08
15000 2677.56 2.07
20000 3209.88 1.86
25000 3534.6 1.64
30000 4592.11 1.78
Table A.3.6. Reach Six
Discharge Volume Travel Time
(cfs) (Acre-foot) (Hour)
100 186.45 21.45
250 195.57 9.02
500 210 4.92
1000 238.66 2.82
2000 301.58 1.77
4000 474.16 1.4
7000 786.51 1.32
10000 1210.83 1.42
15000 1904.16 1.48
20000 2368.76 1.39
25000 2602.68 1.22
30000 2989.46 1.17
173

Table A.3.7. Channel routing – Number of sub-reaches calculation
Ave.
Travel-
Time (hr) (1)
Selected
Travel-Time
(hr) (2)
No. of
Sub-
Reaches(4)
Corresponding-
Flow(cfs) (3)
Reach
ID Length
(ft)
1 18180 2.0675 2.25 250-25000 6
2 15320 1.24 1 500-30000 3
3 4165 0.6975 0.75 2000-30000 2
4 16689 1.4325 1 2000-30000 3
5 28227 3.4708 2 4000-30000 5
6 19329 4.115 1.5 2000-30000 4
(1) HEC-RAS ‘travel time ave.’ averaged over the 12 flow rates (100 cfs to 30,000 cfs).
(2) Travel time based on the most likely flow rates involved during a 100- year flood.
(3) The flow rate for which the selected travel time values are reasonable.
(4) Number of Sub-Reaches= (Selected Travel Time /1.5)/ (Time interval) where:
1.5 = Ratio of wave velocity/ average flow velocity; time interval =0.25 hrs. (15 min.)
A.3.2. Calculations for the Water Volume Indicator
The Rhawn St Stream Gauge is the only presently functioning stream gauge in
the PCW and is also the stream gauge with the longest period of record. Due to
the need for long periods of record to find the low-flow conditions this was the
only point at which the baseflow calculations were conducted.
Calculating the Natural Flow at the Rhawn St. Stream Gauge:
A previous study commissioned by the DRBC conducted by R.E. Wright
Associates determined the natural baseflow rates for the basic geological
formations in Southeastern Pennsylvania. The PCW is contained in the area
studied and so it is possible to use the results from this study to estimate the
natural baseflow at the Rhawn St. Gauge. Using Arcview 9.0, the areas of these
geologic formations were determined and these areas were multiplied by the flow
rate for each geologic formation from the R.E. Wright Study. The sum of these
values represents the natural baseflow at the Rhawn St. Gauge. The table below
shows these calculations.
Table A.3.8. Natural Baseflow
Calculated
Baseflow 25 yr low flow Area Rock Type
(mgd/sq. mile) (sq. mile) mgd
0.299 29.541 crystalline rocks 8.8328
0.299 0.446 unconsolidated 0.1334
0.299 0.028 unconsolidated 0.0084
0.299 0.591 unconsolidated 0.1767
0.289 0.507 carbonate rocks 0.1465
174

0.289 0.011 carbonate rocks 0.0032
0.189 18.404 Stockton Fm. 3.4784
0.154 0.002 diabase 0.0003
0.299 0.058 unconsolidated 0.0173
0.299 0.070 unconsolidated 0.0209
Total Calculated Natural Baseflow 12.8178 mgd
Calculating the Baseflow at the Rhawn St. Stream Gauge:
Daily Streamflow data was available from the USGS Rhawn Street Stream
Gauge for the period of from June 1, 1965 to the present. The stream gauge
data is freely available from the USGS-NWIS website
(http://waterdata.usgs.gov/nwis/)
A baseflow separation was then conducted using the daily streamflow data from
June 1, 1965 through September 30, 2003. This was done using a hydrograph
separation computer program based on the local-minimum method.
The mean daily baseflow for each year is presented below in millions of gallons
per day.
Table A.3.9. Mean Daily Baseflow
Baseflow
(mgd) Year Year
Baseflow
(mgd)
1966 12.87433739 1984 31.30797216
1967 26.87509365 1985 11.49848451
1968 17.75934976 1986 17.02884794
1969 16.80829931 1987 20.36145059
1970 22.44521688 1988 15.61943228
1971 24.02986678 1989 26.67228037
1972 38.22665225 1990 27.09586725
1973 41.93693342 1991 19.75784884
1974 30.1283519 1992 17.62926179
1975 35.48657303 1993 24.46359038
1976 21.51855848 1994 28.23392992
1977 19.77640883 1995 15.7021773
1978 31.43029373 1996 31.58958812
1979 38.03735947 1997 23.182198
1980 22.95256525 1998 19.89273682
1981 16.39900948 1999 17.82220466
1982 24.34030943 2000 25.12756324
1983 32.2240425 2001 23.77671553
These values were then graphed to show the recurrence intervals of the
baseflows. Figure A.3.1. shows the recurrence interval of the various baseflows
at this point on the Pennypack. Where the red lines cross is the point that
175

