Geographical Information Systems, Decision Support Systems, and Urban
Stormwater Management
James P. Heaney, David Sample, and Leonard Wright
University of Colorado
Boulder, Colorado
Final Report to the US Environmental Protection Agency
Edison, NJ
1 This report was prepared by the University of Colorado under Cooperative AgreementNo. CZ826256-01-0 with the EPA. The
information presented does not necessarily reflect the views of the Agency, and no official endorsement should be inferred. The
mention of trade names or commercial products does not imply endorsement by the United States government.
-------
Table of Contents
Abstract v
1.0 Introduction 1
2.0 Literature Review 2
2.1 Overview of Sources of Reviewed Literature 2
2.2 GIS as a Spatial Database for Urban Stormwater Modeling 2
2.2.1 GIS as a pre-processor for urban stormwater models 3
2.2.2 GIS as a post-processor for urban stormwater models 4
2.2.3 GIS used to estimate spatial input parameters 4
2.2.4 GIS used to estimate non-point source pollutant loads 5
2.3 Integration of GIS and Hydrologic Time Series 5
2.4 Integration of GIS and Urban Stormwater Models 6
2.5 Management Evaluation Using Integrated GIS and Urban Stormwater Models 7
2.6 Trends in the Integration of GIS and Urban Stormwater Modeling 8
3.0 Summary of Available GIS Urban Stormwater Modeling Software 9
3.1 SWMM and EPA Windows SWMM 11
3.2PCSWMM'98andPCSWMMGIS 11
3.3 XP-SWMM by XP Software (Also Available as Visual Hydro by CAiCE) 12
3.4SWMM-DUET 13
3.5 DHI Software 13
3.5.1 MIKE SWMM 13
3.5.2 MOUSE and MOUSE GIS 13
3.6 Wallingford Software-Hydro Works and Info Works 16
3.7 Summary 17
4.0 Future Urban Stormwater Modeling in a DSS Environment 19
4.1 State Information 21
4.1.1 GIS 21
4.1.2 Time series 22
4.1.3 Relational database 23
4.2 Process Information-Simulation Tools 28
4.3 Evaluation Tools 28
4.4 Overall DSS for Water Management 28
5.0 Application of GIS and DSS to Micro Storm Analysis 31
5.1 Spatial Scale and GIS-Stormwater Modeling 32
5.2 Description of Happy Acres Case Study GIS 37
5.3 Simulation Tools for Hydraulic Design 44
5.4 Simulation Tools for Hydrologic Analysis 49
5.4.1 Hydrologically functional landscaping 49
5.4.2 Determination of runoff volumes using NRCS method 52
5.4.3 Breakdown of calculated volumes per function 52
5.5 Simulation Tools for Cost Analysis 55
5.6 Optimization of Control Options for Happy Acres 59
5.7 Decision Support Systems and the Happy Acres Case Study 61
6.0 Summary and Conclusions 62
6.1 Summary 62
6.2 Conclusions 62
7.0 References 65
Appendix: Happy Acres Database 72
-------
List of Figures
Figure 3.1: PCSWMM output 12
Figure 3.2: Visual Hydro 14
Figure 3.3: Mouse GIS user action 15
Figure 3.4: System response to user action, Mouse GIS 15
Figure 3.5: Info Works from Wallingford Software 16
Figure 4.1: DSS structure and components 20
Figure 4.2: Relational database query example in Arc View using water use data 25
Figure 4.3: Spatial results for example query from figure 4.2 26
Figure 4.4: Query results output to Excel using Avenue script tool and Microsoft DDE 27
Figure 4.5: CU-CADSWES DSS 29
Figure 4.6: Danish Hydraulic Institute DSS, based on integrated water resources modeling 30
Figure 5.1: Proposed DSS for micro storm analysis 32
Figure 5.2: BASINS dataset for Boulder, Colorado 34
Figure 5.3: Arc View coverage of Boulder, Colorado 35
Figure 5.4: City of Boulder Arc View GIS coverage for University Hill neighborhood, Boulder, Colorado 36
Figure 5.5: AutoCAD file for University Hills neighborhood, Boulder, Colorado 38
Figure 5.6: AutoCAD coverage for study area 39
Figure 5.7: Study area topography 40
Figure 5.8: Study area land use 41
Figure 5.9: Study area soils 42
Figure 5.10: Study area sewer network 45
Figure 5.11: Conventional storm drainage 50
Figure 5.12: Illustration of hydrologically functional landscape 51
Figure 5.13: Allocation of available storage for initial abstraction and land use 55
in
-------
List of Tables
Table 3.1: Summary of available urban stormwater modeling software with GIS linkages 10
Table 3.2: Characteristics of urban storm stormwater models 18
Table 5.1: Available BASINS data attributes 33
Table 5.2: Minimum horizontal accuracy and example features for various map scales in urban areas 34
Table 5.3: Mix of land uses in Happy Acres 37
Table 5.4: AutoCAD layers for study area 43
Table 5.5: Right of way characteristics 43
Table 5.6: Lot characteristics for residential parcels 44
Table 5.7: Aggregate characteristics for commercial, apartments, and schools 44
Table 5.8: Sewer network design hydrology 46
Table 5.9: Sewer network design hydraulics 47
Table 5.10: Sewer network design cost 48
Table 5.11: Initial abstraction as a function of curve numbers, CN. 49
Table 5.12: SCS hydrologic classifications, and calculation of unit storage values, 1/99$ 53
Table 5.13: Calculation of developed and predevelopment stormwater volumes for Happy Acres 54
Table 5.14: Land valuation for medium density lot, 1/99$ 56
Table 5.15: Cost analysis of landscaping for medium density lot, 1/99$ 57
Table 5.16: Calculation of unit costs for controls, including opportunity costs for land, 1/99$ 58
Table 5.17: Results of LP optimization-land use allocation by function (includes opportunity costs) 60
Table 5.18: Least-cost LP solutions for land Use/BMP options (including land costs) for Happy Acres 61
Table A-l: Parcel attributes 73
Table A-2: Right of way attributes 78
-------
Abstract
This report reviews the application of Geographic Information System (GIS) technology to the
field of urban stormwater modeling. The GIS literature is reviewed in the context of its use as a
spatial database for urban stormwater modeling, integration of GIS and hydrologic time series,
and integration of GIS and urban stormwater models (from both a software and management
perspective). The available urban stormwater modeling software is reviewed and discussed with
respect to their GIS integration capabilities. Decision Support Systems (DSS) are reviewed with
respect to their integration with GIS, and their applicability to urban stormwater management
problems. A simplified neighborhood scale DSS is presented that includes a GIS, a database, a
stormwater system design template, and an optimization capability for screening alternatives.
The area and soil based NRCS method is used for calculating runoff from GIS information.
Using economic analysis that compares the costs of controls, including the opportunity cost of
land for land intensive controls, the optimal selection of Best Management Practice (BMP)
controls was accomplished by use of a linear programming (LP) method. The intent of this
presentation is to provide an example of the types of problems that become possible to explore
with the application of DSS and GIS technology on a small scale. This field is evolving rapidly,
and warrants carefully targetted research efforts, particularly at developing nonspecific software
tools that aid in integrating existing models.
-------
1.0 Introduction
A mathematical model of an urban hydrologic response to precipitation usually requires
extensive data due to the complexity of surfaces, flow paths and conduits found in developed
locales. Many of these data are geographic in nature; e.g., geographic boundaries of the
hydrologic basin provide boundary conditions of the mathematical model. Therefore the
marriage of mathematical stormwater models and geographic information systems (GIS) is a
natural development of simulation and database technology. The relationship between urban
stormwater models and GIS may take many forms. This is apparent from the nearly 50 journal
articles, conference proceedings and internet reports surveyed for this review of recent literature.
The relationship between GIS and urban stormwater models may be distinct, where the GIS
functions as a separate pre- and post-processor; or the distinction may be blurred, where the
model is seamlessly integrated to the GIS.
The purpose of this report is to accomplish several tasks. In chapter 2 a review of technical
literature is performed to determine how GIS is being used in the field of urban storm stormwater
modeling. Next, in chapter 3, the predominant urban stormwater models are reviewed within the
context of the taxonomy developed in chapter 2. Then, in chapter 4, looking at the future
directions of urban stormwater models, Decision Support Systems (DSSs) are described. DSS is
now being used extensively for river basin modeling, particularly in the hydropower context.
This type of system lends itself to unstructured problems where data integration is a key to
evaluation of the problem. The various components of DSS including models, database
structure, GIS, optimization, and time series management are discussed. A process level DSS is
developed for a textbook subdivision in chapter 5. This DSS contains a GIS, including graphic
features and a relational database, a system simulator, and an optimizer. Stormwater design
templates were created using Excel spreadsheets, paralleling the design problem from the
textbook. Next, GIS data were utilized in a simple hydrologic model using the NRCS (National
Resources Conservation Service) method. This data was combined with unit cost data into a
linear programming model (LP) in order to develop the least costly mix of BMP controls that
maintain the same initial abstraction after development as before. Suggestions for further
improvement of the DSS are made by comparison of the DSS structure with those found in
chapter 4. Finally conclusion are presented in chapter 6.
-------
2.0 Literature Review
2.1 Overview of Sources of Reviewed Literature
The GIS literature is broad, due to the wide variety of areas that utilize geographic data.
Likewise, the literature describing GIS applications in water resources is itself very broad.
However, much of this work in water resources has been in the area of natural hydrology and
large-scale, river-basin hydrology. GIS has a long history in this area due in large part to the
early availability of remotely sensed spatial data suited for this purpose. A good overview of the
concepts of GIS and database technology and their application within the field of natural systems
hydrology is found in Singh and Fiorentino (1996).
The use of GIS in modeling urban stormwater systems has been more limited due to the need for
large, expensive and detailed spatial and temporal databases, along with the fact that many
computer tools used in urban stormwater modeling are not easily amenable to integration with
GIS. However, as local data gathering efforts have increased and software integration has
evolved, the use of GIS in urban stormwater is now widespread. Shamsi et al. (1995) estimate
that more than 70% of the information used by local governments is georeferenced. Much of
this information has been, or will be, transferred to a digital format, usually a GIS.
Recent literature was found in several distinct fields. From the water resources field, recent
conferences focusing on urban stormwater have several papers on GIS. Proceedings from two
European conferences on urban stormwater by Butler and Maksimovic (eds. 1998), and Seiker
and Verworn (eds. 1996), have a wealth of current information on GIS. The American Water
Resources Association (AWRA) has sponsored conferences specific to the use of GIS in water
resources, such as Harlin and Lanfear (1993) and Hallam et al. (1996). These reports have
sections devoted to urban stormwater, of which modeling is a recurring theme. Significant
literature in this area was also found on the internet. The Center for Research in Water
Resources at The University of Texas at Austin has a large online library of reports and papers
on the use of GIS for hydrologic research, some of which concerns the modeling of urban areas
(University of Texas, 1998).
Other resources were found in the GIS field. One software provider, Environmental Systems
Research Institute (ESRI), hosts a large annual international user conference. The proceedings
for these conferences are located on the internet at http://www.esri.com (ESRI 1998). The
International Association of Hydrological Sciences (IAHS) publishes the proceedings from its
many conferences, some of which have dealt specifically with the integration and application of
GIS and water resources management (e.g. Kovar and Nachtnebel 1996). Other IAHS
conferences have focused on applications, which usually have several papers on using GIS for
that application. For example Simonovic et al. (1995) edited "Modeling and Management of
Sustainable Basin-Scale Water Resource Systems", proceedings from a 1995 conference in
Boulder, CO. which contained several papers on GIS and model integration.
2.2 GIS as a Spatial Database for Urban Stormwater Modeling
The most basic role a GIS can play in the modeling of urban stormwater is that of a simple pre-
processor of spatial data. As a pre-processor, GIS may simply store geographic information in a
database, or it may be used to calculate model-input parameters from stored geographic data.
-------
Frequently data are exported from the GIS in a file format consistent with a model-input file. As
a post-processor, GIS may be used to map water surface elevations, concentrations, etc., or to
derive spatial statistics based on model output. Shamsi (1998) describes the batch transfer of
data from a GIS to SWMM as the interchange data. The GIS and SWMM are operated
separately, with no direct interlink. The GIS is used to extract data required by SWMM from the
spatial database into a file compatible with a SWMM input file. A recurring theme in recent
literature focuses on the ability to get the most out of data by assuring that information tools are
consistent. This idea has been termed "hydroinformatics" and is especially prominent in the
recent European literature (Fuchs and Scheffer 1996).
2.2.1 GIS as a pre-processor for urban stormwater models
Many municipalities store general spatial information in a GIS, and the information is used for a
wide variety of purposes and functions within the institutional framework. VanGelder and
Miller (1996) describe a typical use of GIS as a spatial database for modeling stormwater from a
municipal airport. Detailed georeferenced data were used in conjunction with maintenance data
to develop an operation and management schedule as well as to link node information needed to
create a SWMM EXTRAN model. Pryl et al. (1998) use a GIS to export details of the urban
stormwater network to a hydraulic simulator for Prague in the Czech Republic. The Danish
Hydraulic Institute (DHI) program Model Of Urban SEwers (MOUSE) was used to simulate
various scenarios for development of an urban stormwater master plan. Rodriguez et al. (1998)
used a GIS to study stormwater characteristics of an urban area in Nantes, France. This study
used the urban land parcel as the base hydrologic unit of a detailed hydrologic model, as opposed
to the more typical basin defined by topography and the layout of the stormwater network. A
detailed water budget was performed around the owner-defined parcel. This physically based
hydrologic model was then used with the stormwater network to analyze the behavior of urban
catchments under a wide variety of storm events. The idea of using small hydrologic units based
on land ownership for urban stormwater modeling is ideally suited for GIS applications and is
useful when simulating the effect of management decisions made at the parcel level.
Sotic et al. (1998) began a preliminary design of CSO facilities in Kumodraz, Yugoslavia with
paper maps. Existing paper maps and other data were used to create a GIS, which in turn was
used to aid in the design and analysis of the CSO system. This "hydroinformatic" approach
consists of developing a set of tools to collect and process data in a consistent manner. The
attention to consistency in data transferability is to assure that the greatest value is achieved from
the dataset. In this case, the GIS was used to integrate a Digital Elevation Model (DEM), the
street network, and the sewer network; then this information was transferred to the BEAMUS
hydraulic simulation model (Sotic et al. 1998). A similar hydroinformatic approach is described
for the town of Pilsen in the Czech Republic by Hora et al. (1998). Beginning with paper maps,
a GIS was built from the ground-up. The complete process is described, ending with an
information tool that was used to create a hydrodynamic model of the sewer system, store
monitored flow and rain data, evaluate current hydraulic sewer capacity and evaluate the
feasibility of alternative sewer developments.
Barbe et al. (1993) integrate data transfer from a GIS and a SCAD A system to a SWMM model
of the Jefferson Parish stormwater stormwater system in Louisiana. The SWMM RUNOFF
block was used to simulate the hydrologic runoff characteristics of the area. Geospatial data
were transferred from the GIS to the SWMM RUNOFF data file. Similarly, the EXTRAN block
-------
was used to simulate the pipe network, and the network connectivity was transferred from the
GIS to the SWMM EXTRAN data file. Time series data from 150 monitoring sites were
transferred from a SCADA system to the SWMM model for calibration purposes.
2.2.2 GIS as a post-processor for urban stormwater models
GIS may also be used to accept model output. Xu et al. (1998) describe a mixed land use
hydrologic model that uses GIS as a pre- and post-processor of model information. For this
application, the model output of time series of simulated flows may be depicted dynamically
through an Arc View interface. Sorensen (1996) describes a typical use of GIS to present model
output, that of depicting flood inundation maps from the GIS. MIKE GIS is a modeling tool
from DHI that interfaces between Arclnfo or Arc View and MIKE, a flood assessment model.
First developed to study flood management in Bangladesh, MIKE GIS uses both the maximum
flood extent and the time series of flooding to analyze expected damages from peak inundation
and the duration of inundation (Sorensen 1996). A key element to this work is that the GIS is
used for more than mapping model output, but that spatial analysis is done with the GIS that adds
to the information gained from the model output alone.
Shamsi (1998) discusses the difference between transferring data files between Arc View and
SWMM and creating an interface that uses SWMM output as a spatial coverage layer in a GIS.
This "interface method" (as opposed to the interchange method described above) involves
creating a SWMM menu within Arc View. Pre- and post-processors of SWMM input and output
files create input files, read output files, and join and unjoin data files (Shamsi 1998). These
options are made available in Arc View; however SWMM is run separately from Arc View
(Shamsi 1998).
2.2.3 GIS used to estimate spatial input parameters
One of the most important hydrographic features of an urban surface is impervious area.
Fankhauser (1998) describes a method to estimate impervious area from color infrared aerial
photographs and orthophotos. These images have a ground resolution of 25 to 75 centimeters. A
raster based GIS, IDRISI, was used to estimate impendousness to within 10% of the value
determined manually for an entire basin. However, the deviation for individual catchments was
much higher. For this reason, this method was recommended only for large project areas where
the high costs of parameter estimation could be justified.
Olivera et al. (1996) use GIS to calculate hydrographic properties of terrain for non-point load
estimation. Flow paths calculated from paths of steepest descent are used to calculate flow
properties of basins. Cluis et al. (1996) use topographic data and GIS functions to derive
important hydrographic characteristics of the terrain such as overland flow paths in a raster based
format. Mercado (1996) describes the use of detailed spatial information in the creation of a
stormwater model in Tallahassee, FL using XPSWMM software. Scanned and georectified
black and white aerial photography was used as a background with other GIS based data,
including two foot contour elevations, streams, buildings, roads, etc. A DEM was created in
Arclnfo, and the Triangulated Irregular Network (TIN) and Grid functions were used to define
areas of high slope and erosion potential, flow gradients and very accurate subbasin delineation
(Mercado 1996).
-------
Herath et al. (1996) used high-resolution raster data sets to develop a distributed GIS-based
urban hydrologic model. Data sets included 50 m x 50 m and 20 m x 20 m land use grids;
1:25,000 plans were used to develop imperviousness by land use, a 50 m x 50 m DEM,
population density, water supply data, and rainfall. Herath et al. (1996) integrated the hydrologic
model with the GIS, by writing the numerical simulation codes within the GIS, thus reducing
problems of data transfer. However, the computational time was felt to be too high for practical
use due to inefficiencies of performing the hydrologic simulation within the GIS (Herath et al.
1996).
Olivera et al. (1998) developed a GIS-based preprocessor for the new HEC-HMS model
developed by the Army Corps of Engineers' Hydrologic Engineering Center. HEC-HMS is an
updated version of the popular HEC-1 hydrologic model. Olivera et al. (1998) describe HEC-
PrePro as a system of Arc View scripting programs and controls to extract hydrographic
information from spatial databases and prepare an input file to HEC-HMS. Using SCS curve
numbers and a DEM, HEC-PrePro delineates streams and basin boundaries, determines their
interconnectivity, and calculates parameters for each stream and basin (Olivera 1998). A benefit
to automating the calculation of hydrologic parameters that were traditionally estimated
manually is that results are reproducible, i.e., they are not dependent on the bias or experience of
the modeler.
2.2.4 GIS used to estimate non-point source pollutant loads
Using land use as a predictor of non-point source loads is a common use of GIS and hydrologic
models. Hauber and Joeres (1996) describe how a GIS was used to preprocess urban pollutant
loads for the Source Loading and Management Model (SLAMM). Similarly, Wright et al.
(1995) estimated nutrient loads from developed areas in the Onondaga Lake stormwater basin in
upstate NY with the GRASS GIS. These preprocessed loads were then routed from the
developed basins using the SWMM RUNOFF model.
Battin et al. (1998) describe the EPA's BASINS (Better Assessment Science Integrating Point
and Non-Point Sources) software, which integrates watershed point and non-point source load
data, the watershed hydrology program HSPF and the receiving water quality simulation
program QUAL2E. Olivera et al. (1996) describe the use of GIS to account for the spatial
variability of terrain in pollutant loading from a variety of land uses. The authors review the
strength of GIS in quantifying spatially distributing loads, and point out that this is a distinct
advantage over lumped models.
Scarborough and Yetter (1998) evaluated the Non-Point Source (NFS) module in BASINS 2.0
and found it to be a useful tool for evaluating NFS pollution. However, several problems were
found when evaluating a small watershed with the GIS data included with the program. The
most critical problem was that of coverage alignment (Scarborough and Yetter 1998).
Boundaries of land use and watershed boundaries did not match for the test case study, the St.
Jones watershed in Delaware.
2.3 Integration of GIS and Hydrologic Time Series
For the purposes of urban stormwater modeling, spatial data may usually be viewed as static.
Changes in geographic data are typically modeled in a scenario manner, e.g., a model run may be
-------
done for an undeveloped watershed, and then a developed scenario is performed using the same
hydrologic conditions. Hydrologic and meteorological data are commonly a time series of
discrete values. Therefore some attention must be paid to the integration of spatial and time
series data. This idea of consistency among data is key to the concept of hydroinformatics. Pryl
et al. (1998) describe the integration of time series with GIS to accomplish urban stormwater
master planning in the Czech Republic. Similarly, Rodriguez et al. (1998) use time series in their
analysis of the water budget based on parcel-level urban spatial data. Time series integration
was a key element in the work reported by Barbe (1996) in Louisiana. A large network of 150
monitoring locations fed a SCADA system with many time series data that were integrated with
GIS data and the SWMM model. An Oracle database was used to manage non-spatial data for
this project (Barbe 1993).
Da Costa et al. (1995, 1996) examined this problem in developing the Portuguese Water
Resources Information System. The integration of GIS with temporal data is described as one of
the great challenges of developing this system (da Costa et al. 1996). To accomplish this
integration, a database was developed using Oracle software to underlie the information system.
A special processing module was developed to interface time series data with the GIS. The GIS
portion used the ESRI Arc View software. Sorensen et al. (1996) describe the use of time series
in an application of MIKE GIS in Bangladesh. Sotic et al. (1998) describe the integration of
rainfall and flow time series with geographic data in a hydroinformatic manner in Yugoslavia.
Wolf-Schumann and Vaillant (1996) describe in detail the need for integrated time series with
georeferenced data. The development of TimeView, a time series management tool, is described
as adding a whole dimension (time) to spatial data. TimeView is integrated with Arc View, so
that a user can select a geographic feature in Arc View (e.g. a monitored manhole), and
TimeView returns a time series of measured data in graphical format.
2.4 Integration of GIS and Urban Stormwater Models
The linking of GIS and several hydrologic process models (beyond creating pre-processed data
files within the GIS) is examined by Charnock et al. (1996) and DeVantier and Feldman (1993).
Issues of differing scale properties and error propagation are addressed. The use of GIS as a
central hub of information, which is fed to several satellite process models, is favored over
coupling all the processes in one large program. Kopp (1996) addresses these same issues and
argues for more data standards to streamline hydroinformatics. Sponemann et al. (1996) explain
how a GIS can be shared among many varied users, e.g. gas utilities, water utilities, stormwater,
etc. thus maximizing the benefits derived from data collection and management. Greene and
Cruise (1995) developed an urban watershed modeling system using the SCS rainfall-runoff
methodology and GIS parcel attributes. Meyer et al. (19993) developed a raster based GIS for an
urban subdivision in Ft. Collins, Colorado and found that the results compared favorably with
non-GIS hydrologic studies of the same area.
Shamsi (1998) distinguishes three forms of information exchange between Arc View and
SWMM. The interchange and interface methods are described above, and involve the transfer of
information between Arc View and SWMM, which are run independently. Shamsi (1998)
defines the third method, integration, as the most advanced of the methods. SWMM is used as
the hydrologic and hydraulic simulator and is executed from within Arc View. This form of
integration includes performing all program tasks within Arc View: creating SWMM input data,
-------
editing data files, executing SWMM, and displaying output results (Shamsi 1998). Integration as
defined by Shamsi (1998) combines a SWMM Graphical User Interface (GUI) with a GIS to
provide a complete data environment. The advantages of a GUI were advanced by Shamsi
(1997), who provided a summary of software features and needs for SWMM interfaces.