represents the 25-year low flow event, or the low flow event that has a one in 25
chance of occurring in any given year. This is the lowest flow event that can be
determined given the limited period of record for the data. According to the chart
the estimated 25-year low flow is approximately 12 mgd.
At this point the difference between the measured baseflow and the natural
baseflow appears to be slight, only ~0.8 mgd. However, the Upper Moreland –
Hatboro Sewage Treatment Plant discharges directly into Pennypack Stream and
much of the water that this plant discharges is drawn from outside the watershed,
falsely inflating the current baseflow. To determine the amount of outside water
entering the Pennypack at the plant, the amount of water that is drawn from
within the Pennypack Watershed was found and subtracted from the discharge.
What remained was water that had been taken from outside the Pennypack
Watershed and is being discharged into the Pennypack. The table below shows
the calculations.
Figure A.3.9. Pennypack Baseflow at the Rhawn St, Gauge
The mean daily discharge from the plant is 7.173 mgd.
7.173 mgd – 1.9022 mgd = 5.2708 mgd
Thus the baseflow of the Pennypack Stream at the Rhawn St. Gauge is being
overstated by 5.2708 mgd. The more realistic figure is
12 mgd – 5.2708 = 6.7292 mgd.
176

Thus the measured 25-year low flow baseflow as a percentage of natural 25-year
low flow baseflow is 6.7292 / 12.8178 = 52.50%
Table A.3.10. Imported PCW Water
Withdrawal Site MGD
HATBORO BORO AUTH WELL #1 0.0000
HATBORO BORO AUTH WELL #2 0.0000
HATBORO BORO AUTH WELL#12 0.0492
HATBORO BORO AUTH WELL#13 0.0000
HATBORO BORO AUTH WELL#14 0.1051
HATBORO BORO AUTH WELL#15 0.0357
HATBORO BORO AUTH WELL#16 0.0000
HATBORO BORO AUTH WELL#17 0.2040
HATBORO BORO AUTH WELL#18 0.0000
HATBORO BORO AUTH WELL#20 0.2516
HATBORO BORO AUTH WELL#21 0.0378
HATBORO BORO AUTH WELL#3 0.0000
HATBORO BORO AUTH WELL#6 0.0761
HATBORO BORO AUTH WELL#7 0.0000
HATBORO BORO AUTH WELL#8 0.1448
HATBORO BORO AUTH WELL#9 0.1206
HORSHAM TWP WATER AUTHORITY WELL#1 0.0433
HORSHAM TWP WATER AUTHORITY WELL#10 0.0573
HORSHAM TWP WATER AUTHORITY WELL#2 0.1285
HORSHAM TWP WATER AUTHORITY WELL#20 0.2166
HORSHAM TWP WATER AUTHORITY WELL#22 0.3231
HORSHAM TWP WATER AUTHORITY WELL#26 0.0000
HORSHAM TWP WATER AUTHORITY WELL#5 0.0000
HORSHAM TWP WATER AUTHORITY WELL#6 0.0582
HORSHAM TWP WATER AUTHORITY WELL#9 0.0503
Total 1.9022
A.3.2. Calculations for the Water Quality Indicator
The PWD Report provided chemical levels for many common pollutants for each
of their 20 sampling sites located along the Pennypack Creek. The average
values for each site are shown below. The units are either in milligrams (mg) per
liter (L) or colony forming units (cfu) per 100 milliliters (mL).
Targets for water quality for these contaminants were determined using
information from the State of Massachusetts Division of Water Pollution Control
and Lehigh University. These targets were for bodies of water designated as an
excellent habitat for fish, other aquatic life and wildlife. These values are an
approximation of an unpolluted, natural state.
177