Feinberg and Uhrick (1997) discuss integrating an infrastructure database in Broward County,
FL with a GIS and water distribution and wastewater models. The HydroWorks model is used to
simulate the wastewater collection system, with close integration with the database of
infrastructure characteristics and the GIS. Refsgaard et al. (1995) describe the evolution of
DHI's land process hydrologic model, SHE, and its extensive use of GIS. Ribeiro (1996)
describes the use of a raster-based GIS to interface with HSPF to analyze the effects of basin
urbanization. Hellweger (1996) developed an Arc View application using the Avenue scripting
language to perform the model calculations of USD A's hydrologic model TR-55.
Mark et al. (1997) use the MOUSE program from DHI to evaluate storm water in Dhaka, along
the banks of the Ganges and Bramaputra rivers in Bangladesh. Integration of GIS, time series,
and the hydraulic model were accomplished to better understand flooding characteristics.
Maximum inundation and duration of inundation were mapped using MOUSE and GIS. Shamsi
and Fletcher (1996) describe in detail the linkage of Arc View and SWMM for the City of
Huntington, WV. Arc View is shown to be a user-friendly environment to perform stormwater
modeling. Belial et al. (1996) studied partly urbanized basins using a linked GIS and hydrologic
model. The hydrologic model was based on a non-urban water budget, with modifications to
account for urbanization. The GIS was based on a DEM and raster-based land use data.
2.5 Management Evaluation Using Integrated GIS and Urban Stormwater Models
The integration of GIS, time series data, and an urban stormwater model is usually done to
evaluate management options. These options may be watershed-based, which would likely
include non-urban areas, or they may be local to the urban area.
Rodriguez et al. (1998) describe an integrated GIS and urban hydrologic model to evaluate small
storm hydrology for parcel level management decisions. Tskhai et al. (1995) use a GIS linked
with an optimization model to evaluate ecological and economic alternatives for the Upper Ob
River in the Altai region of Russia. While not strictly an urban runoff model in the traditional
sense, this project does link urban management decisions with an economic optimization model.
Makropoulos et al. (1998) focus on urban sustainability to evaluate stormwater systems.
Beginning with the idea that low energy solutions that control impacts at the source are more
sustainable, Makropoulos et al. (1998) demonstrate how a raster-based GIS (IDRISI) can be used
to integrate theoretical concepts and site specific spatial characteristics. The strength of GIS can
be used as a common ground between specialists and non-specialists to help them communicate
effectively. Belial et al. (1996) studied the effect of urbanization on a watershed using a linked
hydrologic model based on a DEM and a GIS. A water budget approach was used around each
raster unit to account for changes due to urbanization.
Mark et al. (1997) describe a detailed evaluation of flood management techniques in Dhaka,
Bangladesh, using MOUSE GIS. Xue et al. (1996) and Xue and Bechtel (1997) describe the
development of a model designed to evaluate the effectiveness of Best Management Practices
-------
(BMP's). This model, called the Best Management Practices Assessment Model (BMPAM),
was linked with Arc View to create an integrated management tool. This integrated model was
used to evaluate the pollutant load reduction potential of a hypothetical wet pond in Okeechobee,
Florida. Kim et al. (1998) used Arc View with an economic evaluation model and a hydraulic
simulator to evaluate storm sewer design alternatives. The hydraulic simulator was used to
generate initial design alternatives, which where in turn evaluated with an economic model. The
GIS was used to store spatial information, generate model input, and present alternative
solutions. The complete package of GIS, economic evaluation model, and hydraulic simulator
was termed a Planning Support System (Kim et al. 1998).
2.6 Trends in the Integration of GIS and Urban Stormwater Modeling
The trend towards a data-centric suite of evaluation tools is clear. The central idea behind the
European concept of hydroinformatics is that a consistent database is used for a variety of
purposes. The model is no longer the central unit driving the decision process. Neither,
however, has the GIS become the central data tool, due in large part to its inability to handle
temporal information effectively. Researchers who have paid equal attention to the model (the
processes), the GIS (the spatial data), and the temporal information (time series of hydrologic
processes) seem to have had considerable success. The integration of GIS and urban stormwater
models should therefore include integration with a database structure equipped to handle time
series. Several advanced applications have used a non-graphic database (e.g. dBase, Oracle,
Access) that is queried by both the GIS and the hydrologic/hydraulic models. While clearly an
evolving area, this approach seems to hold the most promise for the purpose of urban stormwater
decision support systems.
-------
3.0 Summary of Available GIS Urban Stormwater Modeling Software
As described in section 2, a useful taxonomy to define the different ways a GIS is used in urban
hydrologic and hydraulic modeling is presented by Shamsi (1998). The three methods defined
by Shamsi (1998) are data interchange, program interface, and program integration (Shamsi
1998). A fourth grouping was added for this report, the "intermediate program". Several
commercial modeling products feature a data management program to facilitate data transfer
between the GIS and a model. A short description is given below in order of increasing
sophistication.
Data Interchange: a batch process is used to transfer data to and from the model data set. For
example, the GIS may be used to calculate model input parameters e.g., catchment slope, or to
query an existing spatial coverage, such as land use. Then portions of the GIS query file can be
copied into a model-input file with no direct link between the GIS and the model. The model is
executed independently from the GIS, and portions of the output files may be copied back into
the GIS as a new spatial coverage for presentation purposes.
Intermediate Program: a data management program is used to transfer information between a
GIS and a model. This data management program is written specifically to import data from a
variety of common third party GIS software, and export to a model data set. Under certain
conditions this intermediate program could be defined as an interface, but generally it is not.
Program Interface: a direct link consisting of a pre- and post-processor is used to transfer
information between the GIS and the model. This process automates the data interchange
method. Model-specific menu options are added to the GIS. The model is executed
independently from the GIS, however the input file is created in the GIS. For example, in the
data interchange method, the user finds a portion of a file and copies it. An interface automates
this process, so that the pre- and post-processor finds the appropriate portion of the file
automatically.
Program Integration: while the interface method can't launch the model from the GIS, under
the integration method, the model and the GIS are together within one Graphical User Interface
(GUI). This represents the closest relationship between GIS and model, though "closest" does
not necessarily mean "best". It may be more efficient for a model to be independent from a GIS
in certain situations.
As noted elsewhere in this report, the development of a GIS for use in urban hydrologic and
hydraulic modeling is an expensive investment. Typically the most advanced tools are created
for advanced applications, where a full GIS is in place. For some applications, a DOS-based
model may still be the most appropriate. However, as more urban areas create GIS coverages,
the integration of modeling software and GIS software will become more useful and more
prevalent.
The Storm Water Management Model (SWMM) is the most widely used urban
hydrologic/hydraulic model in the US. In addition to SWMM, numerous other hydrologic
models were created in the US during the 70s including the US Army Corps of Engineers
Hydrologic Engineering Center "HEC" series of models (HEC-1 through 6). Two of the most
popular models, HEC-1 and HEC-2, have been updated and renamed HEC-HMS and HEC-RAS,
-------
respectively. These two models have been updated from the original DOS model with a MS
Windows based GUI. HSPF, and ILLUDAS are other models developed in the 70's, which are
still used today.
The original SWMM model, available at no charge from the US EPA (at the following website:
http://ftp.epa.gov/epa_ceam/wwwhtml/ceamhome.htm) was written in Fortran-77 for mainframe
computers (Huber and Dickinson 1988). The model was originally written during the 70s, with
several major improvements made in the early 80s. It has continued to evolve since being ported
to personal computers. Version 4.31 is the latest release; however numerous other modifications
exist to the program (e.g. UD-SWMM, a modification of SWMM by the Urban Stormwater and
Flood Control District of Denver, Colorado). SWMM runs in MS-DOS in a text-based
environment, which is not the user-friendly windows and graphical user interface (GUI) based
environment that is expected today. Despite these shortcomings, it has an active user community
within the United States.
Lack of funding support for SWMM during the 80s and 90s meant that the model had to be self-
sustaining. Interested parties such as local governments, consultants, and third party developers
added their own refinements to the model, with very little support from the federal government.
Because these refinements added value to the original program code, the developers started to
charge for these improvements. XP-SWMM (XP-Software 1998) and PCSWMM (CHI 1998)
are examples of this type of refinement. The SWMM user's listserver has developed into a self-
sustaining community of users. Information on accessing the listserver can be found at
http://www.chi.on.ca/swmmusers.html
During the 1980's, several models started to evolve in Europe. Two of them are HydroWorks,
from Wallingford Software in Great Britain, and MOUSE from the Danish Hydraulic Institute,
DHI, in Denmark. Unlike EPA SWMM, these models are proprietary.
These models are listed in table 3.1, with the addition of MikeSWMM, which is the result of a
recent collaborative effort between DHI and Camp, Dresser, and McKee (CDM). This product
uses the latest SWMM model engine available from the US EPA, and adds the MIKE GUI and
MOUSE GIS from DHI.
Table 3.1: Summary of available urban stormwater modeling software with GIS linkages
Product
HydroWorks/
Info Works
Mouse GIS
MikeSWMM
PCSWMM/GIS
XPSWMM
Model
Hyd reworks
Mouse
SWMM
SWMM
SWMM
Interface
Hyd reworks
Mike
Mike
PCSWMM
XPSWMM
Company/Source
HR Wallingford/
Montgomery Watson
Danish Hydraulic Institute/
Danish Hydraulic Institute/
Camp Dresser and McKee
Computational Hydraulics
International
CAiCHE
Website
www.wallingford-software.co.uk
www.dhi.dk
www.mikeswmm. com
www.chi.on.ca
www.xpsoftware.com
The following sections describe commercial and public domain products that are currently
available for urban hydrologic and hydraulic modeling. The above taxonomy is used to define
how each one handles information transfer between a GIS and the model. However, the reader is
10
-------
cautioned that while integration may be the most advanced method of using a GIS and model
together, it is not necessarily the best method for every application. For some applications
(especially when the GIS is incomplete, inaccurate, or both) different levels of manual operation
may be more appropriate. For example, a limited GIS may exist for an urban watershed, along
with very detailed and accurate CAD files. Certain commercial products (e.g. Visual Hydro by
CAiCE) can handle CAD drawings better than a product designed to run a pre-existing GIS. If
resources were not available to create a GIS, it would be appropriate to use a product suited to
the available data.
3.1 SWMM and EPA Windows SWMM
As stated previously, SWMM is a DOS based program developed under US EPA funding during
the late 1970's and early 1980's. There is no provision to link directly or indirectly with a GIS
other than through standard input text files. This is the most basic version of SWMM available.
This version of SWMM is important because it is in the public domain, and the source code is
readily available. The latest version of the DOS based SWMM can be found at
http ://www. ccee.orst.edu/swmm/
In 1994, the US EPA produced a Windows-based GUI for SWMM. This program (also
available at http://www.epa.gov/ost/SWMM_WINDOWS/) runs on Windows version 3.1, and is
therefore somewhat outdated. This program is also limited by the fact that the DOS based
SWMM engine is in a constant state of improvement by developers and users because the
Fortran source code is available. Unfortunately, the Windows SWMM program used the
SWMM engine available circa 1994, and the newer versions of the SWMM engine cannot easily
be substituted. Therefore the program has quickly become outdated, and has few users.
Windows SWMM could not be linked directly to a GIS program.
To use either of these programs with a GIS, the data-interchange method must be deployed to
transfer information from a GIS to an input file. The GIS may be used to store and estimate
model input parameters. The GIS could be queried for the needed values, and the values could
then be transferred to the input file. The level of automation to perform this task depends on the
user. It could be as simple as copying the needed values onto a Windows clipboard and pasting
them into the input file, or developing special queries from the GIS to create an input file
automatically.
3.2 PCSWMM '98 and PCSWMM GIS
PCSWMM-98 is a set of 32 bit applications designed to facilitate running SWMM. These tools
include an ASCII text editor, an animated hydraulic grade line plot, a chart wizard, an Internet
wizard, a batch file control, a rainfall analysis package, a bibliographic database, a sensitivity
analysis wizard, and a calibration wizard. The GUI allows files from many sources to be linked,
including those accessed across Intranets and Internets. PC-SWMM GIS is an optional tool that
works directly with CAD or GIS files in constructing a link-node database for running the model
from the existing data sources. After importing the data from a CAD or GIS file, an aggregation
tool allows semiautomatic construction of a simplified link-node model. This reduces model
complexity, and provides a direct analog to the aggregated catchment concept in the original
SWMM. An example of output from a PC-SWMM example run is found in figure 3.1.
11
-------
Fib Hdp
fls Jd*
ss
3
iiJ y^ iJ M5PH
Figure 3.1: PCSWMM output
(CHI, 1999)
PCSWMM GIS is an intermediate data management program designed to accept data from a GIS
package and transfer it to a SWMM input file. Because it is a more sophisticated method of
transferring information from a GIS to a model than the data-interchange method, but it is not an
interface as defined by Shamsi (1998), a fourth category was added to the taxonomy, that of the
intermediate program. PCSWMM GIS and PCSWMM'98 were developed by CHI in Guelph,
Ontario. According to the CHI website, (www.chi.on.ca). PCSWMMGIS does not perform any
parameter estimation calculations. It accepts geographic data from an external GIS, within
which the parameter estimation calculations and queries are performed. However, it does
perform tasks specific to SWMM modeling, such as performing geographic and hydrologic
aggregation calculations that are commonly done to simplify a SWMM model.
3.3 XP-SWMM by XP Software (Also Available as Visual Hydro by CAiCE)
XP-SWMM32 by XP Software (also included in Visual Hydro, by CAiCE) is a full 32-bit MS
Windows application. The program has been enhanced by the addition of a graphics database,
and an adaptive dynamic wave solution algorithm that is more stable than the matrix method
used in the original SWMM. The program is divided into a stormwater layer, which includes
hydrology and water quality, a wastewater layer, which includes storage treatment and water
12
-------
quality routing for BMP analysis, and a hydro-dynamic/hydraulics layer for simulation of open
or closed conduits. The user-friendly GUI is based upon a graphical representation of the
modeled system using a link-node architecture. An example of input and output processing in
Visual Hydro is found in figure 3.2. Because the links and nodes are set up on a coordinate
system basis, files can be translated between most CAD and GIS software systems. CAD or GIS
files can also be used as a backdrop for the system being modeled. However, since there is no
interface with a GIS, data interchange method must be used to transfer parameters (e.g., slope,
width, percent imperviousness, etc.) from a GIS to the model. However, the program can import
and export files from and to a GIS.
3.4 SWMM-DUET
SWMM-DUET is the only fully integrated application of a model into a GIS. It was developed
using Arclnfo and the native Arclnfo development language AML (Shamsi 1998). SWMM
DUET uses relational databases, both pre- and post-processors, and expert system logic to
integrate the SWMM environment and the graphical paradigm of Arclnfo (Shamsi 1998). Future
plans include an Arc View version of this product (Shamsi 1998).
3.5 DHI Software
3.5.1 MIKE SWMM
MikeSWMM is a proprietary GUI for SWMM from the Danish Hydraulic Institute and Camp,
Dresser and McKee, Inc. Mike SWMM can be integrated with a GIS system using Mouse GIS,
also available from DHI. Mike SWMM is a classified as an Arc View interface due to its ability
to link with the Mouse GIS program, which is described in the follow section.
3.5.2 MOUSE and MOUSE GIS
Mouse GIS is a module for MikeSWMM and Mouse users that also allows tight integration
between the GIS and the model database. Mouse GIS is an Arc View GIS application. Files do
not need to be translated and converted from the GIS to the model format. The DHI product for
stormwater modeling, Mouse, uses the Mike GUI within the MS Windows environment. Mouse
is a dynamic 32-bit model running in MS Windows that is capable of modeling any type or
combination of open or closed conduits and pressurized or gravity flows. An example of the
result of a simple query that illustrates the operating environment of Mouse GIS can be seen in
figures 3.4 and 3.5. Each object within Mouse GIS has database attributes that can be queried.
Mouse GIS is an interface between Arc View and the hydraulic pipe simulator, MOUSE. Mouse
is a sophisticated proprietary hydraulic model that is commonly compared to SWMM.
13
-------
Node - NODH2
•—• 655-15
Viewing Style
Font Size
.Numetic Piecision
Plotting Method
Data Shadows
Grid Lines
G(id In Front
Include Data Labels
Mark Data Points
Show Annotations
Maximize
Customization Dialog
Export Dialog
Help
or Help, press F1
I File Export Window Help
Node Data Table
Node Name
Ground Elevation
Invert Elevation
Maximum
X Coordina Y Coordinat
1960632.927
1960575.611
1960490.001
1960401.579
1960331.983
1960270.453
1534420.985
-1534496.662
-1534527.750
1534570.236
-1534637.243
-1534720.629
Freeboard
JS CAiCE - [CAICE -VISUAL HYDRO (C:\CAICE\SAMPLES\MYTOOLS\MYTDOLS1 (MYNET2) : MYNET2.XP1
X=1961073.372, Y=1535194.512 Hdr 1:10000
For Help, press F1
Figure 3.2: Visual Hydro
(CAiCHE, 1998)
14
-------
ioMfi InmnH E»rt»«
Figure 3.3: Mouse GIS user action
(www. dhi. dk/mouse/)
Figure 3.4: System response to user action, Mouse GIS
(www. dhi. dk/mouse/).
15
-------
3.6 Wallingford Software-HydroWorks and InfoWorks
HydroWorks and InfoWorks are companion products produced by Wallingford, Inc. of the UK.
Wallingford has taken a different approach to managing geospatial data. InfoWorks is designed
to import relational and geospatial data from third party software (e.g. Access and Arc View).
Once transferred to InfoWorks, the data is then used to create and run a HydroWorks model.
Hydroworks is an urban stormwater modeling system with a user friendly GUI. HydroWorks
uses a fully dynamic solution technique that solves backwater and unsteady open or closed
conduit situations.
InfoWorks performs GIS-type operations, and is designed to operate with HydroWorks, the
hydrologic and hydraulic simulator produced by Wallingford, Inc. While the relationship
between InfoWorks and HydroWorks may be defined as an interface or even fully integrated,
InfoWorks is not a GIS interface. An example of InfoWorks is shown in figure 3.5. Data from a
general use GIS product like Arc View would need to be imported into InfoWorks, much like the
PCSWMM GIS program from CHI.
Figure 3.5: InfoWorks from Wallingford Software
(HR-Wallingford, 1999)
16
-------
3.7 Summary
A summary of model and GIS features is presented in table 3.2. As described above, and
summarized in table 3.2, the problem of transferring geographic and hydrographic data between
a GIS and a simulation model has been handled several different ways by various software
developers. It may appear self evident that a tight integration between the hydraulic model and
the GIS is desirable. However, the question should be raised; how integrated should these two
types of software be? For example, should a GIS include a hydraulic model as part of a toolbox
within the GIS? This may, or may not, be desirable. Therefore it should not be assumed that
because SWMM DUET has integrated SWMM within Arclnfo that it is the best modeling tool.
For example, the expert GUI of XP SWMM may be more useful for a given application, despite
the fact that it does not interface directly with a GIS, nor does it have an intermediate data
management program. What is common among the recent software developments is a
transferability of fundamental database information. This theme is formerly known as a
Decision Support System (DSS). Under a DSS framework, neither the GIS nor the model are
"central" to the process. Both GIS and model serve satellite functions to a central master
database. A more fundamental look at this question is given in chapter 5.
The question "which model works best with GIS?" is impossible to answer. Depending on the
problem at hand, several products are designed to work with an existing GIS. The answer
largely depends on the state of information available. If an existing Arclnfo database is in place,
SWMMDUET would work well. Other products have used an information management
approach over GIS integration. This may be best suited for applications with disparate data
sources. Differences amongst hydraulic models may be more important. The DHI suite of
models may be appealing for certain applications. The organization of the Hydrolnfo/
HydroWorks or PC SWMM'98/PC SWMM GIS software may be best suited for other
applications. Each has unique and valuable features, and no recommendation is made in this
report for a specific software package.
The future evolution of both GIS and urban stormwater modeling, and their possible
convergence, appears to be centering upon object intelligence and smaller, programmable
component tools. For example, ESRI's stated goal of its next generation of programs (possibly
Arc View 4.0) is to rewrite and enhance its programs to use standard MS Windows routines that
can be called via dynamic link libraries (DLLs). An early example is the product called
MapObjects, which allows a programmer to insert a GIS-like application within a Visual Basic
or Visual C++ program, and make queries and Arc View-like functions upon GIS databases
without the Arc View program itself. Existing tools like Evolver, for nonlinear optimization, and
@Risk for Monte Carlo simulation are also available as DLLs (Palisade Corporation, 1998).
Urban stormwater modeling tools appear to be evolving into using similar tools as they take
advantage of existing libraries such as spreadsheet and graphic add-ons, (e.g., Visual Hydro,
PCSWMM), and are rewritten in object-oriented programs such as Visual C++, Visual Basic, or
Java. The future convergence of GIS and urban stormwater modeling will probably utilize these
common sets of tools to take advantage of the easier interoperability. Such tools make
integration of these disparate components possible into an integrated Decision Support System,
the subject of the next chapter.
17
-------
Table 3.2: Characteristics of urban storm stormwater models
Software
SWMM Products:
EPASWMM
Windows SWMM
PCSWMM'98/
PCSWMM CIS
Visual Hydro/XP-SWMM
SWMMDUET
MIKE SWMM/
DHI Products
MOUSE, Mike-11
MOUSE CIS
HydroWorks/
InfoWorks
Data
Interchange
X
X
X
Intermediate
Program
X
X
CIS/Model
Interface
X
X
CIS/Model
Integration
X
Advantages/Disadvantages
DOS based
Based on SWMM circa 1994
PCSWMM GIS is a data management
program
Imports CAD, GIS files
Arclnfo based
Arc View based (via MOUSE GIS)
Arc View
InfoWorks is a data management program
for geographic and relational databases.
18
-------
4.0 Future Urban Stormwater Modeling in a DSS Environment
Much of the data used in distributed (and lumped-distributed) hydrologic modeling requires
some level of spatially referenced information. Conversely, purely lumped hydrologic models
by definition do not require data to be spatially referenced. This report is focused on lumped-
distributed models and the type of information required to use them. Lumped-distributed models
are typically defined by sub-catchments within a Stormwater basin. The hydrologic parameters
are lumped within each sub-catchment. On the basin scale, however, the discretization among
sub-catchments provides spatial distribution. Some of the data used in these distributed models
may be more efficiently stored in forms other than GIS spatial database structures (Reitsma et al.
1996). For example, relational data models may be more efficient in storing certain attribute
information. Time series are another form of data commonly used in hydrologic modeling.
These data are frequently stored in a relational form, despite some shortcomings of this structure
for time series (Reitsma et al. 1996).
Besides model input, decision-makers frequently require analysis of model output, and the
analysis may not necessarily be spatially referenced. For these reasons, future model
development should not only focus on the role of GIS in modeling, but on how all information is
stored and used.
Due to the complexity of tools required to fully support a complex hydrologic decision, a system
made up of more than a GIS and simulation model is needed. An integrated suite of tools is
required to manage information. These tools are referred to as Decision Support Systems (DSS).
Although the model is important, much of the focus has shifted to the related needs of relational
database management, developing geographical information systems, and a sophisticated user
friendly interface, all combined in DSS. Figure 4.1 describes these necessary components of a
DSS (Reitsma et al., 1996). The evolution of DSS may be seen as a natural extension of
simulation models (e.g. SWMM, MOUSE, HydroWorks), GIS (e.g. Arc View, IDRISI, Arclnfo),
relational databases (e.g. Dbase, Oracle, Access) and evaluation tools (e.g. optimization
software). Reitsma (1996) define a DSS for water resources applications:
"Decision support systems are computer-based systems which integrate state
information, dynamic or process information, and plan evaluation tools into a
single software implementation."
In this definition, state information refers to data which represents the system's state at any point
in time, process information represents the first principles governing resource behavior, and
evaluation tools refer to software used for transforming raw data into information useful for
decision making. A simple representation of DSS components is shown in figure 4.1.
The GIS and the simulation model are only components of the DSS in figure 4.1. Future model
development should focus not only on GIS interfaces and integration with models, but should
include integration with a more complete management information system.. The view for future
model development should be broader than only GIS integration, because hydrologic decision
making requires more than just spatial information. In a DSS, the GIS only handles spatial data.
Spatially referenced information is only one form of state data that is relevant to hydrologic and
19
-------
hydraulic modeling. Time series and attribute data are also crucial to the analysis, and may be
handled poorly in a GIS database format designed to manage spatially referenced data.
A thorough background on DSSs and their application to reservoir decisions can be found in
Jamieson and Fedra (1996a), Fedra and Jamieson (1996), and Jamieson and Fedra (1996b).
These series of articles describe the conceptual design, planning capability, and example
application of the Water Ware DSS, a complex river basin DSS that combines a "GIS, a geo-
referenced database, groundwater flow, surface water flow, hydrologic processes, demand
forecasting, and water-resources planning" (Jamieson and Fedra 1996a). Reservoir operation
and management was one of the first areas of civil engineering in which DSSs were applied.
Because of the complicated decision criteria governing urban stormwater management, Davis et
al. (1991) studied a prototype DSS developed to analyze the impact of different catchment
policies. Driscoll (1993) developed a DSS to assist highway engineers in determining which
construction sites would contribute to a receiving water quality problem. Azzout et al. (1995)
discuss a DSS under development that would assist in determining the feasibility of alternative
techniques in urban stormwater management.
DSS
Evaluation Tools
-Multi Criteria Evaluation
-Visualization
-Status Checking
I
State Information
-Databases
-Geographic Information
Process Information
-(Simulation) Models
Figure 4.1: DSS structure and components
(Reitsma et al. 1996)
20
-------
The theme of the following sections is that the parts of a DSS are separate but complementary.
They should be able to transfer information to needed process models and evaluation tools
without complications. There is no need to house everything under one umbrella, i.e. to perform
all modeling tasks in an integrated GIS/hydraulic model.
4.1 State Information
In one form of DSS, state information drives the system. This is a "data-centric" view, and it
differs from the more traditional model-based analysis commonly used in urban water resources
modeling. This fundamental change in perspective may be more important to the future of
stormwater modeling than efficient program interfacing. The modeler will need to have tools
that handle spatial and temporal data for purposes of modeling, rather than spending resources
manually transforming data into the format needed for the model. While this is the idea behind
much of the discussion in section 4, a fully integrated GIS/model like SWMMDUET may not be
the best modeling tool for the future. It may be that an intermediate database manager (e.g.
Hydrolnfo, PCSWMM GIS, etc) may be closer to a DSS than full GIS integration.
State information is stored in relational databases or spatial databases in a modern DSS. Instead
of integrating all data forms into one database model, the relational and the spatial information
are kept separate, and are linked together to form a geo-relational database structure.
4.1.1 GIS
The focus of this report has been on spatial data for modeling purposes. GIS is a critical part of
the DSS for systems that are spatially distributed. Since some spatial discretization is needed to
model urban hydrologic systems, much effort has been placed on smoothly transferring spatial
data to the model and vice versa. Under the DSS data-centered framework, the GIS is one part
of the central database of state information. Due to the popularity of GIS software, there has
been some interest in housing the entire DSS within the GIS framework. For example, Walsh
(1993) investigate spatial DSS, a GIS driven DSS. Reitsma et al. (1996) describe some of the
problems associated with a GIS-based DSS:
"Recent developments in modeling in GIS (NCGIA 1991; 1993) suggest that GIS
can be extended even further into other domains of modeling, e.g., water
resources. This type of architecture does offer certain advantages in that it makes
use of sophisticated software for management and evaluation of spatial data. A
distinct problem, however, is that although rapid improvements are being made in
the integration of GIS and modeling (NCGIA 1991; 1993), the full integration of
all three components of DSS in GIS is, to say the least, problematic."
To facilitate a non-GIS-based DSS framework, i.e. GIS as a component but not central to the
DSS, there are several considerations for GIS. First, the spatial database in the GIS must
communicate with other DSS components. This means that much of the interfacing/integration
of models and GIS discussed by Shamsi (1998) and reviewed in Chapter 3 must be extended to
include other DSS components. Second, spatial tools should be available for modification by the
modeler. The GUI should include a dynamic toolbox. For example, if the GIS performs an
21
-------
aggregation calculation in one way, the modeler may wish to modify the algorithm without
having to re-write a lot of computer code.
The spatial analysis of topographic and hydrographic data may be efficiently carried out in a
GIS. GIS software, e.g. Arc View, contain tools that take basic geographic input parameters, e.g.
a DEM, and create stormwater boundaries, do slope analysis, etc. Land use and soil coverages
are commonly used to estimate hydrologic parameters. Shamsi (1998) discusses several ways
that SWMM input parameters may be estimated using GIS. Subarea characteristics such as area,
width of overland flow, percent imperviousness and slope may be estimated for the RUNOFF
block of SWMM. Parameters used for water quality simulation with the TRANSPORT block of
SWMM such as curb length may be estimated from road characteristics in a GIS. Similarly, land
use data may be used from a GIS to create SWMM TRANSPORT input files for water quality
simulation.
Hellweger and Maidment (1999) discuss the details of the spatial analysis required to create an
input file for the HEC-HMS model. While not specifically an urban model, it may be useful to
review the procedures used. A method to define sub-basin boundaries and stream network
connectivity was developed using GIS data layers derived from digital terrain data. Intersecting
the sub-basin and stream network layers results in a node-arc representation of the watershed.
This information is used to develop an input file for the HEC-HMS model. In this example, an
underlying assumption was that streams flowed perpendicular to topographic contour lines.
While many of the tools and methods described by Hellweger and Maidment (1999) are useful
for modeling natural hydrologic systems, the effect of managed systems in urban areas
significantly complicates the analysis. For example, gravity sewers and engineered open
channels may have slopes that are independent from the ground surface slope, possibly crossing
natural stormwater boundaries and otherwise defying a general physics-based analysis that is
used when describing natural systems. Managed or altered hydrologic systems may also be
operated based on logic other than the processes that drive a natural system. For example, flow
may be diverted from a stream only during dry weather for irrigation purposes, thereby
exaggerating the apparent peaking ratio of a stream gauging station.
The problems associated with a "pure" GIS analysis of an urban, managed system highlight the
advantages of integrating GIS, simulation tools, and relational databases into a DSS. The DSS
framework addresses many of the problems associated with using a GIS for urban analysis
because of the ability to access and manage related, auxiliary information.
4.1.2 Time series
The analysis of time series data is equally important to modeling as the analysis of spatial data.
Temporal data includes flow and rain time series, water quality data, etc., as well as dynamic
model output. The DSS could include a time series toolbox. Statistical tests and statistical
models could reside in this portion of the DSS, for comparison with process models and for
analyzing model output. An example of some of this type of pre-processing is that which is
currently done in outside statistical packages, or even using Microsoft Excel.
Continuous simulation modeling usually will require large amounts of time series data for input
22
-------
purposes. Urban stormwater models that have the capability of continuous simulation usually
are capable of reading several different formats of rainfall data. For example, SWMM reads the
following formats (Gregory and James, 1996):
1. National Weather Service Hourly Rainfall Data (in two formats).
2. Pre-1980 National Weather Service Hourly Rainfall Data
3. User Defined Hourly Rainfall Data
4. Canadian Atmospheric Environment Service Hourly Rainfall Data
In SWMM, the standard modules RUNOFF, TRANSPORT, EXTRAN, and STORAGE can
import the above formats of time series data. In addition, the modules RAIN, TEMP, STATS,
and COMBINE can be used to preprocess time series data. HSP-F, the Hydrologic Simulation
Program FORTRAN, includes several time series facilities (Gregory and James, 1996). Several
single purpose time series data management programs are available. The HEC-DSS, or the
Hydrologic Engineering Center Data Storage System (not Decision Support System), was
developed to link time series data with the various HEC watershed management programs.
ANNIE, developed by the US Geological Survey, uses watershed data management (WDM)
files, and can import WATSTORE files (Gregory and James, 1996). Both ANNIE and HEC-
DSS are non-proprietary FORTRAN models. Due to the multitude of file formats it is difficult
to import and export datasets between different modeling environments. For this reason, the
CASCADE2 time series management program was developed (Wang and James, 1997). This
program, written in Visual Basic, runs under MS Windows and bridges the gap between SWMM
and HEC-DSS formats.
To be used within a relational database, the time value must be stored, which creates a
redundancy of information. This is because a time series is defined by the start time, the time
interval, and either the length of the interval or the end time (Reitsma et al. 1996). Another
disadvantage of the relational approach is that the DSS must store the criteria for searching the
time series (Reitsma et al. 1996). The importance of this redundancy becomes more evident in
the case of real time control, which utilizes signal processing and control theory. Lavallee et al.
(1996) describe a real time control system developed for the Quebec urban area to manage a
stormwater system to minimize CSOs. The unique data needs and system architecture of the
RTC system support many of the concepts of DSS due to the demand for timely decisions and
vast amounts of data available..
4.1.3 Relational database
An example of a relational database query and its results is presented in this subsection. This
example is presented within the context of a relational database contained within a GIS. The
same queries can be made in a non-graphic relational database. The linked tabular structure of a
relational database allows for extremely complex and powerful queries to be constructed, thus
relevant information is made available to the user. The City of Aurora, Colorado has developed
a very good base system for GIS. A subcatchment was chosen from the Shop Creek watershed
of Aurora, Colorado, a pilot area for GIS development for the City of Aurora.
The available themes from this area are as follows:
1. Water lines
23
-------
2. Digital elevation models
3. Rain gages
4. Stream gages
5. Parcels
6. Sewer lines
7. Sewer manholes
8. Digital orthophotos
9. Streets centerlines
10. Sewer tap locations
11. Water meter locations
12. Impervious areas (created by tracing the digital orthophotos)
Many tables are associated with each of these themes. An important feature of Arc View is the
use of the relational database structure. Tables are linked to graphical features, or themes
(analogous to layers in AutoCAD) through the use of spatial geocoding. The user links or joins
the tables by choosing a common column, or field between them. The three main types of
relationships among tables are:
1. One to One
2. One to Many or Many to One
3. Many to Many
All of the records in the one to one table could be placed in the same table. However, good
database practice suggests organizing the tables around their functions, instead of the other way
around. For example, many attributes are associated with your name, but only your address and
phone number are listed in a telephone directory. The first two of these types of relationships is
shown in figure 4.2. The two tables nearest the bottom, "Attributes of Themel.shp" and
"Attributes of Parcel" are joined by a one to one relationship, with the fields "Parcel-ID" being
the common column. This is again the relationship between "Attributes of Parcel" and
"Attributes of Address", using the fields "Parcel-Id" and "Address-Id" as the common columns
(it is not necessary that they have the same name). Lastly, a one to many relationship is shown
by the indexing of "Attributes of Address" and "all_9295.dbf' with the fields "Gistag" and
"Gisno". The function of this linking is essentially the following. The themel.shp table contains
the parcels that are located within the small subcatchment. The Attributes of Parcel table
contains data on all parcels. Attributes of Address contain address information, including the
GIS tag number needed within the Water Use database. This database lists monthly water use
data within entire Shop Creek basin, so many records are associated with each parcel.
The query shown in figure 4.2 illustrates the power of this tool. The query asks for all linked
records in which the water use in a month is over 10,000 gallons. The results of the query are
highlighted within the tables. These queries can be moved to the top of their respective tables for
further visual analysis. Alternatively, by clicking on the view with the current theme set to
Themel.shp, the visual results of the query can be seen by highlighting parcels that used at least
10,000 gallons a month as shown in figure 4.3.
24
-------
- Inlxi
Eile Edit lable Field Window
i ii 1 i ii ii 1 i Fields Values
[HUB yyOSJOS] [D l[Pre date] U] | = | | <> | | and | 6
13 of 63selecte IMniDiiii. f— 1 fTT] r^-| -'
1 FRslAJer IJBfil ! 1 II II 1 F
^J-
566070650
5i'6'07065i:i
566070650
< |
^
[Storm usej ! 1 < 1 1 <= 1 1 not 1 £
• :s::::::j . ^M P
^\3ffff vvV? | [Month! i 1 1 v * \ I 1
SA | I 3 p
SA | [Water use]>=10
SA !
1-- A
dat&exsff | | ^:
2243 j j
2244 T
2245J
2246!
2247!
2248!
2248!
2250!
2251 !
2252!
I 69013J 566070655! 14530 ]E ! TUFTS
68126'T 566070366T14T72 !I fRADCLW
68132! 566(171 H/2ii 41 74 !E i RADCUFF
nni 1 4 ; nnnri7G378 ! i i'i 98 ! E j RADCUFF
63108! bGbU/LQU'l ! I42IIJ IE ! RADCUFF
68642 ! 566070742 ! 4527 ! S ! FAIR PLAY
685241 56U07COQJ ! 4453 IS I EAGLE
68521 i 566070897 ! 4473 ! S ! EAGLE
nnR4.:: : nnnn-o743 ! 4517 ! s ! FAIRPLAY
68119! Se'GOTOMl ! 1 41 52 !E ! RADCliFF
*3l
..TAsaw .-4™ ftsanefer
Polygon i 608.84226! 88.87247! 2
Polygon ! 33824.443791 5771. 08960 ! 3
Polygon !
Polygon i
1 93538.85220 ! 1 061 3.42282 ! 4 !
53017.50035! 1639.50133! 5!
Polygon ; iM/bS.UBUSb i b^a.aiJJb: b:
i
S/lJFff | XL™ /fe«MHfer | /^wraJS" /
Polygon ! 21357.77638! 602.17219 2149
Polygon ! 14204. 22725! 504.1 0308 2176!
"Polygon""! TraizSOiOl"! 591141339 2188!
Polygon ! 14240. 529' 95 ! 521. 531 57 2215
Pnlnnnn i inRQ9 ifl441 ! 4^4 44791 77?? i
3& fiMni xl I
i^Slarl | |^*^ArcView GIS Version ... B2?" Microsoft Word - epal
Ld
I |
LJ
^^^Hd
< Update Values
•* ! New Set i|
Add To Set
~^ Select From Set
- lnl x • 1
niil
|D| xl
afe | &fefsr_use \ Ssfnsr_uss Sfoa}}_use
i ZJ 5, S-±4
1 .1! 5.i S_J
! 9| 5 C ,
1 ^" jj ' —
«|l
^11
^[1
31
«yl
:'-ln|x|
i±ttrfj»- | Xjott^M1 2^3 | fbf
! AVE ! 8001 5 i 2073-07-1 -1 1 -01 5
|"C"IR ; l'aob'i'5 T"2b"7¥b'7-2"23'^i'il
! CIR I ! 80015 ! 2073-07-2-23-1 27
I 5R | 8001 5 ! 2073 -07-2-23-1 28
F'CIR : rsbbi's !"2b73"-b7-2'-2'3"i38"
! C"f ^ ........ .......| .g^ g ^ 2673-07-1 -06-669
! CIR : 1 Bom 5 i 2673-07-1 -bs'-ooe
! CIR ! ! 8001 5 ! 2673-07-1 -05-012
i CT i 80015 ! 2073-07-i -06-ODB
! CIR ! ! 80015 ! 2"673'-b7-2-23'-i i i
1 - |n| x| 80015 i..2.?Z3:P?.:i:1.1:£ii
^oii^t?' |^ /ay \ SyH].5 L.<:.y/i?.:y.(!:.f:.^H!rli/.
58646 i 207 3-04-2-22-031
58667 1 207 3-05-1 -1 0-005 ff,
68298 ! UN 'MOWN
6831 4 ! 207 3-08-1 -27-002
1 New Open Add
68315 : 20 f 3-08-1-26-002 _,. , = , —j.—-,-.-,
' *" ' I ] '^l/' ~~ Attributes of Address
-=-U=U-XJ wicw- Attributes of Parcel
~\3fi~s/-K/ Jg^ 1 71317 Af& s**^ Attributes of S anitary
69601 ! 2073-07-1 -1 1 -01 5 - ^^ Attributes of Sanitary_MH
6"9'6'9'4"l'2073-07:vi'i'-'6'i"4' MF?** Attnbutes of ^emel'*p
gg7Q4't 2073"o7 "fi'l "6'i "i * — *S3^* Attributes of T hemeS. shp
6'9's'5'5"!"2"67'3-67'-i-l'i'-6'io * m> Attributes of Theme6.Shp
£qSmt?n7^n7:i:iT:nTfi T - _. , fiow" d™ ...
~^~~| Charts sanit mh.dbr
=LJ t£j^\ sanitary, dbf
1 t5^ J 1 1 .
i
!
!
:
-i
i
!
i
...|, —
i
I
!
"f
...!.
! T
~n
•
^m
o"
—i
10
_1_1 S*
12:36PM |
Figure 4.2: Relational database query example in Arc View using water use data
25
-------
Eile Edit View Xneme Graphics Window Help
jsp r*n f^flf^nfj^n rs&nnnrj^?
[ma
Them eS .s hp
m
Pilotare a.shp
rzi
0 rigin: (2,191,703.79, 655,996. B4) E xtent: (1,274.52,1,162.19) Area: 1,481,241.36 sq
art < ArcView GIS Version ...
Figure 4.3: Spatial results for example query from figure 4.2.
-------
The results of the query can also be output to an Excel spreadsheet by using Arc View's Avenue
script language and Microsoft's Dynamic Data Exchange (DDE). This capability is incorporated
as a toolbar shown as an "X" in figure 4.3. The results of this query, output to MS Excel, can be
found in figure 4.4.
] t* E*
|.-3l --l-fa-l li-l
z I
* •» 11
» 14 H -
Psfcn Parcl id WSIDI USE
779 2H9 69601 ~10
ise.* Z2Z2 effloi H
I9JB3 2322 7D1i6 10
212 23*2 7Q2& 26
7 33 MB (TC13B 16
.237 2J22 7Q3SS 11
912 2«5 7W3? U
723 25U 70627 ili
.1« 3887 70720 10
SS 3a* KH3? 11
1 99 jsrr rio^ is
211 25Ti 71031 12
SB 2STS 71060 20
Ddi
19933123
1S20IZT
19920122
19320122
19320122
193301??
1'3320122
19520122
19920122
19333122
1933] in-
19933122
199301J?
Sups
Shipa
3'"ilp8
9^spc
Shjpo
SBRO
S^l^pA
3lSp«
ampe
Shapg
'Jiipa
SJ-ripd
3ap8
Stnpg
"
Figure 4.4: Query results output to Excel using Avenue script tool and Microsoft DDE
An example of a relational database within a DSS can be found in Reitsma et al. (1996). In a
review of the TERRA DSS system, the authors explain that the data were divided into seven
main groupings:
1. Time Series Data
2. Historical Data
3. Physical Attribute Data
4. Operational Constraints
5. Model Data
6. Security Data
7. MetaData
Meta data, the last group, is data about data; and allows the Data Management Interface (DMI), a
program component of the DSS, to refer only to the meta data, which keeps track of the data
structure and where and how the data is stored. This allows the DSS program to be relatively
27
-------
free of data constraints (Reitsma et al., 1996). Although the relational database model has some
shortcomings, particularly for time series, it remains the database structure of choice for DSS, as
it is the prevailing database model at present.
4.2 Process Information-Simulation Tools
In the DSS framework, the process information is contained in simulation models. Process
models simulate transitions of the state of the system, as described by the geo-relational
database. The simulation model must therefore communicate in some fashion with the rest of the
system. For stormwater management models, this may occur in much the same way as described
in chapter 3. Data must be transferred to the model from spatial and relational databases. This
may occur in a variety of ways, from the rudimentary (but effective) data interchange methods to
full-fledged integration in a DSS, running along with the other tools that make up a DSS. The
difference from the methods described in chapter 3 is that the communication is not only with the
GIS, but also with all elements of the DSS.
4.3 Evaluation Tools
Evaluation tools assist the decision-maker by presenting the output from the process and state
information in a manner consistent with resource or policy appraisal (Reitsma et al. 1996).
Evaluation tools may be of many forms. While much of the above discussion is framed around
the excellent review of DSS by Reitsma et al. (1996), the discussion of optimization deviates
somewhat from their discourse. Reitsma et al. (1996) do not consider optimization tools to be
strictly an evaluation tool, nor do they feel that optimization has been accepted by the user
community. While perhaps true for classical optimization techniques, the development of new
Intelligent Search Techniques (1ST) is proving to be useful for many realistic problems that are
unsuitable for traditional methods.
4.4 Overall DSS for Water Management
An overall DSS for water management of hydropower and river operations is shown in figure
4.5. This DSS combines the concepts of a centralized database, including hydrologic as well as
spatial information, and utilizes two different models that access that data; the Modular
Modeling System (MMS) which is a watershed and general environmental model, and
RiverWare, which models rivers and reservoirs. Evaluation tools are included within each of the
model components. The DSS includes a GIS as a tool for the user to query the common spatial
database. This DSS was developed by the Center for Advanced Decision Support in Water and
Environmental Systems (CADSWES) at the University of Colorado at Boulder, with support
from the Tennessee Valley Authority and the US Bureau of Reclamation. This DSS focuses on
large watersheds with complex reservoir and hydropower operations.
28
-------
River Ware System
River and Reservoir Management Models
I Long-teini Policy I
| and Planning |
I Mid-teim
| Short-turn |
I Operations |
L J
Data Sources
River & Res ervoir
Telemetry
SCADA
NEXRAD
Data Management System
HDB
Hydrolo gic D atab as e
Query., Display,
and Analysis
GIS
Statistics
Tradeoffs
Risk
Modular Modeling System (MMS)
Watershed and Environmental Models
I Root Zone I
I Models {ARS) |
I Precipitation- I
I Runoff Models |
S (dime nt I
Tianspoit Models |
Figure 4.5: CU-CADSWES DSS
(Fulpetal., 1994)
A DSS framework for the urban stormwater field is presented in figure 4.6. This DSS is an
amalgamation of the different components of the Mike series of software produced by the Danish
Hydraulic Institute (DHI), emphasizing their interoperability and common database, Mike Info.
The database (relational and spatial) is the common link between separate functions and
applications of the DSS. The peripheral models include Mike-11 for urban drainage, Mike SHE
for distributed watershed modeling, WUS for river basin planning, and NAM for statistical
analysis of streamflow/unguaged catchments.
29
-------
wus
« fiver kajip planting
River basin studies
Runoff from
ngauged areas
Wetland dynamics
Irrigation
Figure 4.6: Danish Hydraulic Institute DSS, based on integrated water resources modeling (DHI,
1998)
30
-------
5.0 Application of GIS and DSS to Micro Storm Analysis
This chapter focuses upon the application of GIS, database management, and DSS to the urban
stormwater management problem. A textbook case study from Tchobanoglous (1981) is used to
develop a GIS and an accompanying relational database. The database is used with hydrologic
and hydraulic models, and a cost analysis module. The combination of these components
represents a systematic urban stormwater design tool. The tool is then interfaced with an
optimization software package to develop optimal designs of the proposed network. The costs of
these designs can then be compared with a decentralized approach to controlling runoff. This
was done by using the GIS in conjunction with the NRCS analysis, which computes the initial
abstraction storage volume that is lost as a result of development. Using unit costs developed in
Heaney et al. (1999a), the optimal suite of controls can be selected using linear programming
(LP).
A diagram of the process used in the chapter is found in figure 5.1. The reader may notice
similarities between some of the components of a DSS and figure 5.1. In particular, the problem
consists of a database, simulation tools, and evaluation tools, similar in concept to that of a DSS
presented by Reitsma et al. (1996). The database includes GIS and its inherent spatial database,
but also a cost database, and a hydrologic database. The simulation tools consist of the NRCS
curve number method for computation of initial abstraction, the hydrologic model spreadsheet
template, the hydraulic model spreadsheet template, and the costing module. The evaluation tool
consists of a genetic algorithm to optimize the stormwater network, and a linear programming
model to evaluate proposed controls based upon unit costs developed in Heaney et al. (1999a).