Table A.3.11. PCW Chemical Measurements
Alkalinity Dissolved
O2 Fecal
Coliform
Nitrate Ortho-
phosphate Suspended
Solids Station pH
(mg
CaCO3/L) (mg/L) (cfu/100mL) (mg/L) (mg/L) (mg/L)
PP01 61.00 10.96 3124.17 3.10 0.38 88.72 7.48
PP02 46.42 10.68 4760.00 2.08 0.10 89.79 7.60
PP03 64.00 10.39 5160.83 3.17 0.41 103.39 7.33
No
DataPP04 No Data No Data No Data No Data 95.17 7.57
PP05 66.92 10.25 2696.67 3.96 0.49 88.48 7.41
No
DataPP06 No Data No Data No Data No Data 95.03 7.50
PP07 74.00 9.59 2322.50 4.71 0.59 93.92 7.51
No
DataPP08 58.00 No Data No Data 0.28 92.34 7.39
No
DataPP09 No Data No Data No Data No Data No Data 7.80
PP10 76.00 10.28 2160.83 5.24 0.77 No Data 7.80
PP11 85.17 10.41 2068.33 2.53 0.10 91.40 7.52
PP12 46.67 10.83 3693.33 2.47 0.11 98.20 7.58
No
DataPP13 No Data No Data No Data No Data No Data 7.80
PP14 77.58 10.02 2055.83 6.53 1.15 No Data 7.70
No
DataPP15 No Data No Data No Data No Data 103.28 7.58
PP16 68.58 10.31 3425.83 3.50 0.23 No Data 7.80
PP17 85.17 8.61 3645.83 7.21 1.37 100.16 7.37
No
DataPP18 64.00 No Data No Data 2.34 96.84 7.31
No
DataPP19 No Data No Data No Data No Data No Data 7.70
PP20 75.50 10.10 3242.50 1.43 0.10 No Data 7.62
Table A.3.12. “Target” Stream Measurements
Alkalinity
Dissolved
O2
Fecal
Coliform
Nitrate Ortho-
phosphate Suspended
Solids pH
(mg
CaCO3/L)
(mg/L) (cfu/100mL) (mg/L) (mg/L) (mg/L)
100-200 6 200 1 .03 10 6.5-85
Target
The actual values were then transformed into percentages of the target. For
Alkalinity where the actual values were below the target the percentage was
calculated by dividing the actual value by 100mg (the lower end of the target
range). For Dissolved Oxygen where exceeding the target does not have a
negative impact, any value exceeding the target was treated as 100%. For pH
where all of the values fell within the target range each value was treated as
178

100%. For the other categories, where exceeding the target is the negative
condition the percentage of the target was calculated by dividing the target by the
actual value (an inverse percentage). The results are displayed in the table
below.
Table A.3.13. Chemical Measurements: % of Target Levels
Station Alkalinity Dissolved
O2 Fecal
Coliform Nitrate Ortho-
phosphate S.
Solids pH
PP01 61.00% 100.00% 6.40% 32.31% 7.83% 11.27% 100.00%
PP02 46.42% 100.00% 4.20% 48.00% 30.00% 11.14% 100.00%
PP03 64.00% 100.00% 3.88% 31.55% 7.41% 9.67% 100.00%
PP04 No Data No Data No Data No Data No Data 10.51% 100.00%
PP05 66.92% 100.00% 7.42% 25.27% 6.18% 11.30% 100.00%
PP06 No Data No Data No Data No Data No Data 10.52% 100.00%
PP07 74.00% 100.00% 8.61% 21.22% 5.12% 10.65% 100.00%
PP08 58.00% No Data No Data No Data 10.56% 10.83% 100.00%
PP09 No Data No Data No Data No Data No Data No Data 100.00%
PP10 76.00% 100.00% 9.26% 19.09% 3.92% No Data 100.00%
PP11 85.17% 100.00% 9.67% 39.52% 30.00% 10.94% 100.00%
PP12 46.67% 100.00% 5.42% 40.48% 26.35% 10.18% 100.00%
PP13 No Data No Data No Data No Data No Data No Data 100.00%
PP14 77.58% 100.00% 9.73% 15.33% 2.60% No Data 100.00%
PP15 No Data No Data No Data No Data No Data 9.68% 100.00%
PP16 68.58% 100.00% 5.84% 28.61% 13.03% No Data 100.00%
PP17 85.17% 100.00% 5.49% 13.87% 2.18% 9.98% 100.00%
PP18 64.00% No Data No Data No Data 1.28% 10.33% 100.00%
PP19 No Data No Data No Data No Data No Data No Data 100.00%
PP20 75.50% 100.00% 6.17% 100.00% 29.56% No Data 100.00%
67.79% 100.00% 6.84% 34.60% 12.57% 10.54% 100.00%
Mean
Each of these chemical factors was given an equal weighting, so that the final
water quality value is the mean of the category means. This value is 47.47%.
A.3.3. Calculations for the Biological Integrity Indicator
Calculating the Macroinvertebrate Biological Integrity
The Philadelphia Water Department (PWD) Report provided a Biological Quality
value for each of their 20 sampling sites located along the Pennypack Stream.
These biological quality values took the form of percentages and were based on
comparison to a stream with similar drainage area and geomorphologic
attributes, but with an unimpaired ecology. One hundred percent would indicate
unimpaired macroinvertebrate ecology and zero percent is severely impaired
macroinvertebrate ecology. These Biological Quality scores are presented
below.
179