Although not integrated into a single software program, the process shown here closely parallels
that of a DSS. The utility of GIS (to the urban stormwater field) is enhanced by its close
integration with the database, models, and analysis tools used in the problem. Because of the
large investment in time and resources necessary to construct an urban GIS, there is a natural
tendency for the GIS system to move to center stage. However, the value of the GIS is when it is
fully integrated within a DSS which is then used to address complex processes that cannot be
easily solved by other means.
Key considerations are the concepts of accuracy and scale as they apply to GIS data. Since the
datasets presented here vary substantially in terms of their level of detail and scale, a discussion
of spatial scale becomes necessary.
31
-------
DSS
Evaluation Tools
Optimization
Linear Programming (LP)
Genetic Algorithms (GA)
Database \
Relational (nongraphic)
addresses
billing
unit costs
time series input data
GIS/Spatial Database
Themes
Topography
Soils
Land use
Streets
Right of way
Pipe network
Parcels
'Simulation Tools
NRCS CN Hydrologic Method
Rational Method
Hydraulic Design Template
Cost Template
Figure 5.1: Proposed DSS for microstorm analysis
5.1 Spatial Scale and GIS-Stormwater Modeling
A recent software development, BASINS 2.0, developed by TetraTech for the US Environmental
Protection Agency, has created interest in the development of model-graphical user interface-
GIS linkages within the water community. BASINS 2.0 runs within Arc View 3.0 and includes a
national dataset on the attributes listed in Table 5.1 (Battin, et al. 1998).
32
-------
Table 5.1: Available BASINS data attributes
(Battinetal. 1998)
Spatially Distributed Data
Land use/land cover (GIRAS)
Urbanized areas
Populated place location
Reach File, version 1 (RF1)
Reach File, version 3 (RF3)
Soils (STATSGO)
Elevation (DEM)
Major roads
Environmental Monitoring Data
Water quality monitoring station summaries
Water quality observation data
Bacteria monitoring station summaries
Weather Station Sites (477)
Clean Water Needs Survey
Point Source Data
Permit Compliance System
Industrial Facilities Discharge (IFD) sites
Toxic Release Inventory (TRI) sites
USGS Hydrologic unit boundaries
Drinking water supplies
Dam sites
EPA region boundaries
State boundaries
County boundaries
Federal and Indian Lands
Ecoregions
USGS gaging stations
Fish and wildlife advisories
National Sediment Inventory (NSI)
Shellfish Contamination Inventory
Resource Conservation & Recovery Act (RCRA) sites
Mineral availability system/mineral industry location
Superfund national priority list sites
BASINS 2.0 includes tools for automatic watershed delineation and handling of digital elevation
models (DEM). Its main data handling routines include: Target, which is a regional, or state
level broad-based watershed water quality or point source assessment tool; Assess, which
operates a smaller scale of one or a few watersheds and enclosed discharge points or water
quality stations; and Data Mining, which dynamically links water discharge stations and
geographic location information. Modeling tools include a nonpoint source model (later to be
enhanced by the addition of SWAT, the MS Windows based nonpoint source model developed
by the USD A), HSPF, Qual-2E, and Toxiroute. Model post processors include graphs (Battin,
Kinerson, and Lahlou 1998). EPA SWMM may be linked with BASINS in the future.
33
-------
The accepted accuracy levels of mapping work are listed in Table 5.2 (Shamsi et al. 1995). Most
of the BASINS work and modeling have been on a watershed or regional level scale. An
example is shown in figure 5.2. The size of this file relative to the area it represents reflects a
scale of about 1:2000.
Table 5.2: Minimum horizontal accuracy and example features for various map scales in urban
areas
(Shamsi et al. 1995)
Map Scale
1"=50'
l'=100'
1"=200'
1"=2000'
Minimum Horizontal
Accuracy, per National Map
Accuracy Standards
±1.25'
±2.50'
±5.00'
±40'
Examples of Smallest
Features Depicted
Manholes, catch basins
Utility poles, fence lines
Buildings, edge of pavement
Transportation, developed
areas, watersheds
£eft !$ew Jtheme Jaraphics Tcsget Assess Model fieport Lookup Utility
S3
Permit Complianc
ndustrial Facilities
National Priority Lis
ous int Soli
Qyjlity StJtk
Baettria Stations
Drinking Water Sup
A
0am Lccatioris
I
Reach F]i«, V1
Figure 5.2: BASINS dataset for Boulder, Colorado
Automatic watershed delineation of undeveloped areas may be appropriate at this scale.
However, urban systems have extremely altered topography. The topography in these types of
catchments can be represented by a dense DEM; however, development of watersheds based
34
-------
upon triangular irregular networks (TINs) from this information is not presently reliable. This is
not to say that the database information presented from a watershed level scale has no value.
Actually, having the information presented in figure 5.2 can provide the modeler with possible
alternative sources of data, possibly structures that may not have been considered, etc. However,
a key disadvantage of using GIS information from different scales of accuracy is that a vector
GIS cannot show any uncertainty. An assumption of the GIS model is that the points are known
to 100% accuracy. This leaves it up to the reader to verify locations and discrepancies,
particularly when the scales, and the resultant accuracy, differ widely.
In addition, the memory requirements for regional level stormwater-GIS modeling are
staggering. For example, the City of Boulder has an ongoing GIS project, a broad view of which
is shown in figure 5.3 (Brown and Caldwell and Camp, Dresser, and McKee, 1997).
Figure 5.3: Arc View coverage of Boulder, Colorado
(Brown and Caldwell and Camp, Dresser, and McKee, 1997)
Minor roads are outlined in light green, major roads are outlined in thick maroon; creeks are
shown in light blue, lakes in shaded blue, and sub-basins boundaries in black. Not shown for
better clarity, but available, are parcels, zoning, topography, watershed boundaries, and several
other miscellaneous themes. Also not shown is the database describing each graphic entity (for
example, the parcel database). Even at this finer resolution, urban stormwater modeling is at too
aggregate a scale to evaluate sets of alternatives that include micro-topographical changes to
implement BMPs.
35
-------
In order to evaluate the effects of source and neighborhood-level BMPs, the coverage as depicted
in figure 5.4 is needed. This area is a block in the University Hill neighborhood of Boulder. The
parcel theme is shown in red, the street centerline is shown in green, and the streams are shown
in blue. Topography is not shown, but exists in this database at the 40 foot contour interval,
reflecting a scale of about 1:200.
Figure 5.4: City of Boulder Arc View GIS coverage for University Hill neighborhood, Boulder,
Colorado.
36
-------
Moving towards a finer dataset, another parallel project at the City of Boulder, in the Public
Works/Public Utilities group, is an Automated Mapping/Facilities Management (AM/FM)
project in which the city's infrastructure is being mapped by street surveys and aerial
photography. The end product at the present time is a tiled set of AutoCAD maps representing
portions of the city. The representation of this project for the same block in the University Hill
neighborhood is shown in figure 5.5. The scale of this information is approximately 1:100. The
green layer signifies building rooflines, yellow is the street centerline and parking
areas/driveways, red is sidewalks, and black is the curblines. This file has been edited
extensively to eliminate extraneous lines and close polygons. Since the end product of the
project was a set of AutoCAD maps, manual and automatic processes on the digital photography
result in multiple lines whose ends may not match and polygons that do not close. Although
acceptable for graphic presentation, this information is of limited value for extracting data for
stormwater evaluations. Extensive cleanup is necessary for this information prior to inputting it
into a GIS. Topography for this information is available for an additional cost at a 2-foot contour
interval. At the present time, conversion of this data to Arclnfo and Arc View coverages is
underway.
5.2 Description of Happy Acres Case Study GIS
A textbook study area, nicknamed "Happy Acres", was selected from Tchobanoglous (1981). A
GIS coverage for this case study was developed. The study area was first digitized in AutoCAD,
then edited for geometric consistency, i.e., parallel lines were kept parallel, polygons were joined
from separated lines, to make the transition to GIS easier. The mix of land uses for the area is
laid out in table 5.3. The reconstructed AutoCAD drawing of the area is shown in figure 5.6.
The topography of the study area and the layout of the storm sewer system are shown in figure
5.7 (Tchobanoglous 1981). Land use is shown in figure 5.8. Soils data is shown in figure 5.9.
The entire study area is divided into 54 sub-areas that range in size from 0.8 to 5.4 acres in size.
A description of the attribute information in figure 5.6 is found in table 5.4.
Table 5.3: Mix of land uses in Happy Acres
Land Use
Residential, low density
Residential, medium density
Apartments
School
Commercial
Total
Acres
20.8
51.7
10.0
5.7
18.4
106.6
Dwelling
units/acre
2-3
6-8
10
N/A
N/A
37
-------
Figure 5.5: AutoCAD file for University Hills neighborhood, Boulder, Colorado.
38
-------
Figure 5.6: AutoCAD coverage for study area
(adapted from Tchobanoglous, 1981)
39
-------
IN
A
100
Figure 5.7: Study area topography
(adapted from Tchobanoglous, 1981)
40
-------
I Tchoban_roads2_region.shp
\lwgrd3
Apartment
Commercial
LD Residential
MD Residential
School
No Data
300 0 300 600 900 12001500180021002400 Feet
N
Figure 5.8: Study area land use
(adapted from Tchobanoglous, 1981)
41
-------
Tchoban_roads2_region.shp
Tchoban_drainage2_region.shp
Soilgrid
Clay
Rock
Silt
No Data
300 0 300 600 900 12001500180021002400 Feet
N
Figure 5.9: Study area soils
(adapted from Tchobanoglous, 1981)
42
-------
Table 5.4: AutoCAD layers for study area
Layer/Object Category
Streets
Manholes
Sewer lines
Land use boundary
Hydrologic boundary
Parcel
Rooflines
Driveways
Soils
Color
Not shown (for clarity)
Blue
Red
Aqua
Blue
Green
Magenta
Orange
Not shown (for clarity)
The AutoCAD layers shown in table 5.4 become the following Arc View themes:
1. Streets
2. Manholes
3. Sewer lines
4. Land use boundary
5. Hydrologic boundary
6. Parcel
7. Rooflines
8. Driveways
9. Soils
A relational database is associated with each graphic object, grouped according to type.
Attributes associated with parcels are address and land area; and with streets are right of way
width, length, land area, and street name. Soils and land use exist in separate tables, and this
information was combined with the parcel and street databases by performing an intersection
query on the two themes. The results of the query can also be output to an Excel spreadsheet by
using Arc View's Avenue script language and Microsoft's Dynamic Data Exchange (DDE). This
procedure was used to extract the relevant attribute information for parcels and streets.
The rights of way identified in figures 5.6 through 5.9 were assigned widths based upon the
following criteria. Minor streets within the development have a 50 foot right of way, a minor
arterial is given a 60 foot right of way, and a major arterial a 70 foot right of way. The profile of
each right of way is given in table 5.4. The reader is referred to Heaney et al. (1999a) for further
details on the database.
Table 5.5: Right of way characteristics
R/W
ft
50
60
70
Length,
ft
28,680
1,124
2,741
Curb
ft
4
4
4
Parking
ft
8
16
16
Landscaping
strip, ft
10
10
18
Sidewalk
ft
8
8
8
Traffic
Lanes, ft
20
22
24
43
-------
Note: Some of the parameters are summed from both sides of the street.
Lot characteristics for the two single lot residential land use classifications are presented in table
5.6. Lots were aggregated in this manner for the optimization; however the GIS contains the full
heterogeneity of each parcel.
Table 5.6: Lot characteristics for residential parcels
Land Use
MD Residential (6-8 DU/AC)
LD Residential (2-5 DU/AC)
No. of
Parcels
255
51
Roof
Area
SF
1,600
2,000
Patio
SF
200
400
Driveway
SF
600
800
Landscap-
ing
SF
3,600
9,800
Total
Area
SF
6,000
13,000
For the apartments, commercial, and school land uses, an aggregate analysis was used because
these land uses exhibited multi-parcel characteristics, such as for parking. A summary of these
characteristics is found in table 5.7
Table 5.7: Aggregate characteristics for commercial, apartments, and schools
Land Use
Apartments
Commercial
School
No. of
parcels
2
6
3
Stories
2
1
1
Parcel
Area
SF
162,680
481,070
149,407
Roof
Area
SF
46,927
152,839
69,080
Parking
Area
SF
75,083
304,678
51,807
Landscap-
ing
SF
40,670
23,553
28,521
5.3 Simulation Tools for Hydraulic Design
The storm sewer network for the Happy Acres subdivision is diagrammed in figure 5.10. A
spreadsheet template has been developed to simulate and optimize storm sewer design for the
Happy Acres neighborhood-see tables 5.8 to 5.10. The value of better data obtained using GIS
can be estimated by evaluating the designs with and without this better information. The
following columns in table 5.7 represent data that can be obtained partially or totally with a GIS
system for this example.
Column
5
6
7
Description
Sewer length
Stormwater area
Dwelling units per acre
The output from table 5.8 is the design peak discharge leaving each subcatchment. This
information is input to the sewer design table 5.9 that finds feasible combinations of pipe
diameters and slopes. The constraints on the design are:
Minimum depth of cover for the sewer, and
44
-------
Minimum velocity in the pipe.
The decision variables are pipe diameter (column 8) and slope (column 6). Trial and error
procedures are used to find a feasible solution to the design problem. In more sophisticated
analysis, the costs of the alternative systems are evaluated as shown in table 5.10. The
background for development of the cost relationships found in this table can be found in Heaney
et al. (1999a), and is based upon data obtained from R.S. Means (1996a). Additional GIS data
are helpful for the cost analysis. Specifically, soil conditions (column 8) affect the side slopes of
the sewer excavations, and the bedding costs.
Figure 5.10: Study area sewer network
(adapted from Tchobanoglous, 1981)
45
-------
Table 5.8: Sewer network design hydrology
(Heaney etal. 1999)
aas-
a »t
T SC
a as i
3* ij 23 2*
2* na
** 1 a r
IHWC*
j,*-***,^
tllr^^ 1^1
«BJ 5,
»«3
•t .sm o 90
:^ t — 1
9*3*1
&KI
•**..»«
3L1B
use
B-t*
?-B3 ffll
'-»* 8*
46
-------
Table 5.9: Sewer network design hydraulics
(Heaney etal. 1999)
1
*gf**£
m^&mex$mWi&
Mssite^rdwKSQei
M*SWf?fr3*«Ou4
IB»"!i««r «¥? rfV*WW" ™
MiajJS*?'* e-f-Jw-Cs
111 _ ne
*•£*#•*
"*4*
Hak
i ">
V«HMJ*
*•** r v-t
v* Fi la**
W Fas*"«S;
W Ftiifs&s?
W Func^;
£ Fors-Si
E. FSWM
Kv^»fraw«!,t'S^lKn
S¥fcWJt*SF«€>rt
S'^c-sn^f^f !*"**
SvcansofiftC Im
gyii^ifiwssil-flCT!
S ^amonsriri:rn
5¥&3fm»feC5*ft
Asiiru'iAj^faiViSTic h
A shuAefif f jifENits^
&x!Vifi£CiBTijf$Sif c ft
Afc hMfce* fsjiB*f Ch
.AKWAr^rTOfferc^
&shMc43Fn*13sirc h
A^AsfirnaR*^
£issl fere?*
Fastftec* j
A Ced»>
v¥ Onqsif
W Ctsstar
W fZ*rtw
E C*4&»
£.. Ce»
Aspsm
Asjiers
*?V &KNraw&
W !#*jshra£»r3
W &£?W¥3fei
E fehmart
(r A&£tmonr
H*qMaro
^%**and
A|pir*e
oafc
W-atnsj*
Fcsf*?*?
£^n
Fcstesir
&*«fs
ft5P€m
Aimers!
H^Mand
2
T^fw
Hruiv*
Brancti
Sr#nefv
%anrh
iSasrt
fet-r
fcTKTtj
B
Snarr^
Bran**
iSTZK^lfi
S?am^
£$*3Rcfc
IBrsmch
ffejfr'ctt
ftsrscft
Hg^H^cts
&Hf?cn
tf&anzH
&and*i
teanch
S^arKf:
9Jas^
arancn
B*9Pa5ft
iSpg-rich
BnirsE*s
grj|ncn
|fe«^i
»vncft
&anch
awch
TWSfe
Tru?fl«
Trunls
Try**
try™s
Tnjn*
Tftf*
T>ro^
¥,rv*
Tnrft
!*y>^
3
Mr>
Ffustft
2
3
4
-3 "
'*.„
BC«
fe.
«e
"U^
^s
h*
13C
*Qf
10C
1*^3
10A
1?B
17*
o t u. d u 5 *
SlSKa
1-3T
13£
^-^3
I^C
13D
13A
?3*
13M
1«?.
UK
1«0
14-%
1«
14F
!SB
15*
1SC
1flB
lAA
I«C
16D
O'S
17*
G
?
6
9
10
^i
11
11
S
*6
t?
4
d«*
To
?
3
•I
1
»p
~A
SB
ft*
OS
a
'DC
ir>~
103
10*
10
11*
12
n^
11E
no
tic
iie
•>i A
it
i:^
':•€
Tto
«3C
us
13*
!^
?3w
13
r »4C
l^ffl
S4A
i-4
1«F
t4.
^SA
»5
IfiS
ISA
r-E
ieo
»5
^t'A
17
=
9
t-5
11
tj
?.4
«»
ta
^a.
s
TfW
Slop.
IB«
f
4
4
S
4
a
9
1*
»3
4
4
e.
s
1
5
i
5
»
1
11
3«
i©
7
J
«
S
4
11
ts
IS
*
9
r
«
4
a
4
S
17
s
4
7
4
S
19
13
1(
4
1i
1^
JS
•
U
2
«
*
I
m
4
4
4
4
4
S
Trial
Wop*
«U*L
O004
aoj*
COCO
»cn«
grnis
Sd13
OOM
& CKiiS
ODDS
•9005
BCD6
DOK
COM
C012
DOt
9 oo:-
0003
DO' 2
QUO*
SOW
00 ITS
D q-s IS
0009
GOES
0007
O0*3fi
p>TCB
DOCK
OO2
O?S[^
05JO7
OOOS
0513
00!
0«75
Q0401
0 9»5
QOH
OBI
OOD6
Oooe
oooe
oatM
o«>*
0064
ossw
0.3O4
y
TNal
PH»
ID*
9
1
4
i
i
i
;
i
«i i
!
ft
S
4
5
S
«
4
2
3
4
4
§
J
13
J
14
$
1
4
t
*
3
3
t3
3
4
J
1
f
«
4
S
9
t
8
9
«
1f
It
It
«
a
«
TUB
Kff
OtLlM)
1!l
21
7t
24
IB
18
i-3
m
?*
24
?4
^5
18
21
74
2-*
?«
?7
IS
21
21
J*
2«
1S
1S
'sa
21
18
n
18
11
IS
38
21
24
OS
{25
tD4
7 3
11
Oo«*f«stFva3**
f>«pW:W
we •»!>!»»
f«t
53
S J
74
85
73
4 J
5 ^
sa
43
4 4
4 4
5 1
< 7
50
45
5.8
4 a
4.1
4 4
47
45
4 :
^'A
OS8
H«
102
102
W*
D36
*j/^
0. *B
MUS
1 S3
1 O2
PS^
037
182
1 rJ3
1 01
t 02
1 CO
065
OS5>
« J
fiM 1
wwacny 1
m
•JHC
?*A
4 10
4 W
see
498
»*
S 1S
WA
V >3
4&»
4M
WA
4se
NIA
SOB
?se
r r*;
soa
^ A
r ''a
Si^
^ 4*
ea-
i y
i er
s ^^
WA
- ".
857
«!•.
8141
'tr.
r-«
579
W&
502
S1«
SO
WA
S«?
H/*
s 13
Hit.
J »
7 IB
Hs'A
*8T
Hs'sft
JO»
WA.
981
510
3t *3
1ft IS
iKS
965
S 14
S 14
S 14
f033
tn 32
fS
OasSD^
vdoeidr
**
ftJS«c
,WA
4 2O
464
S*
509
W*
8JB
!*«
S HI
458
sas
WA
4SS
WA
4B6
586
5PH
•s rj
r^ft
** ai
r**
*• ir
60S
440
» a?
56"
" 43
*1A
7 ~4
sx:
fir«
B32
673
T IB
»»A
5*«
WA
511
320
855
K»A
5»
hs.*A
S 0?
W*
755
7H3
.W*
S?4
?*"*
98'
H/A
9JS
5 1i
9M
9 Sf^
901
9S9
a jj
915
S .1?
978
9K-
2@
V*toel%
Clt*cfe
ta*w>-a
«-aeam
2
2
2
2
2
2
7
2
2
5
2
J
2
2
2
2
7
2
2
2
J
2
2
2
2
2
J
2
i
2
^
2
t
2
2
47
-------
Table 5.10: Sewer network design cost
(Heaney etal. 1999)
48
-------
Using a new intelligent search technique called genetic algorithms (GAs), the optimal design was
found by having Evolver (Palisade Corp., 1998), a commercially available GA, evaluate different
combinations of pipe diameters and slopes until the least cost design is found.
5.4 Simulation Tools for Hydrologic Analysis
Heaney, Wright, and Sample (1999) describe a method for using the NRCS curve number (CN)
approach for evaluating micro storms. The fundamental principle is that development should not
reduce the initial soil moisture storage that existed prior to development. This initial soil
moisture storage is equivalent to the initial abstraction as calculated using the Natural Resources
Conservation Service (NRCS) curve number (CN) method. The initial abstraction is a good
measure of the ability of the soil system to filter the stormwater. The initial abstraction, as a
function of CN, is shown in table 5.11. Inspection of table 5.11 reveals the importance of CN. A
low CN of 30 corresponds to an initial abstraction of 4.67 inches. Even at a CN of 80, the initial
abstraction is still 0.5 inches. If the original CN is fairly low, then a significant amount of soil
moisture storage is lost if this area is rendered impervious by development.
Table 5.11: Initial abstraction as a function of curve numbers, CN
CN
20
30
40
50
60
la, inches
8
4.67
3
2
1.33
CN
70
80
90
100
la, inches
0.86
0.5
0.22
0.02
This method uses the concept of modifying the CNs for the developed condition so that the
modified CN is the same as the natural CN. The more cost-effective controls tend to focus on
utilizing the pervious area for more intensive infiltration. Alternatively, we seek to design
hydrologically functional landscapes as described in the next section.
5.4.1 Hydrologically functional landscaping
Traditional landscaping relies on covering most, if not all, of the pervious area with grass. The
lot is graded so that stormwater drains to the street and/or the rear of the lot as shown in figure
5.11 (Dewberry and Davis 1996). An example of a hydrologically functional landscape is shown
in figure 5.12 (Prince Georges County 1997). The general idea is to maximize the infiltration of
stormwater by providing depressions, draining runoff from impervious areas to pervious areas,
providing more circuitous routes for the stormwater to increase the time of concentration, etc.
49
-------
a) Lot Grading: Drainage Directed Toward Front of Dwelling
b) Lot Grading: Drainage Directed Toward Rear of Dwelling
c) Lot Grading: Drainage Directed Toward Front and Rear of Dwelling
Figure 5.11: Conventional storm drainage
(Dewberry and Davis 1996).
50
-------
100-Fcot Maximum Overlaid Flow el Minimum 1% Slope
I*
Channel Bottom ij|i|iViai Swale
© 2% miii
jh A A A A A ^ Jk A JL A
-Street-
PLAN VJEW
10 Feet -
Hi =1111=1 ill = 1.1'= .i.hKVI.Si.tKsl.l
\ I "" ]
i^A^N- ljv=U.S Ife
SP!:1 SI -I =Jhl SIli IS-' I! =11 ~^^"~" ii i I T i r ^|V~ !!| Sl'l 'Still £
ELEVATION
Figure 5.12: Illustration of hydrologically functional landscape
(Prince Georges County 1997).
51
-------
5.4.2 Determination of runoff volumes using NRCS method
Each developed land use is assigned a curve number (CN) based upon work done by the Soil
Conservation Service (1986). The initial abstraction, or available storage, is estimated by the
following equation:
/ =200-2 5.1
a CN
The final list of 10 permeable and 16 impermeable candidate land uses with their expected
effectiveness as measured by their curve number (CN) and the associated initial abstraction in
inches, calculated using equation 5.1, are shown in table 5.12. The CNs range from 25 to 98.
The initial abstraction associated with a CN of 25 is 6.00 inches of precipitation. Making this
land impervious increases the CN to 98 with an associated initial abstraction of only 0.04 inches,
a major loss of infiltration capacity. Using unit costs in $/square feet, which are developed in
section 5.5 (and detailed in Heaney et al. 1999a) and having determined the appropriate
abstraction, it is possible to convert the control option costs to $/gallon, which is done in the last
four columns of table 5.12. Several different functional land uses are given in table 5.12. These
include two kinds of aspens, fair, and good (referring to the health and density of the stand), two
kinds of driveways, permeable and impermeable, three types of grass cover, good, fair, and poor
(again referring to health and density), four types of parking, a traditional impervious surface,
and three of gradually increasing porosity, two types of patios, permeable and impermeable, two
kinds of roofs, with retention and without, two kinds of sidewalks, permeable and impermeable,
storage (detention pond), four types of streets, a traditional street profile with curb and gutter, a
street with curb and gutter and porous pavement, an impervious street with swales, and a street
with porous pavement and swales, two types of swales of progressively greater infiltration
capacity (and greater area), and two kinds of wooded areas, fair and poor, again referring to
health and density of the trees.
These values are unique to the soil type heading the column. The NRCS method aggregates clay
and silt together as soil type "B", and rock as soil type "D". Unit costs expressed as $/gallon are
useful for comparative purposes, as will be seen later.
5.4.3 Breakdown of calculated volumes per function
A functional analysis within each land use and soil classification was performed by adding the
total areas for the functions of roof, lawns, driveways, and parking (for non-right of way uses),
and streets, curbs, parking, sidewalks, and lawns for right of way areas. Volumes of developed
runoff can then be calculated by multiplying the initial abstraction by the appropriate area.
Predevelopment runoff can be calculated by using the composite curve number for Happy Acres
prior to development of 63.07, determining an initial abstraction for each soil group, and
multiplying this again by the area as done for the developed volumes. The result of this analysis
is found in table 5.13. This provides a snapshot of the increase in runoff volume for each land
use generated by development. Because the NRCS method is unique to soil characteristics, this
is further broken down by soil group.
52
-------
Table 5.12: SCS hydrologic classifications, and calculation of unit storage values, 1/99$
No.
1
2
1
2
3
4
5
6
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
Type
Permeable
Permeable
Impervious
Impervious
Permeable
Permeable
Permeable
Impervious
Impervious
Impervious
Impervious
Impervious
Impervious
Impervious
Impervious
Impervious
Impervious
Permeable
Impervious
Impervious
Impervious
Impervious
Permeable
Permeable
Permeable
Permeable
Cover Description
Cover type and hydrologic condition
Aspen-mountain brush mixture: Fair:30-
70% ground cover
Aspen-mountain brush mixture: Good:
>70% ground cover
Driveway
Driveway-porous pavement
Lawns, pasture, grassland: Fair condition
(grass cover 50-75%)
Lawns, pasture, grassland: Good condition
(grass cover >75%)
Lawns, pasture, grassland: Poor condition
(grass cover < 50%)
Parking
Porous parking 1
Porous parking 2
Porous parking 3
Patio
Porous patio
Roof
Roof with detention
Sidewalks
Sidewalks with porous materials
Storage-off-site in infiltration/detention
basins
Street with curb and gutter
Street with curb and gutter and porous
pavement
Street with swales
Street with swales and porous pavement
Swales 1
Swales 2
Woods: Fair: Woods are grazed but not
burned, and some forest litter
Woods:Good: Woods without grazing, and
adequate litter and brush
ID
Aspen F
Aspen G
Driveway 1
Driveway 2
Grass F
Grass G
Grass P
Parking 1
Parking 2
Parking 3
Parking 4
Patio 1
Patio 2
Roof 1
Roof 2
Sidewalk 1
Sidewalk 2
Storage
Street 1
Street 2
Street 3
Street 4
Swales 1
Swales 2
Woods F
Woods G
Curve Number
A
28
25
98
70
49
39
68
98
61
46
36
95
76
95
85
98
70
15
98
70
76
61
46
29
36
25
B
48
30
98
80
69
61
79
98
75
65
55
95
85
95
85
98
80
20
98
80
85
75
65
50
60
55
C
57
41
98
85
79
74
86
98
83
77
67
95
89
95
85
98
85
35
98
85
89
83
77
62
73
70
D
63
48
98
87
84
80
89
98
87
82
72
95
91
95
85
98
87
40
98
87
91
87
82
67
79
77
Initial Abstraction in inches
A
5.14
6.00
0.04
0.86
2.08
3.13
0.94
0.04
1.28
2.35
3.56
0.11
0.63
0.11
0.35
0.04
0.86
11.33
0.04
0.86
0.63
1.28
2.35
4.90
3.56
6.00
B
2.17
4.67
0.04
0.50
0.90
1.28
0.53
0.04
0.67
1.08
1.64
0.11
0.35
0.11
0.35
0.04
0.50
8.00
0.04
0.50
0.35
0.67
1.08
2.00
1.33
1.64
C
1.51
2.88
0.04
0.35
0.53
0.70
0.33
0.04
0.41
0.60
0.99
0.11
0.25
0.11
0.35
0.04
0.35
3.71
0.04
0.35
0.25
0.41
0.60
1.23
0.74
0.86
D
1.17
2.17
0.04
0.30
0.38
0.50
0.25
0.04
0.30
0.44
0.78
0.11
0.20
0.11
0.35
0.04
0.30
3.00
0.04
0.30
0.20
0.30
0.44
0.99
0.53
0.60
Unit
cost
$/sf
$2.00
$3.00
$0.23
$0.25
$0.81
$1.03
$0.70
$0.23
$0.25
$0.26
$0.28
$0.19
$0.19
$0.00
$1.50
$0.19
$0.19
$5.00
$0.25
$0.26
$0.27
$0.28
$3.00
$6.00
$0.80
$1.40
Unit Costs in $/gallons
A
$0.62
$0.80
$9.21
$0.47
$0.63
$0.53
$1.19
$9.21
$0.31
$0.18
$0.13
$2.89
$0.49
$0.00
$6.82
$7.44
$0.36
$0.71
$9.77
$0.49
$0.68
$0.35
$2.05
$1.97
$0.36
$0.37
B
$1.48
$1.03
$9.21
$0.80
$1.45
$1.29
$2.12
$9.21
$0.60
$0.39
$0.27
$2.89
$0.88
$0.00
$6.82
$7.44
$0.62
$1.00
$9.77
$0.84
$1.22
$0.67
$4.47
$4.81
$0.96
$1.37
C
$2.13
$1.67
$9.21
$1.13
$2.45
$2.35
$3.45
$9.21
$0.98
$0.71
$0.46
$2.89
$1.25
$0.00
$6.82
$7.44
$0.88
$2.16
$9.77
$1.19
$1.74
$1.09
$8.06
$7.85
$1.73
$2.62
D
$2.73
$2.22
$9.21
$1.34
$3.42
$3.30
$4.55
$9.21
$1.34
$0.97
$0.58
$2.89
$1.57
$0.00
$6.82
$7.44
$1.04
$2.67
$9.77
$1.41
$2.17
$1.49
$10.9
6
$9.77
$2.41
$3.76
Source: adapted from SCS, 1986
53
-------
Table 5.13: Calculation of developed and predevelopment stormwater volumes for
Happy Acres
Land Use
Apartments
Commercial
MD Residential
LD Residential
School
Streets
50
60
70
Function
Roof
Parking
Driveway
Lawns
Roof
Parking
Driveway
Lawns
Roof
Parking
Driveway
Lawns
Patio
Roof
Parking
Driveway
Lawns
Patio
Roof
Parking
Driveway
Lawns
ROW
Street with
curb and gutter
Parking
Sidewalks
curb
Lawns
ROW
Street with
curb and gutter
Parking
Sidewalks
curb
Lawns
ROW
Street with
curb and gutter
Parking
Sidewalks
curb
Lawns
Total
Soil
Types
B
sf
46927
75083
0
40670
95132
44810
0
6839
140800
0
52800
353666
17600
102000
0
40800
491233
20400
69080
51806
0
28521
659728
105556
105556
105556
52778
52778
87540
11672
23344
11672
5836
5836
13195
1508
3016
1508
754
754
Soil
Types
D, Total
sf
0
0
0
0
57707
259868
0
16714
267200
0
100200
538755
33400
0
0
0
0
0
0
0
0
0
774288
123886
123886
123886
61943
61943
0
0
0
0
0
0
189531
21661
43321
21661
10830
10830
Area, Total
sf
46927
75083
0
40670
152839
304678
0
23553
408000
0
153000
892420
51000
102000
0
40800
491233
20400
69080
51806
0
28521
1434016
229443
229443
229443
114721
114721
87540
11672
23344
11672
5836
5836
202726
23169
46337
23169
11584
11584
1724282
Volume
Developed, B
cf
412
255
0
4334
834
152
0
729
1235
0
180
37686
154
895
0
139
52344
179
606
176
0
3039
359
359
359
180
3952
40
79
40
20
437
5
10
5
3
56
Volume
Developed, D
cf
0
0
0
0
506
884
0
696
2344
0
341
22448
293
0
0
0
0
0
0
0
0
0
421
421
421
211
1966
0
0
0
0
0
74
147
74
37
344
Total Vol.
Developed
cf
412
255
0
4334
1341
1036
0
1425
3579
0
520
60134
447
895
0
139
52344
179
606
176
0
3039
780
780
780
390
5918
40
79
40
20
437
79
158
79
39
400
140882
Volume
Undev.,
B
cf
4580
7327
0
3969
9284
4373
0
667
13741
0
5153
34514
9954
0
3982
47939
6742
5056
0
2783
10301
10301
10301
5151
5151
1139
2278
1139
570
570
147
294
147
74
74
Volume
Undev.,
D
cf
0
0
0
0
49
86
0
68
229
0
33
2191
0
0
0
0
0
0
0
0
41
41
41
21
192
0
0
0
0
0
7
14
7
4
34
Tot. Volume
Undev.
cf
4580
7327
0
3969
9333
4459
0
735
13969
0
5186
36705
0
9954
0
3982
47939
0
6742
5056
0
2783
10342
10342
10342
5171
5343
1139
2278
1139
570
570
154
309
154
77
107
210758
54
-------
The functions were then compared across land uses by computing the difference between
the sum of the function's pre-development and post-development storage volumes. The
result is plotted as a bar chart in figure 5.13. The greatest impact is from streets and
roofs, with roughly equal values of storage volume reduction. Patios are insignificant in
this analysis. Lawns actually add a great deal of storage, offsetting somewhat the drastic
reductions from roofs and streets. Driveways and parking lots result in smaller
reductions in volume, however, the local impact may be significant.
140000
120000
100000
80000
.S> 60000
.Q
3
o
£
f= 40000
20000
DVolume, post development, (CF)
Volume, predevelopment (CF)
D Difference
-20000
-40000
Function
Figure 5.13: Allocation of available storage for initial abstraction and land use.
5.5 Simulation Tools for Cost Analysis
If the cost of modifying the CNs can be determined, then cost-effective strategies can be
developed for maintaining the undeveloped CN for each parcel or combination of parcels.
Since most BMPs are land intensive, a careful evaluation of their costs must include land
valuation. The costs used in the analysis were developed in Heaney et al. (1999), for
each control and each land use. The procedure for calculation of the land component of
controls within one land use, medium density residential, is outlined in table 5.14.
55
-------
Table 5.14: Land valuation for medium density lot, 1/99$
Component
Roof-house
Roof-garage
Driveway
Yard
Patio
Total
SF
1200
400
600
3600
200
6000
%of
total
20.0%
6.7%
10.0%
60.0%
3.3%
100.0%
$/sf
$56.25
$34.00
$4.00
$1.00
$4.00
Construction
Cost, $
$67,500
$13,600
$2,400
$3,600
$800
$87,900
Total Land $
$8,790
$2,930
$4,395
$26,370
$1,465
$43,950
Unimproved
Land, $
$5,860
$1,953
$2,930
$17,580
$977
$29,300
An estimate of the cost in $/sf is found in column 4 of table 5.14. Next, the construction
cost (column 5) is obtained by multiplying column 2 by column 4. Next, the percentage
in column 3 is multiplied by the total of column 5 to obtain an estimate of the land cost,
in column 6. Column 7, the unimproved land cost, is obtained by multiplying the values
in column 6 by 2/3. The value of the 3,600 square feet of land for the yard function is
$26,370.
Next, opportunity costs must be calculated. This procedure is illustrated in table 5.15.
The value of $26,370 is annualized, using an interest rate of 6%, and an infinite term (as
in equation 6.2), to obtain $l,582/year. Then, this value is spread over 25 years at 6%, to
obtain $20,226. Dividing this value by 3,600 square feet gives $5.62/square feet. This
value is used for all grass types as the underlying value of the land is assumed to be
constant irrespective of the type of grass. Landscaping costs were developed from RS
Means (1996b), and updated to January 1999, and are presented in table 5.15 (for a
medium density residential lot). The initial capital investment consists of the cost of soil
preparation including sod, topsoil, and soil conditioners, and an irrigation system. For a
good lawn, the present value of the initial landscaping investment is $2.22 per square
foot. Costs for lesser quality lawns drop to $1.71/sf and $.95/sf for fair and poor quality
lawns. For the good lawn system, operation and maintenance costs add an additional
$2.45 per square foot bringing the total to $10.29 per square foot. An estimated 10
percent of this total cost is allocated to stormwater management. Similar estimates were
made for fair and poor lawns. The resulting total costs per square foot vary from $0.70 to
$1.03 per square foot. Better lawns have a lower CN and are thereby preferable from the
viewpoint of being able to store more water.
Similar estimates were made for the land valuation for low-density residential lots,
commercial, apartments, and schools. A similar procedure was followed for these uses,
except that the commercial, apartments, and schools are aggregated as one lot. However,
they also cost more. The cost for each control was then estimated using these land
valuations. The matrix of controls and land uses is presented in table 5.16. A linear
programming model is used to find the least costly mix for each land use. See Heaney et
al. (1999b) for a more detailed explanation of this method.
56
-------
Table 5.15: Cost analysis of landscaping for medium density lot, 1/99$
Item
A. Initial Capital Investment
1 . Soil preparation
Initial cost of sod
Initial cost of topsoil, 6"
Spreading topsoil, 6"
Soil conditioners
Sprinkler system
2. Opportunity Cost of Land
Land Investment Cost
Opportunity cost investment rate
Annual cost, $/yr.
Interest rate per year
Present worth over 25 years
Cost in $/ft2
Total of initial capital investment
B. Operation & Maintenance Costs, $
Lawn watering
Inches per year
% of pervious area that is irrigated
Cost of water, $/1 ,000 gallons
Present worth factor
Present worth, $/ft2
Lawn maintenance
Weeks per year
$/week
Maintenance area, ft2
Present worth, $/ft2
Sprinkler system maintenance
Total operation and maintenance costs, $
C. Total Cost, $/ft2
Portion attributable to stormwater
Assumed %
D. Cost for Stormwater
Input
Data
$26,370
6%
$1,582
0.06
$20,226
20
80%
$1.50
12.78
26
$8.46
2880
10%
Good
$/ft2
$0.43
$0.50
$0.64
$0.03
$0.62
$2.22
$5.62
$7.84
$0.24
$0.98
$0.25
$1.46
$9.31
$0.93
Fair
$/ft2
$0.34
$0.40
$0.51
$0.02
$0.44
$1.71
$5.62
$7.33
$0.15
$0.50
$0.15
$0.80
$8.13
$0.81
Poor
$/ft2
$0.26
$0.30
$0.38
$0.01
$0.00
$0.95
$5.62
$6.57
$0.09
$0.35
$0.00
$0.44
$7.01
$0.70
57
-------
Table 5.16: Calculation of unit costs for controls, including opportunity costs for land,
1/99$
ID
Aspen F
Aspen G
Driveway 1
Driveway 2
Grass F
Grass G
Grass P
Parking 1
Parking 2
Parking 3
Parking 4
Patio 1
Patio 2
Roof 1
Roof 2
Sidewalk 1
Sidewalk 2
Storage
Street 1
Street 2
Street 3
Street 4
Swales 1
Swales 2
Woods F
Woods G
LDRes
$/sf
$2.00
$3.00
$0.23
$0.25
$0.60
$0.69
$0.49
$0.23
$0.25
$0.26
$0.28
$0.19
$0.19
$0.00
$1.50
$0.19
$0.19
$5.00
$0.25
$0.26
$0.27
$0.28
$3.00
$6.00
$0.80
$1.40
MDRes
$/sf
$2.00
$3.00
$0.23
$0.25
$0.60
$0.69
$0.49
$0.23
$0.25
$0.26
$0.28
$0.19
$0.19
$0.00
$1.50
$0.19
$0.19
$5.00
$0.25
$0.26
$0.27
$0.29
$3.00
$6.00
$0.80
$1.40
Commercial
$/sf
$2.00
$3.00
$0.23
$0.25
$2.12
$2.18
$2.01
$0.23
$0.25
$0.26
$0.28
$0.19
$0.19
$0.00
$1.50
$0.19
$0.19
$5.00
$0.25
$0.26
$0.27
$0.28
$3.00
$6.00
$0.80
$1.40
School
$/sf
$2.00
$3.00
$0.23
$0.25
$2.49
$2.56
$2.38
$0.23
$0.25
$0.26
$0.28
$0.19
$0.19
$0.00
$1.50
$0.19
$0.19
$5.00
$0.25
$0.26
$0.27
$0.28
$3.00
$6.00
$0.80
$1.40
Apartments
$/sf
$2.00
$3.00
$0.23
$0.25
$1.22
$1.29
$1.11
$0.23
$0.25
$0.26
$0.28
$0.19
$0.19
$0.00
$1.50
$0.19
$0.19
$5.00
$0.25
$0.26
$0.27
$0.28
$3.00
$6.00
$0.80
$1.40
RW50
$/sf
$2.00
$3.00
$0.23
$0.25
$0.60
$0.69
$0.49
$0.23
$0.25
$0.26
$0.28
$0.19
$0.19
$0.00
$1.50
$0.19
$0.19
$5.00
$0.25
$0.26
$0.27
$0.28
$3.00
$6.00
$0.80
$1.40
RW60
$/sf
$2.00
$3.00
$0.23
$0.25
$0.60
$0.69
$0.49
$0.23
$0.25
$0.26
$0.28
$0.19
$0.19
$0.00
$1.50
$0.19
$0.19
$5.00
$0.25
$0.26
$0.27
$0.29
$3.00
$6.00
$0.80
$1.40
RW70
$/sf
$2.00
$3.00
$0.23
$0.25
$0.60
$0.69
$0.49
$0.23
$0.25
$0.26
$0.28
$0.19
$0.19
$0.00
$1.50
$0.19
$0.19
$5.00
$0.24
$0.26
$0.27
$0.28
$3.00
$6.00
$0.80
$1.40
58
-------
5.6 Optimization of Control Options for Happy Acres
The results of the LP optimizations are summarized in tables 5.17 and 5.18. The results
are allocated along functional grouping within each soil class in table 5.17, and
aggregated for each land use type in table 5.18. The least cost design allocates the
appropriate control option to the appropriate soil type and land use (soil is reflected in its
predevelopment CN, land use is reflected in the influence of land valuation on the cost of
the control). The changes in control options affect the appearance of the neighborhood,
and this is evident by inspection of table 5.17. For example, porous pavements were
selected (with curb and gutter) for the street design in the rocky soil. In the clay and silt
soils where more percolation can take place, the LP model selected a street design with
porous pavement and swales instead of curb and gutter. A similar allocation took place
with parking areas; both were porous, however, the more permeable soils resulted in a
design that had a higher infiltration capacity. The more permeable driveway, patio, and
sidewalk choices were chosen in both soil types. Good grass was selected over the other
options for all soil areas, except in commercial areas where poor was selected in silts and
clays, and fair was selected in rock. This is due to the relatively small amount of
landscaped area in commercial areas. There may be other aesthetic factors with
commercial areas that would put a higher premium on a higher quality grass other than
for a stormwater quality function. Aspens were chosen, but in small amounts, so it would
not look significantly different than a typical subdivision. The roof choice remained the
standard, rather the roof with detention, due to its relatively high unit cost. Storage was
chosen when no other controls were feasible, the highest values, as expected, were in
commercial areas with rocky soils, which would not have much infiltration capacity.
The cost of the optimal solution for each soil class and land use is found in table 5.18.
The total cost for the controls would be $5.2 million, some of which overlaps with money
that would be spent for landscaping anyway. About half of this amount is used to attempt
to control runoff from transportation related functions.
What differs from a traditional subdivision development is the allocation of use. A
traditional subdivision would have allocated everything in ground cover to the high
quality grass, (particularly for commercial areas) and neglected the woods and aspens
(although some exceptions to this exist, mainly for aesthetics). In commercial areas, the
detention storage, would have been utilized. For sidewalks, patios, streets, and parking
lots, nonporous pavement would have been chosen. Curb and gutter would have replaced
swales along street rights of way.
An important note here is that this DSS cannot dynamically change land uses. For
example, the net amount of area used for rights of way, 39 acres out of the 106 total (see
table 5.18), must remain the same. Likewise, the amounts and locations for medium
density and low density, as well as the other land uses, must remain the same. What has
been done here, however, is to attempt to allocate storage optimally throughout each of
these land uses. A more general problem exists which would allow tradeoffs between the
land uses. This problem is extremely complex because it involves re-creation of the GIS
for each iteration.
59
-------
Table 5. 17: Resu
Its of LP o
Street 1
Street 2
Street 3
Street 4
Sidewalk 1
Sidewalk 2
Grass P
Grass F
Grass G
Swales 1
Swales 2
Storage
Parking 1
Parking 2
Parking 3
Parking 4
Roof 1
Roof 2
Driveway 1
Driveway 2
Patio 1
Patio 2
Woods F
Woods G
Aspen F
Aspen G
ptimization-land use allocation by function (includes opportunity costs)
Land Use Area in Soil Group B in acres
50
9.69
2.42
3.03
1.00
60
1.08
0.21
0.26
0.06
70
0.03
0.01
0.01
0.00
LD
11.23
2.34
0.94
0.47
0.25
MD
6.49
3.23
1.21
0.40
0.79
HD
0.57
0.04
1.72
1.08
0.36
Comm
0.16
0.14
2.04
1.03
Sch
0.65
0.24
1.19
1.59
Land Use Area in Soil Group D in acres
50
11.38
0.50
1.12
0.83
60
0.00
0.00
0.00
0.00
70
2.74
0.50
1.12
0.83
LD
0.00
0.00
0.00
0.00
0.00
0.00
MD
4.60
0.35
6.13
2.30
0.77
9.20
HD
0.00
0.00
0.00
0.00
0.00
Comm
0.38
2.15
1.32
5.97
Sch
0.00
0.00
0.00
0.00
0.00
60
-------
Table 5.18: Least-cost LP solutions for land Use/BMP options (including land costs) for Happy
Acres.