Table A.3.14. Macroinvertebrate Biological Quality Scores
Station Biological Quality Station Biological Quality
PP01 0% PP11 46.67%
PP02 40% PP12 40%
PP03 6.67% PP13 0%
PP04 0% PP14 13.33%
PP05 0% PP15 20%
PP06 0% PP16 40%
PP07 0% PP17 0%
PP08 40% PP18 40%
PP09 13.33% PP19 40%
PP10 26.67% PP20 66.67%
Due to the relatively stationary nature of macroinvertebrate communities the
results for each point were only attributed to the subbasin immediately upstream
of the point. This basins attributed to each station are shown in map BIO1 and
the biological integrity score for each basin is shown in map BIO2. The scores
for these twenty subbasins were then averaged to yield an overall score for the
Pennypack. This score is 21.67% which according to the system devised by
PWD is at the bottom end of the Moderately Impaired Range.
Calculating the Fish Biological Integrity
The PWD Report also provided an Index of Biotic Integrity score for each
monitoring station at which they sampled the fish population. These scores are
on a range from 0 to 50, they were transformed into percentages by multiplying
them by 2. These values are all presented in the table below.
Table A.3.15. Fish Biological Quality %s
Biotic
Integrity
Fish are more mobile than macroinvertebrates and so it was determined that the
scores for the fish could be attributed to more than just the upstream basin. The
scores for each station were attributed to all the basins that were upstream of the
monitoring station and downstream of another monitoring station. Major
branches of the creek that were not directly monitored were not attributed any
data. Map BIO3 shows exactly which stations were attributed to which basins,
and map BIO4 shows the biological integrity attributed to those basins. Thirty
basins were assigned data from the monitoring stations, so the data from these
thirty basins were averaged to yield an overall Index of Biotic Integrity for the
Station Percentage Station Biotic
Integrity Percentage
PP01 34 68% PP14 28 56%
PP04 38 76% PP15 26 52%
PP05 38 76% PP17 24 48%
PP07 28 56% PP19 24 48%
PP09 32 64% PP20 24 48%
180

Pennypack. This score is 61.20%. This is considerably higher than the score of
yielded by the macroinvertebrates. In reviewing the PWD report it was clear that
the fish had greater biodiversity in the tidal areas whereas the
macroinvertebrates fared poorly there due to the unstable water levels. The tidal
nature of the lower portions of the Pennypack means that neither of these
populations is completely indicative of the biological integrity of the stream. In
the interests of providing a single score and due to this fact that neither
population is completely indicative it was decided to provide a weighted average.
Each overall score was multiplied by the number of basins it represented; the two
products were then added and divided by the total number of basins (50) to yield
a score of 45.39%.
Macroinvertebrates
21.67% x 20 = 4.33
Fish
61.20% x 30 = 18.36
18.36 + 4.33 = 22.69
22.69/50 = 45.39%
A.3.4. Calculations for the Impervious Surface Indicator
The Impervious Surface Data originated from Satellite photography. The satellite
data provided a percentage of impervious coverage for each 30m x 30m pixel.
For each pixel the percentage impervious was multiplied by the pixel size. This
yielded the amount of impervious land in each pixel. These values were summed
for each subbasin to obtain the amount of impervious land in each subbasin.
This amount of impervious land was then divided by the total area of in each
subbasin to yield the percentage of imperviousness for each subbasin. The table
below shows these calculations.
181