Land Use
50 ft ROW
60 ft ROW
70 ft ROW
Low Density Residential
Medium Density Residential
Apartments
Commercial
School
SUM
SoilB
Area (acres)
15.15
1.55
0.05
15.02
12.97
3.73
3.37
3.43
55.27
SoilD
Area (acres)
17.78
0.00
4.35
0.00
21.57
0.00
7.67
0.00
51.37
Total
(acres)
32.92
1.55
4.41
15.02
34.54
3.73
11.04
3.43
106.64
Land Use
50 ft ROW
60 ft ROW
70 ft ROW
Low Density Residential
Medium Density Residential
Apartments
Commercial
School
Cost in Soil B, $
$443,554
$36,463
$1,058
$376,677
$361,197
$98,633
$39,267
$106,305
$1,463,153
Cost in Soil D, $
$1,484,917
$-
$247,981
$-
$1,509,515
$-
$517,237
(D
J>-
$3,759,650
TOTAL
Sum, $
$1,928,471
$36,463
$249,039
$376,677
$1,870,712
$98,633
$556,503
$106,305
$5,222,803
$5,220,000
5.7 Decision Support Systems and the Happy Acres Case Study
The previous sections have illustrated how a simple hydrologic model can be constructed with
basic GIS information. The methods presented in this report allow hydrologic and economic
analysis to be performed on micro scales not traditionally used in urban analysis. These micro
scales, although unfamiliar, must be used to properly evaluate BMPs for the control of locally
generated stormwater runoff. This same information can be used as building blocks for SWMM.
SWMM aggregates information in a manner controlled by the user, into an equivalent
rectangular catchment. Several methods of aggregation are available within SWMM add-on
packages (such as PC SWMM). Unfortunately, this method homogenizes the parcels within each
subcatchment, i.e., they lose their unique hydrologic characteristics. The aggregation was
typically done so that the user was not overwhelmed by data, as most had to be handled
manually. However, within the context of a DSS, appropriate tools can be used to process the
data, so smaller scales may be evaluated.
A disadvantage of the DSS process used in this case study and outlined in figure 5.1 is that most
of the analysis is one way, i.e., there is not a true interchange of information between the
modules. The most obvious example is the GIS. It would be desirable to optimize land use in a
general form of a land allocation model considering the effects of land valuation, soils, and
control options. In order to do this efficiently, the spatial database underlying the parcel
delineation must be re-created for each iteration of the model. Of course, this level of integration
is also the most difficult and expensive.
61
-------
6.0 Summary and Conclusions
6.1 Summary
In summary, GIS has transformed our approach to the urban stormwater management problem.
Not only are input parameters in the model itself becoming more easily obtainable, but also the
scale of possible evaluations has decreased to a point that it is now possible to effectively
evaluate source controls. The case study process shown in figure 5.1 provides a preliminary
evaluation of the complex urban stormwater problem and the linked problem of allocation of
land use. Several models exist that utilize GIS information; the degree of integration that is
desirable remains debatable. Due to the widely disparate spatial scales involved, and the detailed
amount of information available in a GIS, it is quite possible for the analyst to be drowned in
data that may not be needed in evaluating the problem. The urban stormwater problem needs to
be of primary concern to the analyst; rather than the micro maintenance of the GIS. The problem
should be the primary focus, even more so than the model, or the database used. As the models
evolve into more general Decision Support Systems, they will tend to become more data
centered, and computational engines more interchangeable. The GIS data will become more
available and standardized, and will be an important tool. One lesson to be learned from the 90s
and the computer software explosion that has transformed the working world is that too much
reliance on any one technology can lead to obsolescence. DSS promises to be the technology
that links many of these tools together to enable the analyst to explore new challenging problems
in old contexts.
6.2 Conclusions
Advances in development of computer software have produced two key linked technologies:
relational databases and geographic information systems. The combination of these two has
affected the development of another technology, decision support systems, that has been applied
to complex unstructured water resources and environmental problems. Most DSSs include these
two technologies, with the addition of simulation models, an evaluation tool (can include
optimization), and a graphical user interface. The graphical user interface, mainly the MS-
Windows interface, is another advance that has both transformed software as well changed the
standard of model development. Construction of programs within this environment tends to be
more difficult due to its object oriented architecture, however, it is also inherently more dynamic
than constructing programs within older environments such as FORTRAN-77. This is primarily
due to the advent of structured programming techniques that tend to keep data handling
processes out of the main program files, which tends to advance a more data centric approach to
modeling. The structured techniques also avoid the use of "spaghetti code" in which it is
difficult to debug code due to vague loops and "GOTO" statements that branch the program in
many different directions.
New types of solvers are now available that can serve as better evaluation tools for a DSS.
These include genetic algorithms (GA), simulated annealing (SA), and the relative ease with
which linear programming (LP) solvers are used. These optimization tools allow rapid
evaluation of both linear and nonlinear problems, which can assist the designer in finding the
better or best solution.
62
-------
Urban stormwater models have been created according to specific needs and available funding.
The predominant US model, SWMM, was created in the late 60s and early 70s. There is an
active user community for this largely public domain model. Several enhancements to the
model, namely PC SWMM, Visual Hydro (XP SWMM), and MikeSWMM, are now available in
the private domain as well. These enhancements contain facilities that include graphical user
interfaces for ease of program use, GIS and CAD interfaces for construction of models based
upon the best available system mapping, and external links to available databases to enhance the
use of available system data. European models, in particular the DHI and the HR-Wallingford
series, have been significantly ahead of the US modeling community in the use of GUIs and GIS.
The reason for this gap is primarily the result of funding. Funding for urban stormwater
modeling in the US ceased in the early 80s. Meanwhile, the European models were developed
and enjoyed significant funding during the 80s and early 90s from both national governments as
well as the European Union. These models may have become self-supporting by the creation of
companies that sell the licensed product. This enables future enhancements in the models to be
made, as well as user support from a centralized source.
The US should focus its efforts on the use of linked technologies to take advantage of significant
savings that can be realized by avoiding the re-creation of common tools currently available. For
example, spreadsheet technology in the US has been effectively standardized upon MS Excel
(even if you don't use it, you use a program that can read these files). Input and output
processing within new models could make use of this application, which would allow the user
greater flexibility in terms of pre- and post-processing of model output. Visual Hydro provides a
good example of the use of spreadsheet tools for data input and output. The US has been a
leader in the GIS and database software development field; available links to these programs will
continue to evolve and interfaces with GIS should become easier to construct than those at
present. A significant portion of this effort is the development of both the graphic features of the
GIS and the associated system attributes as well. The case study outlined in this report, although
using a simplified hydrologic model, provides a possible outline of the use of this data for
problems that have remained intractable to this point, for example, the selection of the
appropriate BMP control technology for each parcel. Further work needs to be done to enhance
the development of DSS technology to the urban stormwater field. The funding resources should
carefully target the development of models and DSSs that link available tools rather than re-
create them, and provide a common set of technologies that the user may combine with other
available software. The funding should also seek to complement or prod the development of
existing commercial software, rather than supplant the market by the introduction of competing
products. A possible model could also be the European model community, in which the
government funds the initial development of the model, then licenses it to a nonprofit company
that markets and sells the model at a self-sustaining price.
Care should be taken in that as the model interfaces become easier to run, they may be used
inappropriately. A stated goal within the DSS community is to bring the computing power to the
level of the decision-maker, rather than an intermediary. This works well if the decision-maker,
or their assistant, is trained in the field of urban stormwater. The field of urban stormwater
modeling involves the use of complex boundary conditions. Using GIS involves the use of
wildly different scales where the uncertainty in the information may not be immediately evident
63
-------
to the user. Such complex problems require a technically competent professional to carefully use
and evaluate the information the DSS presents. Rather than simply using a sophisticated set of
tools to solve the same problem more efficiently than we can at present, the problems evaluated
will become more complex as well as the possible array of solutions to them. The advent of DSS
and its inherent technologies, relational databases and GIS, have transformed the field of urban
stormwater modeling and allow the evaluation of previously intractable problems.
64
-------
Abstract
This report reviews the application of Geographic Information System (GIS) technology to the
field of urban stormwater modeling. The GIS literature is reviewed in the context of its use as a
spatial database for urban stormwater modeling, integration of GIS and hydrologic time series,
and integration of GIS and urban stormwater models (from both a software and management
perspective). The available urban stormwater modeling software is reviewed and discussed with
respect to their GIS integration capabilities. Decision Support Systems (DSS) are reviewed with
respect to their integration with GIS, and their applicability to urban stormwater management
problems. A simplified neighborhood scale DSS is presented that includes a GIS, a database, a
stormwater system design template, and an optimization capability for screening alternatives.
The area and soil based NRCS method is used for calculating runoff from GIS information.
Using economic analysis that compares the costs of controls, including the opportunity cost of
land for land intensive controls, the optimal selection of Best Management Practice (BMP)
controls was accomplished by use of a linear programming (LP) method. The intent of this
presentation is to provide an example of the types of problems that become possible to explore
with the application of DSS and GIS technology on a small scale. This field is evolving rapidly,
and warrants carefully targetted research efforts, particularly at developing nonspecific software
tools that aid in integrating existing models.
-------
1.0 Introduction
A mathematical model of an urban hydrologic response to precipitation usually requires
extensive data due to the complexity of surfaces, flow paths and conduits found in developed
locales. Many of these data are geographic in nature; e.g., geographic boundaries of the
hydrologic basin provide boundary conditions of the mathematical model. Therefore the
marriage of mathematical stormwater models and geographic information systems (GIS) is a
natural development of simulation and database technology. The relationship between urban
stormwater models and GIS may take many forms. This is apparent from the nearly 50 journal
articles, conference proceedings and internet reports surveyed for this review of recent literature.
The relationship between GIS and urban stormwater models may be distinct, where the GIS
functions as a separate pre- and post-processor; or the distinction may be blurred, where the
model is seamlessly integrated to the GIS.
The purpose of this report is to accomplish several tasks. In chapter 2 a review of technical
literature is performed to determine how GIS is being used in the field of urban storm stormwater
modeling. Next, in chapter 3, the predominant urban stormwater models are reviewed within the
context of the taxonomy developed in chapter 2. Then, in chapter 4, looking at the future
directions of urban stormwater models, Decision Support Systems (DSSs) are described. DSS is
now being used extensively for river basin modeling, particularly in the hydropower context.
This type of system lends itself to unstructured problems where data integration is a key to
evaluation of the problem. The various components of DSS including models, database
structure, GIS, optimization, and time series management are discussed. A process level DSS is
developed for a textbook subdivision in chapter 5. This DSS contains a GIS, including graphic
features and a relational database, a system simulator, and an optimizer. Stormwater design
templates were created using Excel spreadsheets, paralleling the design problem from the
textbook. Next, GIS data were utilized in a simple hydrologic model using the NRCS (National
Resources Conservation Service) method. This data was combined with unit cost data into a
linear programming model (LP) in order to develop the least costly mix of BMP controls that
maintain the same initial abstraction after development as before. Suggestions for further
improvement of the DSS are made by comparison of the DSS structure with those found in
chapter 4. Finally conclusion are presented in chapter 6.
-------
2.0 Literature Review
2.1 Overview of Sources of Reviewed Literature
The GIS literature is broad, due to the wide variety of areas that utilize geographic data.
Likewise, the literature describing GIS applications in water resources is itself very broad.
However, much of this work in water resources has been in the area of natural hydrology and
large-scale, river-basin hydrology. GIS has a long history in this area due in large part to the
early availability of remotely sensed spatial data suited for this purpose. A good overview of the
concepts of GIS and database technology and their application within the field of natural systems
hydrology is found in Singh and Fiorentino (1996).
The use of GIS in modeling urban stormwater systems has been more limited due to the need for
large, expensive and detailed spatial and temporal databases, along with the fact that many
computer tools used in urban stormwater modeling are not easily amenable to integration with
GIS. However, as local data gathering efforts have increased and software integration has
evolved, the use of GIS in urban stormwater is now widespread. Shamsi et al. (1995) estimate
that more than 70% of the information used by local governments is georeferenced. Much of
this information has been, or will be, transferred to a digital format, usually a GIS.
Recent literature was found in several distinct fields. From the water resources field, recent
conferences focusing on urban stormwater have several papers on GIS. Proceedings from two
European conferences on urban stormwater by Butler and Maksimovic (eds. 1998), and Seiker
and Verworn (eds. 1996), have a wealth of current information on GIS. The American Water
Resources Association (AWRA) has sponsored conferences specific to the use of GIS in water
resources, such as Harlin and Lanfear (1993) and Hallam et al. (1996). These reports have
sections devoted to urban stormwater, of which modeling is a recurring theme. Significant
literature in this area was also found on the internet. The Center for Research in Water
Resources at The University of Texas at Austin has a large online library of reports and papers
on the use of GIS for hydrologic research, some of which concerns the modeling of urban areas
(University of Texas, 1998).
Other resources were found in the GIS field. One software provider, Environmental Systems
Research Institute (ESRI), hosts a large annual international user conference. The proceedings
for these conferences are located on the internet at http://www.esri.com (ESRI 1998). The
International Association of Hydrological Sciences (IAHS) publishes the proceedings from its
many conferences, some of which have dealt specifically with the integration and application of
GIS and water resources management (e.g. Kovar and Nachtnebel 1996). Other IAHS
conferences have focused on applications, which usually have several papers on using GIS for
that application. For example Simonovic et al. (1995) edited "Modeling and Management of
Sustainable Basin-Scale Water Resource Systems", proceedings from a 1995 conference in
Boulder, CO. which contained several papers on GIS and model integration.
2.2 GIS as a Spatial Database for Urban Stormwater Modeling
The most basic role a GIS can play in the modeling of urban stormwater is that of a simple pre-
processor of spatial data. As a pre-processor, GIS may simply store geographic information in a
database, or it may be used to calculate model-input parameters from stored geographic data.
-------
Frequently data are exported from the GIS in a file format consistent with a model-input file. As
a post-processor, GIS may be used to map water surface elevations, concentrations, etc., or to
derive spatial statistics based on model output. Shamsi (1998) describes the batch transfer of
data from a GIS to SWMM as the interchange data. The GIS and SWMM are operated
separately, with no direct interlink. The GIS is used to extract data required by SWMM from the
spatial database into a file compatible with a SWMM input file. A recurring theme in recent
literature focuses on the ability to get the most out of data by assuring that information tools are
consistent. This idea has been termed "hydroinformatics" and is especially prominent in the
recent European literature (Fuchs and Scheffer 1996).
2.2.1 GIS as a pre-processor for urban stormwater models
Many municipalities store general spatial information in a GIS, and the information is used for a
wide variety of purposes and functions within the institutional framework. VanGelder and
Miller (1996) describe a typical use of GIS as a spatial database for modeling stormwater from a
municipal airport. Detailed georeferenced data were used in conjunction with maintenance data
to develop an operation and management schedule as well as to link node information needed to
create a SWMM EXTRAN model. Pryl et al. (1998) use a GIS to export details of the urban
stormwater network to a hydraulic simulator for Prague in the Czech Republic. The Danish
Hydraulic Institute (DHI) program Model Of Urban SEwers (MOUSE) was used to simulate
various scenarios for development of an urban stormwater master plan. Rodriguez et al. (1998)
used a GIS to study stormwater characteristics of an urban area in Nantes, France. This study
used the urban land parcel as the base hydrologic unit of a detailed hydrologic model, as opposed
to the more typical basin defined by topography and the layout of the stormwater network. A
detailed water budget was performed around the owner-defined parcel. This physically based
hydrologic model was then used with the stormwater network to analyze the behavior of urban
catchments under a wide variety of storm events. The idea of using small hydrologic units based
on land ownership for urban stormwater modeling is ideally suited for GIS applications and is
useful when simulating the effect of management decisions made at the parcel level.
Sotic et al. (1998) began a preliminary design of CSO facilities in Kumodraz, Yugoslavia with
paper maps. Existing paper maps and other data were used to create a GIS, which in turn was
used to aid in the design and analysis of the CSO system. This "hydroinformatic" approach
consists of developing a set of tools to collect and process data in a consistent manner. The
attention to consistency in data transferability is to assure that the greatest value is achieved from
the dataset. In this case, the GIS was used to integrate a Digital Elevation Model (DEM), the
street network, and the sewer network; then this information was transferred to the BEAMUS
hydraulic simulation model (Sotic et al. 1998). A similar hydroinformatic approach is described
for the town of Pilsen in the Czech Republic by Hora et al. (1998). Beginning with paper maps,
a GIS was built from the ground-up. The complete process is described, ending with an
information tool that was used to create a hydrodynamic model of the sewer system, store
monitored flow and rain data, evaluate current hydraulic sewer capacity and evaluate the
feasibility of alternative sewer developments.
Barbe et al. (1993) integrate data transfer from a GIS and a SCAD A system to a SWMM model
of the Jefferson Parish stormwater stormwater system in Louisiana. The SWMM RUNOFF
block was used to simulate the hydrologic runoff characteristics of the area. Geospatial data
were transferred from the GIS to the SWMM RUNOFF data file. Similarly, the EXTRAN block
-------
was used to simulate the pipe network, and the network connectivity was transferred from the
GIS to the SWMM EXTRAN data file. Time series data from 150 monitoring sites were
transferred from a SCADA system to the SWMM model for calibration purposes.
2.2.2 GIS as a post-processor for urban stormwater models
GIS may also be used to accept model output. Xu et al. (1998) describe a mixed land use
hydrologic model that uses GIS as a pre- and post-processor of model information. For this
application, the model output of time series of simulated flows may be depicted dynamically
through an Arc View interface. Sorensen (1996) describes a typical use of GIS to present model
output, that of depicting flood inundation maps from the GIS. MIKE GIS is a modeling tool
from DHI that interfaces between Arclnfo or Arc View and MIKE, a flood assessment model.
First developed to study flood management in Bangladesh, MIKE GIS uses both the maximum
flood extent and the time series of flooding to analyze expected damages from peak inundation
and the duration of inundation (Sorensen 1996). A key element to this work is that the GIS is
used for more than mapping model output, but that spatial analysis is done with the GIS that adds
to the information gained from the model output alone.
Shamsi (1998) discusses the difference between transferring data files between Arc View and
SWMM and creating an interface that uses SWMM output as a spatial coverage layer in a GIS.
This "interface method" (as opposed to the interchange method described above) involves
creating a SWMM menu within Arc View. Pre- and post-processors of SWMM input and output
files create input files, read output files, and join and unjoin data files (Shamsi 1998). These
options are made available in Arc View; however SWMM is run separately from Arc View
(Shamsi 1998).
2.2.3 GIS used to estimate spatial input parameters
One of the most important hydrographic features of an urban surface is impervious area.
Fankhauser (1998) describes a method to estimate impervious area from color infrared aerial
photographs and orthophotos. These images have a ground resolution of 25 to 75 centimeters. A
raster based GIS, IDRISI, was used to estimate impendousness to within 10% of the value
determined manually for an entire basin. However, the deviation for individual catchments was
much higher. For this reason, this method was recommended only for large project areas where
the high costs of parameter estimation could be justified.
Olivera et al. (1996) use GIS to calculate hydrographic properties of terrain for non-point load
estimation. Flow paths calculated from paths of steepest descent are used to calculate flow
properties of basins. Cluis et al. (1996) use topographic data and GIS functions to derive
important hydrographic characteristics of the terrain such as overland flow paths in a raster based
format. Mercado (1996) describes the use of detailed spatial information in the creation of a
stormwater model in Tallahassee, FL using XPSWMM software. Scanned and georectified
black and white aerial photography was used as a background with other GIS based data,
including two foot contour elevations, streams, buildings, roads, etc. A DEM was created in
Arclnfo, and the Triangulated Irregular Network (TIN) and Grid functions were used to define
areas of high slope and erosion potential, flow gradients and very accurate subbasin delineation
(Mercado 1996).
-------
Herath et al. (1996) used high-resolution raster data sets to develop a distributed GIS-based
urban hydrologic model. Data sets included 50 m x 50 m and 20 m x 20 m land use grids;
1:25,000 plans were used to develop imperviousness by land use, a 50 m x 50 m DEM,
population density, water supply data, and rainfall. Herath et al. (1996) integrated the hydrologic
model with the GIS, by writing the numerical simulation codes within the GIS, thus reducing
problems of data transfer. However, the computational time was felt to be too high for practical
use due to inefficiencies of performing the hydrologic simulation within the GIS (Herath et al.
1996).
Olivera et al. (1998) developed a GIS-based preprocessor for the new HEC-HMS model
developed by the Army Corps of Engineers' Hydrologic Engineering Center. HEC-HMS is an
updated version of the popular HEC-1 hydrologic model. Olivera et al. (1998) describe HEC-
PrePro as a system of Arc View scripting programs and controls to extract hydrographic
information from spatial databases and prepare an input file to HEC-HMS. Using SCS curve
numbers and a DEM, HEC-PrePro delineates streams and basin boundaries, determines their
interconnectivity, and calculates parameters for each stream and basin (Olivera 1998). A benefit
to automating the calculation of hydrologic parameters that were traditionally estimated
manually is that results are reproducible, i.e., they are not dependent on the bias or experience of
the modeler.
2.2.4 GIS used to estimate non-point source pollutant loads
Using land use as a predictor of non-point source loads is a common use of GIS and hydrologic
models. Hauber and Joeres (1996) describe how a GIS was used to preprocess urban pollutant
loads for the Source Loading and Management Model (SLAMM). Similarly, Wright et al.
(1995) estimated nutrient loads from developed areas in the Onondaga Lake stormwater basin in
upstate NY with the GRASS GIS. These preprocessed loads were then routed from the
developed basins using the SWMM RUNOFF model.
Battin et al. (1998) describe the EPA's BASINS (Better Assessment Science Integrating Point
and Non-Point Sources) software, which integrates watershed point and non-point source load
data, the watershed hydrology program HSPF and the receiving water quality simulation
program QUAL2E. Olivera et al. (1996) describe the use of GIS to account for the spatial
variability of terrain in pollutant loading from a variety of land uses. The authors review the
strength of GIS in quantifying spatially distributing loads, and point out that this is a distinct
advantage over lumped models.
Scarborough and Yetter (1998) evaluated the Non-Point Source (NFS) module in BASINS 2.0
and found it to be a useful tool for evaluating NFS pollution. However, several problems were
found when evaluating a small watershed with the GIS data included with the program. The
most critical problem was that of coverage alignment (Scarborough and Yetter 1998).
Boundaries of land use and watershed boundaries did not match for the test case study, the St.
Jones watershed in Delaware.
2.3 Integration of GIS and Hydrologic Time Series
For the purposes of urban stormwater modeling, spatial data may usually be viewed as static.
Changes in geographic data are typically modeled in a scenario manner, e.g., a model run may be
-------
done for an undeveloped watershed, and then a developed scenario is performed using the same
hydrologic conditions. Hydrologic and meteorological data are commonly a time series of
discrete values. Therefore some attention must be paid to the integration of spatial and time
series data. This idea of consistency among data is key to the concept of hydroinformatics. Pryl
et al. (1998) describe the integration of time series with GIS to accomplish urban stormwater
master planning in the Czech Republic. Similarly, Rodriguez et al. (1998) use time series in their
analysis of the water budget based on parcel-level urban spatial data. Time series integration
was a key element in the work reported by Barbe (1996) in Louisiana. A large network of 150
monitoring locations fed a SCADA system with many time series data that were integrated with
GIS data and the SWMM model. An Oracle database was used to manage non-spatial data for
this project (Barbe 1993).
Da Costa et al. (1995, 1996) examined this problem in developing the Portuguese Water
Resources Information System. The integration of GIS with temporal data is described as one of
the great challenges of developing this system (da Costa et al. 1996). To accomplish this
integration, a database was developed using Oracle software to underlie the information system.
A special processing module was developed to interface time series data with the GIS. The GIS
portion used the ESRI Arc View software. Sorensen et al. (1996) describe the use of time series
in an application of MIKE GIS in Bangladesh. Sotic et al. (1998) describe the integration of
rainfall and flow time series with geographic data in a hydroinformatic manner in Yugoslavia.
Wolf-Schumann and Vaillant (1996) describe in detail the need for integrated time series with
georeferenced data. The development of TimeView, a time series management tool, is described
as adding a whole dimension (time) to spatial data. TimeView is integrated with Arc View, so
that a user can select a geographic feature in Arc View (e.g. a monitored manhole), and
TimeView returns a time series of measured data in graphical format.
2.4 Integration of GIS and Urban Stormwater Models
The linking of GIS and several hydrologic process models (beyond creating pre-processed data
files within the GIS) is examined by Charnock et al. (1996) and DeVantier and Feldman (1993).
Issues of differing scale properties and error propagation are addressed. The use of GIS as a
central hub of information, which is fed to several satellite process models, is favored over
coupling all the processes in one large program. Kopp (1996) addresses these same issues and
argues for more data standards to streamline hydroinformatics. Sponemann et al. (1996) explain
how a GIS can be shared among many varied users, e.g. gas utilities, water utilities, stormwater,
etc. thus maximizing the benefits derived from data collection and management. Greene and
Cruise (1995) developed an urban watershed modeling system using the SCS rainfall-runoff
methodology and GIS parcel attributes. Meyer et al. (19993) developed a raster based GIS for an
urban subdivision in Ft. Collins, Colorado and found that the results compared favorably with
non-GIS hydrologic studies of the same area.
Shamsi (1998) distinguishes three forms of information exchange between Arc View and
SWMM. The interchange and interface methods are described above, and involve the transfer of
information between Arc View and SWMM, which are run independently. Shamsi (1998)
defines the third method, integration, as the most advanced of the methods. SWMM is used as
the hydrologic and hydraulic simulator and is executed from within Arc View. This form of
integration includes performing all program tasks within Arc View: creating SWMM input data,
-------
editing data files, executing SWMM, and displaying output results (Shamsi 1998). Integration as
defined by Shamsi (1998) combines a SWMM Graphical User Interface (GUI) with a GIS to
provide a complete data environment. The advantages of a GUI were advanced by Shamsi
(1997), who provided a summary of software features and needs for SWMM interfaces.
Feinberg and Uhrick (1997) discuss integrating an infrastructure database in Broward County,
FL with a GIS and water distribution and wastewater models. The HydroWorks model is used to
simulate the wastewater collection system, with close integration with the database of
infrastructure characteristics and the GIS. Refsgaard et al. (1995) describe the evolution of
DHI's land process hydrologic model, SHE, and its extensive use of GIS. Ribeiro (1996)
describes the use of a raster-based GIS to interface with HSPF to analyze the effects of basin
urbanization. Hellweger (1996) developed an Arc View application using the Avenue scripting
language to perform the model calculations of USD A's hydrologic model TR-55.
Mark et al. (1997) use the MOUSE program from DHI to evaluate storm water in Dhaka, along
the banks of the Ganges and Bramaputra rivers in Bangladesh. Integration of GIS, time series,
and the hydraulic model were accomplished to better understand flooding characteristics.
Maximum inundation and duration of inundation were mapped using MOUSE and GIS. Shamsi
and Fletcher (1996) describe in detail the linkage of Arc View and SWMM for the City of
Huntington, WV. Arc View is shown to be a user-friendly environment to perform stormwater
modeling. Belial et al. (1996) studied partly urbanized basins using a linked GIS and hydrologic
model. The hydrologic model was based on a non-urban water budget, with modifications to
account for urbanization. The GIS was based on a DEM and raster-based land use data.
2.5 Management Evaluation Using Integrated GIS and Urban Stormwater Models
The integration of GIS, time series data, and an urban stormwater model is usually done to
evaluate management options. These options may be watershed-based, which would likely
include non-urban areas, or they may be local to the urban area.
Rodriguez et al. (1998) describe an integrated GIS and urban hydrologic model to evaluate small
storm hydrology for parcel level management decisions. Tskhai et al. (1995) use a GIS linked
with an optimization model to evaluate ecological and economic alternatives for the Upper Ob
River in the Altai region of Russia. While not strictly an urban runoff model in the traditional
sense, this project does link urban management decisions with an economic optimization model.
Makropoulos et al. (1998) focus on urban sustainability to evaluate stormwater systems.
Beginning with the idea that low energy solutions that control impacts at the source are more
sustainable, Makropoulos et al. (1998) demonstrate how a raster-based GIS (IDRISI) can be used
to integrate theoretical concepts and site specific spatial characteristics. The strength of GIS can
be used as a common ground between specialists and non-specialists to help them communicate
effectively. Belial et al. (1996) studied the effect of urbanization on a watershed using a linked
hydrologic model based on a DEM and a GIS. A water budget approach was used around each
raster unit to account for changes due to urbanization.
Mark et al. (1997) describe a detailed evaluation of flood management techniques in Dhaka,
Bangladesh, using MOUSE GIS. Xue et al. (1996) and Xue and Bechtel (1997) describe the
development of a model designed to evaluate the effectiveness of Best Management Practices
-------
(BMP's). This model, called the Best Management Practices Assessment Model (BMPAM),
was linked with Arc View to create an integrated management tool. This integrated model was
used to evaluate the pollutant load reduction potential of a hypothetical wet pond in Okeechobee,
Florida. Kim et al. (1998) used Arc View with an economic evaluation model and a hydraulic
simulator to evaluate storm sewer design alternatives. The hydraulic simulator was used to
generate initial design alternatives, which where in turn evaluated with an economic model. The
GIS was used to store spatial information, generate model input, and present alternative
solutions. The complete package of GIS, economic evaluation model, and hydraulic simulator
was termed a Planning Support System (Kim et al. 1998).
2.6 Trends in the Integration of GIS and Urban Stormwater Modeling
The trend towards a data-centric suite of evaluation tools is clear. The central idea behind the
European concept of hydroinformatics is that a consistent database is used for a variety of
purposes. The model is no longer the central unit driving the decision process. Neither,
however, has the GIS become the central data tool, due in large part to its inability to handle
temporal information effectively. Researchers who have paid equal attention to the model (the
processes), the GIS (the spatial data), and the temporal information (time series of hydrologic
processes) seem to have had considerable success. The integration of GIS and urban stormwater
models should therefore include integration with a database structure equipped to handle time
series. Several advanced applications have used a non-graphic database (e.g. dBase, Oracle,
Access) that is queried by both the GIS and the hydrologic/hydraulic models. While clearly an
evolving area, this approach seems to hold the most promise for the purpose of urban stormwater
decision support systems.
-------
3.0 Summary of Available GIS Urban Stormwater Modeling Software
As described in section 2, a useful taxonomy to define the different ways a GIS is used in urban
hydrologic and hydraulic modeling is presented by Shamsi (1998). The three methods defined
by Shamsi (1998) are data interchange, program interface, and program integration (Shamsi
1998). A fourth grouping was added for this report, the "intermediate program". Several
commercial modeling products feature a data management program to facilitate data transfer
between the GIS and a model. A short description is given below in order of increasing
sophistication.
Data Interchange: a batch process is used to transfer data to and from the model data set. For
example, the GIS may be used to calculate model input parameters e.g., catchment slope, or to
query an existing spatial coverage, such as land use. Then portions of the GIS query file can be
copied into a model-input file with no direct link between the GIS and the model. The model is
executed independently from the GIS, and portions of the output files may be copied back into
the GIS as a new spatial coverage for presentation purposes.
Intermediate Program: a data management program is used to transfer information between a
GIS and a model. This data management program is written specifically to import data from a
variety of common third party GIS software, and export to a model data set. Under certain
conditions this intermediate program could be defined as an interface, but generally it is not.
Program Interface: a direct link consisting of a pre- and post-processor is used to transfer
information between the GIS and the model. This process automates the data interchange
method. Model-specific menu options are added to the GIS. The model is executed
independently from the GIS, however the input file is created in the GIS. For example, in the
data interchange method, the user finds a portion of a file and copies it. An interface automates
this process, so that the pre- and post-processor finds the appropriate portion of the file
automatically.
Program Integration: while the interface method can't launch the model from the GIS, under
the integration method, the model and the GIS are together within one Graphical User Interface
(GUI). This represents the closest relationship between GIS and model, though "closest" does
not necessarily mean "best". It may be more efficient for a model to be independent from a GIS
in certain situations.
As noted elsewhere in this report, the development of a GIS for use in urban hydrologic and
hydraulic modeling is an expensive investment. Typically the most advanced tools are created
for advanced applications, where a full GIS is in place. For some applications, a DOS-based
model may still be the most appropriate. However, as more urban areas create GIS coverages,
the integration of modeling software and GIS software will become more useful and more
prevalent.
The Storm Water Management Model (SWMM) is the most widely used urban
hydrologic/hydraulic model in the US. In addition to SWMM, numerous other hydrologic
models were created in the US during the 70s including the US Army Corps of Engineers
Hydrologic Engineering Center "HEC" series of models (HEC-1 through 6). Two of the most
popular models, HEC-1 and HEC-2, have been updated and renamed HEC-HMS and HEC-RAS,
-------
respectively. These two models have been updated from the original DOS model with a MS
Windows based GUI. HSPF, and ILLUDAS are other models developed in the 70's, which are
still used today.
The original SWMM model, available at no charge from the US EPA (at the following website:
http://ftp.epa.gov/epa_ceam/wwwhtml/ceamhome.htm) was written in Fortran-77 for mainframe
computers (Huber and Dickinson 1988). The model was originally written during the 70s, with
several major improvements made in the early 80s. It has continued to evolve since being ported
to personal computers. Version 4.31 is the latest release; however numerous other modifications
exist to the program (e.g. UD-SWMM, a modification of SWMM by the Urban Stormwater and
Flood Control District of Denver, Colorado). SWMM runs in MS-DOS in a text-based
environment, which is not the user-friendly windows and graphical user interface (GUI) based
environment that is expected today. Despite these shortcomings, it has an active user community
within the United States.
Lack of funding support for SWMM during the 80s and 90s meant that the model had to be self-
sustaining. Interested parties such as local governments, consultants, and third party developers
added their own refinements to the model, with very little support from the federal government.
Because these refinements added value to the original program code, the developers started to
charge for these improvements. XP-SWMM (XP-Software 1998) and PCSWMM (CHI 1998)
are examples of this type of refinement. The SWMM user's listserver has developed into a self-
sustaining community of users. Information on accessing the listserver can be found at
http://www.chi.on.ca/swmmusers.html
During the 1980's, several models started to evolve in Europe. Two of them are HydroWorks,
from Wallingford Software in Great Britain, and MOUSE from the Danish Hydraulic Institute,
DHI, in Denmark. Unlike EPA SWMM, these models are proprietary.
These models are listed in table 3.1, with the addition of MikeSWMM, which is the result of a
recent collaborative effort between DHI and Camp, Dresser, and McKee (CDM). This product
uses the latest SWMM model engine available from the US EPA, and adds the MIKE GUI and
MOUSE GIS from DHI.
Table 3.1: Summary of available urban stormwater modeling software with GIS linkages
Product
HydroWorks/
Info Works
Mouse GIS
MikeSWMM
PCSWMM/GIS
XPSWMM
Model
Hyd reworks
Mouse
SWMM
SWMM
SWMM
Interface
Hyd reworks
Mike
Mike
PCSWMM
XPSWMM
Company/Source
HR Wallingford/
Montgomery Watson
Danish Hydraulic Institute/
Danish Hydraulic Institute/
Camp Dresser and McKee
Computational Hydraulics
International
CAiCHE
Website
www.wallingford-software.co.uk
www.dhi.dk
www.mikeswmm. com
www.chi.on.ca
www.xpsoftware.com
The following sections describe commercial and public domain products that are currently
available for urban hydrologic and hydraulic modeling. The above taxonomy is used to define
how each one handles information transfer between a GIS and the model. However, the reader is
10
-------
cautioned that while integration may be the most advanced method of using a GIS and model
together, it is not necessarily the best method for every application. For some applications
(especially when the GIS is incomplete, inaccurate, or both) different levels of manual operation
may be more appropriate. For example, a limited GIS may exist for an urban watershed, along
with very detailed and accurate CAD files. Certain commercial products (e.g. Visual Hydro by
CAiCE) can handle CAD drawings better than a product designed to run a pre-existing GIS. If
resources were not available to create a GIS, it would be appropriate to use a product suited to
the available data.
3.1 SWMM and EPA Windows SWMM
As stated previously, SWMM is a DOS based program developed under US EPA funding during
the late 1970's and early 1980's. There is no provision to link directly or indirectly with a GIS
other than through standard input text files. This is the most basic version of SWMM available.
This version of SWMM is important because it is in the public domain, and the source code is
readily available. The latest version of the DOS based SWMM can be found at
http ://www. ccee.orst.edu/swmm/
In 1994, the US EPA produced a Windows-based GUI for SWMM. This program (also
available at http://www.epa.gov/ost/SWMM_WINDOWS/) runs on Windows version 3.1, and is
therefore somewhat outdated. This program is also limited by the fact that the DOS based
SWMM engine is in a constant state of improvement by developers and users because the
Fortran source code is available. Unfortunately, the Windows SWMM program used the
SWMM engine available circa 1994, and the newer versions of the SWMM engine cannot easily
be substituted. Therefore the program has quickly become outdated, and has few users.
Windows SWMM could not be linked directly to a GIS program.
To use either of these programs with a GIS, the data-interchange method must be deployed to
transfer information from a GIS to an input file. The GIS may be used to store and estimate
model input parameters. The GIS could be queried for the needed values, and the values could
then be transferred to the input file. The level of automation to perform this task depends on the
user. It could be as simple as copying the needed values onto a Windows clipboard and pasting
them into the input file, or developing special queries from the GIS to create an input file
automatically.
3.2 PCSWMM '98 and PCSWMM GIS
PCSWMM-98 is a set of 32 bit applications designed to facilitate running SWMM. These tools
include an ASCII text editor, an animated hydraulic grade line plot, a chart wizard, an Internet
wizard, a batch file control, a rainfall analysis package, a bibliographic database, a sensitivity
analysis wizard, and a calibration wizard. The GUI allows files from many sources to be linked,
including those accessed across Intranets and Internets. PC-SWMM GIS is an optional tool that
works directly with CAD or GIS files in constructing a link-node database for running the model
from the existing data sources. After importing the data from a CAD or GIS file, an aggregation
tool allows semiautomatic construction of a simplified link-node model. This reduces model
complexity, and provides a direct analog to the aggregated catchment concept in the original
SWMM. An example of output from a PC-SWMM example run is found in figure 3.1.
11
-------
Fib Hdp
fls Jd*
ss
3
iiJ y^ iJ M5PH
Figure 3.1: PCSWMM output
(CHI, 1999)
PCSWMM GIS is an intermediate data management program designed to accept data from a GIS
package and transfer it to a SWMM input file. Because it is a more sophisticated method of
transferring information from a GIS to a model than the data-interchange method, but it is not an
interface as defined by Shamsi (1998), a fourth category was added to the taxonomy, that of the
intermediate program. PCSWMM GIS and PCSWMM'98 were developed by CHI in Guelph,
Ontario. According to the CHI website, (www.chi.on.ca). PCSWMMGIS does not perform any
parameter estimation calculations. It accepts geographic data from an external GIS, within
which the parameter estimation calculations and queries are performed. However, it does
perform tasks specific to SWMM modeling, such as performing geographic and hydrologic
aggregation calculations that are commonly done to simplify a SWMM model.
3.3 XP-SWMM by XP Software (Also Available as Visual Hydro by CAiCE)
XP-SWMM32 by XP Software (also included in Visual Hydro, by CAiCE) is a full 32-bit MS
Windows application. The program has been enhanced by the addition of a graphics database,
and an adaptive dynamic wave solution algorithm that is more stable than the matrix method
used in the original SWMM. The program is divided into a stormwater layer, which includes
hydrology and water quality, a wastewater layer, which includes storage treatment and water
12
-------
quality routing for BMP analysis, and a hydro-dynamic/hydraulics layer for simulation of open
or closed conduits. The user-friendly GUI is based upon a graphical representation of the
modeled system using a link-node architecture. An example of input and output processing in
Visual Hydro is found in figure 3.2. Because the links and nodes are set up on a coordinate
system basis, files can be translated between most CAD and GIS software systems. CAD or GIS
files can also be used as a backdrop for the system being modeled. However, since there is no
interface with a GIS, data interchange method must be used to transfer parameters (e.g., slope,
width, percent imperviousness, etc.) from a GIS to the model. However, the program can import
and export files from and to a GIS.
3.4 SWMM-DUET
SWMM-DUET is the only fully integrated application of a model into a GIS. It was developed
using Arclnfo and the native Arclnfo development language AML (Shamsi 1998). SWMM
DUET uses relational databases, both pre- and post-processors, and expert system logic to
integrate the SWMM environment and the graphical paradigm of Arclnfo (Shamsi 1998). Future
plans include an Arc View version of this product (Shamsi 1998).
3.5 DHI Software
3.5.1 MIKE SWMM
MikeSWMM is a proprietary GUI for SWMM from the Danish Hydraulic Institute and Camp,
Dresser and McKee, Inc. Mike SWMM can be integrated with a GIS system using Mouse GIS,
also available from DHI. Mike SWMM is a classified as an Arc View interface due to its ability
to link with the Mouse GIS program, which is described in the follow section.
3.5.2 MOUSE and MOUSE GIS
Mouse GIS is a module for MikeSWMM and Mouse users that also allows tight integration
between the GIS and the model database. Mouse GIS is an Arc View GIS application. Files do
not need to be translated and converted from the GIS to the model format. The DHI product for
stormwater modeling, Mouse, uses the Mike GUI within the MS Windows environment. Mouse
is a dynamic 32-bit model running in MS Windows that is capable of modeling any type or
combination of open or closed conduits and pressurized or gravity flows. An example of the
result of a simple query that illustrates the operating environment of Mouse GIS can be seen in
figures 3.4 and 3.5. Each object within Mouse GIS has database attributes that can be queried.
Mouse GIS is an interface between Arc View and the hydraulic pipe simulator, MOUSE. Mouse
is a sophisticated proprietary hydraulic model that is commonly compared to SWMM.
13
-------
Node - NODH2
•—• 655-15
Viewing Style
Font Size
.Numetic Piecision
Plotting Method
Data Shadows
Grid Lines
G(id In Front
Include Data Labels
Mark Data Points
Show Annotations
Maximize
Customization Dialog
Export Dialog
Help
or Help, press F1
I File Export Window Help
Node Data Table
Node Name
Ground Elevation
Invert Elevation
Maximum
X Coordina Y Coordinat
1960632.927
1960575.611
1960490.001
1960401.579
1960331.983
1960270.453
1534420.985
-1534496.662
-1534527.750
1534570.236
-1534637.243
-1534720.629
Freeboard
JS CAiCE - [CAICE -VISUAL HYDRO (C:\CAICE\SAMPLES\MYTOOLS\MYTDOLS1 (MYNET2) : MYNET2.XP1
X=1961073.372, Y=1535194.512 Hdr 1:10000
For Help, press F1
Figure 3.2: Visual Hydro
(CAiCHE, 1998)
14
-------
ioMfi InmnH E»rt»«
Figure 3.3: Mouse GIS user action
(www. dhi. dk/mouse/)
Figure 3.4: System response to user action, Mouse GIS
(www. dhi. dk/mouse/).
15
-------
3.6 Wallingford Software-HydroWorks and InfoWorks
HydroWorks and InfoWorks are companion products produced by Wallingford, Inc. of the UK.
Wallingford has taken a different approach to managing geospatial data. InfoWorks is designed
to import relational and geospatial data from third party software (e.g. Access and Arc View).
Once transferred to InfoWorks, the data is then used to create and run a HydroWorks model.
Hydroworks is an urban stormwater modeling system with a user friendly GUI. HydroWorks
uses a fully dynamic solution technique that solves backwater and unsteady open or closed
conduit situations.
InfoWorks performs GIS-type operations, and is designed to operate with HydroWorks, the
hydrologic and hydraulic simulator produced by Wallingford, Inc. While the relationship
between InfoWorks and HydroWorks may be defined as an interface or even fully integrated,
InfoWorks is not a GIS interface. An example of InfoWorks is shown in figure 3.5. Data from a
general use GIS product like Arc View would need to be imported into InfoWorks, much like the
PCSWMM GIS program from CHI.
Figure 3.5: InfoWorks from Wallingford Software
(HR-Wallingford, 1999)
16
-------
3.7 Summary
A summary of model and GIS features is presented in table 3.2. As described above, and
summarized in table 3.2, the problem of transferring geographic and hydrographic data between
a GIS and a simulation model has been handled several different ways by various software
developers. It may appear self evident that a tight integration between the hydraulic model and
the GIS is desirable. However, the question should be raised; how integrated should these two
types of software be? For example, should a GIS include a hydraulic model as part of a toolbox
within the GIS? This may, or may not, be desirable. Therefore it should not be assumed that
because SWMM DUET has integrated SWMM within Arclnfo that it is the best modeling tool.
For example, the expert GUI of XP SWMM may be more useful for a given application, despite
the fact that it does not interface directly with a GIS, nor does it have an intermediate data
management program. What is common among the recent software developments is a
transferability of fundamental database information. This theme is formerly known as a
Decision Support System (DSS). Under a DSS framework, neither the GIS nor the model are
"central" to the process. Both GIS and model serve satellite functions to a central master
database. A more fundamental look at this question is given in chapter 5.
The question "which model works best with GIS?" is impossible to answer. Depending on the
problem at hand, several products are designed to work with an existing GIS. The answer
largely depends on the state of information available. If an existing Arclnfo database is in place,
SWMMDUET would work well. Other products have used an information management
approach over GIS integration. This may be best suited for applications with disparate data
sources. Differences amongst hydraulic models may be more important. The DHI suite of
models may be appealing for certain applications. The organization of the Hydrolnfo/
HydroWorks or PC SWMM'98/PC SWMM GIS software may be best suited for other
applications. Each has unique and valuable features, and no recommendation is made in this
report for a specific software package.
The future evolution of both GIS and urban stormwater modeling, and their possible
convergence, appears to be centering upon object intelligence and smaller, programmable
component tools. For example, ESRI's stated goal of its next generation of programs (possibly
Arc View 4.0) is to rewrite and enhance its programs to use standard MS Windows routines that
can be called via dynamic link libraries (DLLs). An early example is the product called
MapObjects, which allows a programmer to insert a GIS-like application within a Visual Basic
or Visual C++ program, and make queries and Arc View-like functions upon GIS databases
without the Arc View program itself. Existing tools like Evolver, for nonlinear optimization, and
@Risk for Monte Carlo simulation are also available as DLLs (Palisade Corporation, 1998).
Urban stormwater modeling tools appear to be evolving into using similar tools as they take
advantage of existing libraries such as spreadsheet and graphic add-ons, (e.g., Visual Hydro,
PCSWMM), and are rewritten in object-oriented programs such as Visual C++, Visual Basic, or
Java. The future convergence of GIS and urban stormwater modeling will probably utilize these
common sets of tools to take advantage of the easier interoperability. Such tools make
integration of these disparate components possible into an integrated Decision Support System,
the subject of the next chapter.
17
-------
Table 3.2: Characteristics of urban storm stormwater models
Software
SWMM Products:
EPASWMM
Windows SWMM
PCSWMM'98/
PCSWMM CIS
Visual Hydro/XP-SWMM
SWMMDUET
MIKE SWMM/
DHI Products
MOUSE, Mike-11
MOUSE CIS
HydroWorks/
InfoWorks
Data
Interchange
X
X
X
Intermediate
Program
X
X
CIS/Model
Interface
X
X
CIS/Model
Integration
X
Advantages/Disadvantages
DOS based
Based on SWMM circa 1994
PCSWMM GIS is a data management
program
Imports CAD, GIS files
Arclnfo based
Arc View based (via MOUSE GIS)
Arc View
InfoWorks is a data management program
for geographic and relational databases.