182
Table A.3.16. Sub-Basin Impervious Surface
Basin
ID Total Area
(sq. mi) Impervious
Area (sq. mi) Percent
Impervious Basin
ID
Total
Area
(sq. mi)
Impervious
Area (sq.
mi) Percent
Impervious
1 1.0671 0.1292 12.11% 25 1.0748 0.3073 28.59%
2 0.9925 0.2945 29.67% 26 1.4887 0.1146 7.70%
3 1.2543 0.3625 28.90% 27 0.9112 0.0918 10.07%
4 1.2179 0.5054 41.50% 28 1.4510 0.3101 21.37%
5 1.0823 0.5483 50.66% 29 0.9040 0.1716 18.99%
6 1.8176 0.5628 30.97% 30 0.7693 0.2250 29.24%
7 1.7223 0.8720 50.63% 31 0.7592 0.1307 17.21%
8 1.4274 0.5389 37.75% 32 0.9373 0.1134 12.10%
9 0.9286 0.3473 37.40% 33 1.2292 0.3932 31.99%
10 2.1699 0.8699 40.09% 34 1.4573 0.8006 54.94%
11 1.6467 0.6422 39.00% 35 0.7949 0.1817 22.86%
12 1.6117 0.4987 30.94% 36 0.3806 0.0562 14.76%
13 1.2037 0.6183 51.36% 37 0.4054 0.0190 4.70%
14 1.1684 0.3751 32.10% 38 2.7683 1.6770 60.58%
15 1.4356 0.2083 14.51% 39 0.7449 0.1913 25.68%
16 0.4486 0.0603 13.45% 40 0.5531 0.1411 25.51%
17 0.7539 0.0323 4.29% 41 1.4028 0.4342 30.95%
18 0.9433 0.3228 34.22% 42 0.9160 0.3477 37.96%
19 0.5261 0.0433 8.23% 43 0.9590 0.4747 49.50%
20 1.1634 0.0670 5.76% 44 1.1197 0.4651 41.54%
21 1.3354 0.2316 17.34% 45 1.6508 0.9157 55.47%
22 1.0150 0.1915 18.86% 46 1.8207 0.9611 52.79%
23 1.2703 0.0736 5.80% 47 0.7659 0.2754 35.96%
24 0.4394 0.0457 10.39% 48 1.0292 0.4859 47.21%
49 0.7920 0.5572 70.35%
The mean of these impervious surface values is the average impervious surface
value for the PCW, 29.67%.
i BASELINE ASSESSMENT OF PENNYPACK CREEK WATERSHED (2002-2003), Produced by the
Philadelphia Dept. of Water, Office of Watersheds. http://www.phila.gov/water/index.html
ii http://www.hylebos.org/Stream_Team/Macro_Definition.htm
iii from chapter 5 of the Stroud Water Research Center Report on New York’s Watersheds accessed at
http://www.stroudcenter.org/research/NYReport/
iv BASELINE ASSESSMENT OF PENNYPACK CREEK WATERSHED (2002-2003), Produced by the
Philadelphia Dept. of Water, Office of Watersheds. http://www.phila.gov/water/index.html

183
v National Soil Survey Handbook (NSSH), Part 618 - Soil Properties and Qualities, Natural Resources
Conservation Services (NRCS), http://soils.usda.gov/technical/handbook/contents/part618p2.html#35,
accessed on August 2, 2005.
vi Infiltration Rate is the rate at which water enters the soil at the surface and is controlled by the surface
conditions, as defined in National Soil Survey Handbook (NSSH), Part 618 - Soil Properties and Qualities,
Natural Resources Conservation Services (NRCS).
vii Transmission Rate is the rate at which water moves in the soil and is controlled by soil properties, as
defined in National Soil Survey Handbook (NSSH), Part 618 - Soil Properties and Qualities, Natural Resources
Conservation Services (NRCS).
1 Source: Philadelphia Water Department Web Site (http://www.phillywater.org)
ix BASELINE ASSESSMENT OF PENNYPACK CREEK WATERSHED (2002-2003), Produced by the
Philadelphia Dept. of Water, Office of Watersheds. http://www.phila.gov/water/index.html
x BASELINE ASSESSMENT OF PENNYPACK CREEK WATERSHED (2002-2003), Produced by the
Philadelphia Dept. of Water, Office of Watersheds. http://www.phila.gov/water/index.html
xi http://www.hylebos.org/Stream_Team/Macro_Definition.htm
xii from chapter 5 of the Stroud Water Research Center Report on New York’s Watersheds accessed at
http://www.stroudcenter.org/research/NYReport/
xiii National Soil Survey Handbook (NSSH), Part 618 - Soil Properties and Qualities, Natural Resources
Conservation Services (NRCS), http://soils.usda.gov/technical/handbook/contents/part618p2.html#35,
accessed on August 2, 2005.
xiv Infiltration Rate is the rate at which water enters the soil at the surface and is controlled by the surface
conditions, as defined in National Soil Survey Handbook (NSSH), Part 618 - Soil Properties and Qualities,
Natural Resources Conservation Services (NRCS).
xv Transmission Rate is the rate at which water moves in the soil and is controlled by soil properties, as
defined in National Soil Survey Handbook (NSSH), Part 618 - Soil Properties and Qualities, Natural Resources
Conservation Services (NRCS).
1 Source: Philadelphia Water Department Web Site (http://www.phillywater.org)