18
-------
4.0 Future Urban Stormwater Modeling in a DSS Environment
Much of the data used in distributed (and lumped-distributed) hydrologic modeling requires
some level of spatially referenced information. Conversely, purely lumped hydrologic models
by definition do not require data to be spatially referenced. This report is focused on lumped-
distributed models and the type of information required to use them. Lumped-distributed models
are typically defined by sub-catchments within a Stormwater basin. The hydrologic parameters
are lumped within each sub-catchment. On the basin scale, however, the discretization among
sub-catchments provides spatial distribution. Some of the data used in these distributed models
may be more efficiently stored in forms other than GIS spatial database structures (Reitsma et al.
1996). For example, relational data models may be more efficient in storing certain attribute
information. Time series are another form of data commonly used in hydrologic modeling.
These data are frequently stored in a relational form, despite some shortcomings of this structure
for time series (Reitsma et al. 1996).
Besides model input, decision-makers frequently require analysis of model output, and the
analysis may not necessarily be spatially referenced. For these reasons, future model
development should not only focus on the role of GIS in modeling, but on how all information is
stored and used.
Due to the complexity of tools required to fully support a complex hydrologic decision, a system
made up of more than a GIS and simulation model is needed. An integrated suite of tools is
required to manage information. These tools are referred to as Decision Support Systems (DSS).
Although the model is important, much of the focus has shifted to the related needs of relational
database management, developing geographical information systems, and a sophisticated user
friendly interface, all combined in DSS. Figure 4.1 describes these necessary components of a
DSS (Reitsma et al., 1996). The evolution of DSS may be seen as a natural extension of
simulation models (e.g. SWMM, MOUSE, HydroWorks), GIS (e.g. Arc View, IDRISI, Arclnfo),
relational databases (e.g. Dbase, Oracle, Access) and evaluation tools (e.g. optimization
software). Reitsma (1996) define a DSS for water resources applications:
"Decision support systems are computer-based systems which integrate state
information, dynamic or process information, and plan evaluation tools into a
single software implementation."
In this definition, state information refers to data which represents the system's state at any point
in time, process information represents the first principles governing resource behavior, and
evaluation tools refer to software used for transforming raw data into information useful for
decision making. A simple representation of DSS components is shown in figure 4.1.
The GIS and the simulation model are only components of the DSS in figure 4.1. Future model
development should focus not only on GIS interfaces and integration with models, but should
include integration with a more complete management information system.. The view for future
model development should be broader than only GIS integration, because hydrologic decision
making requires more than just spatial information. In a DSS, the GIS only handles spatial data.
Spatially referenced information is only one form of state data that is relevant to hydrologic and
19
-------
hydraulic modeling. Time series and attribute data are also crucial to the analysis, and may be
handled poorly in a GIS database format designed to manage spatially referenced data.
A thorough background on DSSs and their application to reservoir decisions can be found in
Jamieson and Fedra (1996a), Fedra and Jamieson (1996), and Jamieson and Fedra (1996b).
These series of articles describe the conceptual design, planning capability, and example
application of the Water Ware DSS, a complex river basin DSS that combines a "GIS, a geo-
referenced database, groundwater flow, surface water flow, hydrologic processes, demand
forecasting, and water-resources planning" (Jamieson and Fedra 1996a). Reservoir operation
and management was one of the first areas of civil engineering in which DSSs were applied.
Because of the complicated decision criteria governing urban stormwater management, Davis et
al. (1991) studied a prototype DSS developed to analyze the impact of different catchment
policies. Driscoll (1993) developed a DSS to assist highway engineers in determining which
construction sites would contribute to a receiving water quality problem. Azzout et al. (1995)
discuss a DSS under development that would assist in determining the feasibility of alternative
techniques in urban stormwater management.
DSS
Evaluation Tools
-Multi Criteria Evaluation
-Visualization
-Status Checking
I
State Information
-Databases
-Geographic Information
Process Information
-(Simulation) Models
Figure 4.1: DSS structure and components
(Reitsma et al. 1996)
20
-------
The theme of the following sections is that the parts of a DSS are separate but complementary.
They should be able to transfer information to needed process models and evaluation tools
without complications. There is no need to house everything under one umbrella, i.e. to perform
all modeling tasks in an integrated GIS/hydraulic model.
4.1 State Information
In one form of DSS, state information drives the system. This is a "data-centric" view, and it
differs from the more traditional model-based analysis commonly used in urban water resources
modeling. This fundamental change in perspective may be more important to the future of
stormwater modeling than efficient program interfacing. The modeler will need to have tools
that handle spatial and temporal data for purposes of modeling, rather than spending resources
manually transforming data into the format needed for the model. While this is the idea behind
much of the discussion in section 4, a fully integrated GIS/model like SWMMDUET may not be
the best modeling tool for the future. It may be that an intermediate database manager (e.g.
Hydrolnfo, PCSWMM GIS, etc) may be closer to a DSS than full GIS integration.
State information is stored in relational databases or spatial databases in a modern DSS. Instead
of integrating all data forms into one database model, the relational and the spatial information
are kept separate, and are linked together to form a geo-relational database structure.
4.1.1 GIS
The focus of this report has been on spatial data for modeling purposes. GIS is a critical part of
the DSS for systems that are spatially distributed. Since some spatial discretization is needed to
model urban hydrologic systems, much effort has been placed on smoothly transferring spatial
data to the model and vice versa. Under the DSS data-centered framework, the GIS is one part
of the central database of state information. Due to the popularity of GIS software, there has
been some interest in housing the entire DSS within the GIS framework. For example, Walsh
(1993) investigate spatial DSS, a GIS driven DSS. Reitsma et al. (1996) describe some of the
problems associated with a GIS-based DSS:
"Recent developments in modeling in GIS (NCGIA 1991; 1993) suggest that GIS
can be extended even further into other domains of modeling, e.g., water
resources. This type of architecture does offer certain advantages in that it makes
use of sophisticated software for management and evaluation of spatial data. A
distinct problem, however, is that although rapid improvements are being made in
the integration of GIS and modeling (NCGIA 1991; 1993), the full integration of
all three components of DSS in GIS is, to say the least, problematic."
To facilitate a non-GIS-based DSS framework, i.e. GIS as a component but not central to the
DSS, there are several considerations for GIS. First, the spatial database in the GIS must
communicate with other DSS components. This means that much of the interfacing/integration
of models and GIS discussed by Shamsi (1998) and reviewed in Chapter 3 must be extended to
include other DSS components. Second, spatial tools should be available for modification by the
modeler. The GUI should include a dynamic toolbox. For example, if the GIS performs an
21
-------
aggregation calculation in one way, the modeler may wish to modify the algorithm without
having to re-write a lot of computer code.
The spatial analysis of topographic and hydrographic data may be efficiently carried out in a
GIS. GIS software, e.g. Arc View, contain tools that take basic geographic input parameters, e.g.
a DEM, and create stormwater boundaries, do slope analysis, etc. Land use and soil coverages
are commonly used to estimate hydrologic parameters. Shamsi (1998) discusses several ways
that SWMM input parameters may be estimated using GIS. Subarea characteristics such as area,
width of overland flow, percent imperviousness and slope may be estimated for the RUNOFF
block of SWMM. Parameters used for water quality simulation with the TRANSPORT block of
SWMM such as curb length may be estimated from road characteristics in a GIS. Similarly, land
use data may be used from a GIS to create SWMM TRANSPORT input files for water quality
simulation.
Hellweger and Maidment (1999) discuss the details of the spatial analysis required to create an
input file for the HEC-HMS model. While not specifically an urban model, it may be useful to
review the procedures used. A method to define sub-basin boundaries and stream network
connectivity was developed using GIS data layers derived from digital terrain data. Intersecting
the sub-basin and stream network layers results in a node-arc representation of the watershed.
This information is used to develop an input file for the HEC-HMS model. In this example, an
underlying assumption was that streams flowed perpendicular to topographic contour lines.
While many of the tools and methods described by Hellweger and Maidment (1999) are useful
for modeling natural hydrologic systems, the effect of managed systems in urban areas
significantly complicates the analysis. For example, gravity sewers and engineered open
channels may have slopes that are independent from the ground surface slope, possibly crossing
natural stormwater boundaries and otherwise defying a general physics-based analysis that is
used when describing natural systems. Managed or altered hydrologic systems may also be
operated based on logic other than the processes that drive a natural system. For example, flow
may be diverted from a stream only during dry weather for irrigation purposes, thereby
exaggerating the apparent peaking ratio of a stream gauging station.
The problems associated with a "pure" GIS analysis of an urban, managed system highlight the
advantages of integrating GIS, simulation tools, and relational databases into a DSS. The DSS
framework addresses many of the problems associated with using a GIS for urban analysis
because of the ability to access and manage related, auxiliary information.
4.1.2 Time series
The analysis of time series data is equally important to modeling as the analysis of spatial data.
Temporal data includes flow and rain time series, water quality data, etc., as well as dynamic
model output. The DSS could include a time series toolbox. Statistical tests and statistical
models could reside in this portion of the DSS, for comparison with process models and for
analyzing model output. An example of some of this type of pre-processing is that which is
currently done in outside statistical packages, or even using Microsoft Excel.
Continuous simulation modeling usually will require large amounts of time series data for input
22
-------
purposes. Urban stormwater models that have the capability of continuous simulation usually
are capable of reading several different formats of rainfall data. For example, SWMM reads the
following formats (Gregory and James, 1996):
1. National Weather Service Hourly Rainfall Data (in two formats).
2. Pre-1980 National Weather Service Hourly Rainfall Data
3. User Defined Hourly Rainfall Data
4. Canadian Atmospheric Environment Service Hourly Rainfall Data
In SWMM, the standard modules RUNOFF, TRANSPORT, EXTRAN, and STORAGE can
import the above formats of time series data. In addition, the modules RAIN, TEMP, STATS,
and COMBINE can be used to preprocess time series data. HSP-F, the Hydrologic Simulation
Program FORTRAN, includes several time series facilities (Gregory and James, 1996). Several
single purpose time series data management programs are available. The HEC-DSS, or the
Hydrologic Engineering Center Data Storage System (not Decision Support System), was
developed to link time series data with the various HEC watershed management programs.
ANNIE, developed by the US Geological Survey, uses watershed data management (WDM)
files, and can import WATSTORE files (Gregory and James, 1996). Both ANNIE and HEC-
DSS are non-proprietary FORTRAN models. Due to the multitude of file formats it is difficult
to import and export datasets between different modeling environments. For this reason, the
CASCADE2 time series management program was developed (Wang and James, 1997). This
program, written in Visual Basic, runs under MS Windows and bridges the gap between SWMM
and HEC-DSS formats.
To be used within a relational database, the time value must be stored, which creates a
redundancy of information. This is because a time series is defined by the start time, the time
interval, and either the length of the interval or the end time (Reitsma et al. 1996). Another
disadvantage of the relational approach is that the DSS must store the criteria for searching the
time series (Reitsma et al. 1996). The importance of this redundancy becomes more evident in
the case of real time control, which utilizes signal processing and control theory. Lavallee et al.
(1996) describe a real time control system developed for the Quebec urban area to manage a
stormwater system to minimize CSOs. The unique data needs and system architecture of the
RTC system support many of the concepts of DSS due to the demand for timely decisions and
vast amounts of data available..
4.1.3 Relational database
An example of a relational database query and its results is presented in this subsection. This
example is presented within the context of a relational database contained within a GIS. The
same queries can be made in a non-graphic relational database. The linked tabular structure of a
relational database allows for extremely complex and powerful queries to be constructed, thus
relevant information is made available to the user. The City of Aurora, Colorado has developed
a very good base system for GIS. A subcatchment was chosen from the Shop Creek watershed
of Aurora, Colorado, a pilot area for GIS development for the City of Aurora.
The available themes from this area are as follows:
1. Water lines
23
-------
2. Digital elevation models
3. Rain gages
4. Stream gages
5. Parcels
6. Sewer lines
7. Sewer manholes
8. Digital orthophotos
9. Streets centerlines
10. Sewer tap locations
11. Water meter locations
12. Impervious areas (created by tracing the digital orthophotos)
Many tables are associated with each of these themes. An important feature of Arc View is the
use of the relational database structure. Tables are linked to graphical features, or themes
(analogous to layers in AutoCAD) through the use of spatial geocoding. The user links or joins
the tables by choosing a common column, or field between them. The three main types of
relationships among tables are:
1. One to One
2. One to Many or Many to One
3. Many to Many
All of the records in the one to one table could be placed in the same table. However, good
database practice suggests organizing the tables around their functions, instead of the other way
around. For example, many attributes are associated with your name, but only your address and
phone number are listed in a telephone directory. The first two of these types of relationships is
shown in figure 4.2. The two tables nearest the bottom, "Attributes of Themel.shp" and
"Attributes of Parcel" are joined by a one to one relationship, with the fields "Parcel-ID" being
the common column. This is again the relationship between "Attributes of Parcel" and
"Attributes of Address", using the fields "Parcel-Id" and "Address-Id" as the common columns
(it is not necessary that they have the same name). Lastly, a one to many relationship is shown
by the indexing of "Attributes of Address" and "all_9295.dbf' with the fields "Gistag" and
"Gisno". The function of this linking is essentially the following. The themel.shp table contains
the parcels that are located within the small subcatchment. The Attributes of Parcel table
contains data on all parcels. Attributes of Address contain address information, including the
GIS tag number needed within the Water Use database. This database lists monthly water use
data within entire Shop Creek basin, so many records are associated with each parcel.
The query shown in figure 4.2 illustrates the power of this tool. The query asks for all linked
records in which the water use in a month is over 10,000 gallons. The results of the query are
highlighted within the tables. These queries can be moved to the top of their respective tables for
further visual analysis. Alternatively, by clicking on the view with the current theme set to
Themel.shp, the visual results of the query can be seen by highlighting parcels that used at least
10,000 gallons a month as shown in figure 4.3.
24
-------
- Inlxi
Eile Edit lable Field Window
i ii 1 i ii ii 1 i Fields Values
[HUB yyOSJOS] [D l[Pre date] U] | = | | <> | | and | 6
13 of 63selecte IMniDiiii. f— 1 fTT] r^-| -'
1 FRslAJer IJBfil ! 1 II II 1 F
^J-
566070650
5i'6'07065i:i
566070650
< |
^
[Storm usej ! 1 < 1 1 <= 1 1 not 1 £
• :s::::::j . ^M P
^\3ffff vvV? | [Month! i 1 1 v * \ I 1
SA | I 3 p
SA | [Water use]>=10
SA !
1-- A
dat&exsff | | ^:
2243 j j
2244 T
2245J
2246!
2247!
2248!
2248!
2250!
2251 !
2252!
I 69013J 566070655! 14530 ]E ! TUFTS
68126'T 566070366T14T72 !I fRADCLW
68132! 566(171 H/2ii 41 74 !E i RADCUFF
nni 1 4 ; nnnri7G378 ! i i'i 98 ! E j RADCUFF
63108! bGbU/LQU'l ! I42IIJ IE ! RADCUFF
68642 ! 566070742 ! 4527 ! S ! FAIR PLAY
685241 56U07COQJ ! 4453 IS I EAGLE
68521 i 566070897 ! 4473 ! S ! EAGLE
nnR4.:: : nnnn-o743 ! 4517 ! s ! FAIRPLAY
68119! Se'GOTOMl ! 1 41 52 !E ! RADCliFF
*3l
..TAsaw .-4™ ftsanefer
Polygon i 608.84226! 88.87247! 2
Polygon ! 33824.443791 5771. 08960 ! 3
Polygon !
Polygon i
1 93538.85220 ! 1 061 3.42282 ! 4 !
53017.50035! 1639.50133! 5!
Polygon ; iM/bS.UBUSb i b^a.aiJJb: b:
i
S/lJFff | XL™ /fe«MHfer | /^wraJS" /
Polygon ! 21357.77638! 602.17219 2149
Polygon ! 14204. 22725! 504.1 0308 2176!
"Polygon""! TraizSOiOl"! 591141339 2188!
Polygon ! 14240. 529' 95 ! 521. 531 57 2215
Pnlnnnn i inRQ9 ifl441 ! 4^4 44791 77?? i
3& fiMni xl I
i^Slarl | |^*^ArcView GIS Version ... B2?" Microsoft Word - epal
Ld
I |
LJ
^^^Hd
< Update Values
•* ! New Set i|
Add To Set
~^ Select From Set
- lnl x • 1
niil
|D| xl
afe | &fefsr_use \ Ssfnsr_uss Sfoa}}_use
i ZJ 5, S-±4
1 .1! 5.i S_J
! 9| 5 C ,
1 ^" jj ' —
«|l
^11
^[1
31
«yl
:'-ln|x|
i±ttrfj»- | Xjott^M1 2^3 | fbf
! AVE ! 8001 5 i 2073-07-1 -1 1 -01 5
|"C"IR ; l'aob'i'5 T"2b"7¥b'7-2"23'^i'il
! CIR I ! 80015 ! 2073-07-2-23-1 27
I 5R | 8001 5 ! 2073 -07-2-23-1 28
F'CIR : rsbbi's !"2b73"-b7-2'-2'3"i38"
! C"f ^ ........ .......| .g^ g ^ 2673-07-1 -06-669
! CIR : 1 Bom 5 i 2673-07-1 -bs'-ooe
! CIR ! ! 8001 5 ! 2673-07-1 -05-012
i CT i 80015 ! 2073-07-i -06-ODB
! CIR ! ! 80015 ! 2"673'-b7-2-23'-i i i
1 - |n| x| 80015 i..2.?Z3:P?.:i:1.1:£ii
^oii^t?' |^ /ay \ SyH].5 L.<:.y/i?.:y.(!:.f:.^H!rli/.
58646 i 207 3-04-2-22-031
58667 1 207 3-05-1 -1 0-005 ff,
68298 ! UN 'MOWN
6831 4 ! 207 3-08-1 -27-002
1 New Open Add
68315 : 20 f 3-08-1-26-002 _,. , = , —j.—-,-.-,
' *" ' I ] '^l/' ~~ Attributes of Address
-=-U=U-XJ wicw- Attributes of Parcel
~\3fi~s/-K/ Jg^ 1 71317 Af& s**^ Attributes of S anitary
69601 ! 2073-07-1 -1 1 -01 5 - ^^ Attributes of Sanitary_MH
6"9'6'9'4"l'2073-07:vi'i'-'6'i"4' MF?** Attnbutes of ^emel'*p
gg7Q4't 2073"o7 "fi'l "6'i "i * — *S3^* Attributes of T hemeS. shp
6'9's'5'5"!"2"67'3-67'-i-l'i'-6'io * m> Attributes of Theme6.Shp
£qSmt?n7^n7:i:iT:nTfi T - _. , fiow" d™ ...
~^~~| Charts sanit mh.dbr
=LJ t£j^\ sanitary, dbf
1 t5^ J 1 1 .
i
!
!
:
-i
i
!
i
...|, —
i
I
!
"f
...!.
! T
~n
•
^m
o"
—i
10
_1_1 S*
12:36PM |
Figure 4.2: Relational database query example in Arc View using water use data
25
-------
Eile Edit View Xneme Graphics Window Help
jsp r*n f^flf^nfj^n rs&nnnrj^?
[ma
Them eS .s hp
m
Pilotare a.shp
rzi
0 rigin: (2,191,703.79, 655,996. B4) E xtent: (1,274.52,1,162.19) Area: 1,481,241.36 sq
art < ArcView GIS Version ...
Figure 4.3: Spatial results for example query from figure 4.2.
-------
The results of the query can also be output to an Excel spreadsheet by using Arc View's Avenue
script language and Microsoft's Dynamic Data Exchange (DDE). This capability is incorporated
as a toolbar shown as an "X" in figure 4.3. The results of this query, output to MS Excel, can be
found in figure 4.4.
] t* E*
|.-3l --l-fa-l li-l
z I
* •» 11
» 14 H -
Psfcn Parcl id WSIDI USE
779 2H9 69601 ~10
ise.* Z2Z2 effloi H
I9JB3 2322 7D1i6 10
212 23*2 7Q2& 26
7 33 MB (TC13B 16
.237 2J22 7Q3SS 11
912 2«5 7W3? U
723 25U 70627 ili
.1« 3887 70720 10
SS 3a* KH3? 11
1 99 jsrr rio^ is
211 25Ti 71031 12
SB 2STS 71060 20
Ddi
19933123
1S20IZT
19920122
19320122
19320122
193301??
1'3320122
19520122
19920122
19333122
1933] in-
19933122
199301J?
Sups
Shipa
3'"ilp8
9^spc
Shjpo
SBRO
S^l^pA
3lSp«
ampe
Shapg
'Jiipa
SJ-ripd
3ap8
Stnpg
"
Figure 4.4: Query results output to Excel using Avenue script tool and Microsoft DDE
An example of a relational database within a DSS can be found in Reitsma et al. (1996). In a
review of the TERRA DSS system, the authors explain that the data were divided into seven
main groupings:
1. Time Series Data
2. Historical Data
3. Physical Attribute Data
4. Operational Constraints
5. Model Data
6. Security Data
7. MetaData
Meta data, the last group, is data about data; and allows the Data Management Interface (DMI), a
program component of the DSS, to refer only to the meta data, which keeps track of the data
structure and where and how the data is stored. This allows the DSS program to be relatively
27
-------
free of data constraints (Reitsma et al., 1996). Although the relational database model has some
shortcomings, particularly for time series, it remains the database structure of choice for DSS, as
it is the prevailing database model at present.
4.2 Process Information-Simulation Tools
In the DSS framework, the process information is contained in simulation models. Process
models simulate transitions of the state of the system, as described by the geo-relational
database. The simulation model must therefore communicate in some fashion with the rest of the
system. For stormwater management models, this may occur in much the same way as described
in chapter 3. Data must be transferred to the model from spatial and relational databases. This
may occur in a variety of ways, from the rudimentary (but effective) data interchange methods to
full-fledged integration in a DSS, running along with the other tools that make up a DSS. The
difference from the methods described in chapter 3 is that the communication is not only with the
GIS, but also with all elements of the DSS.
4.3 Evaluation Tools
Evaluation tools assist the decision-maker by presenting the output from the process and state
information in a manner consistent with resource or policy appraisal (Reitsma et al. 1996).
Evaluation tools may be of many forms. While much of the above discussion is framed around
the excellent review of DSS by Reitsma et al. (1996), the discussion of optimization deviates
somewhat from their discourse. Reitsma et al. (1996) do not consider optimization tools to be
strictly an evaluation tool, nor do they feel that optimization has been accepted by the user
community. While perhaps true for classical optimization techniques, the development of new
Intelligent Search Techniques (1ST) is proving to be useful for many realistic problems that are
unsuitable for traditional methods.
4.4 Overall DSS for Water Management
An overall DSS for water management of hydropower and river operations is shown in figure
4.5. This DSS combines the concepts of a centralized database, including hydrologic as well as
spatial information, and utilizes two different models that access that data; the Modular
Modeling System (MMS) which is a watershed and general environmental model, and
RiverWare, which models rivers and reservoirs. Evaluation tools are included within each of the
model components. The DSS includes a GIS as a tool for the user to query the common spatial
database. This DSS was developed by the Center for Advanced Decision Support in Water and
Environmental Systems (CADSWES) at the University of Colorado at Boulder, with support
from the Tennessee Valley Authority and the US Bureau of Reclamation. This DSS focuses on
large watersheds with complex reservoir and hydropower operations.
28
-------
River Ware System
River and Reservoir Management Models
I Long-teini Policy I
| and Planning |
I Mid-teim
| Short-turn |
I Operations |
L J
Data Sources
River & Res ervoir
Telemetry
SCADA
NEXRAD
Data Management System
HDB
Hydrolo gic D atab as e
Query., Display,
and Analysis
GIS
Statistics
Tradeoffs
Risk
Modular Modeling System (MMS)
Watershed and Environmental Models
I Root Zone I
I Models {ARS) |
I Precipitation- I
I Runoff Models |
S (dime nt I
Tianspoit Models |
Figure 4.5: CU-CADSWES DSS
(Fulpetal., 1994)
A DSS framework for the urban stormwater field is presented in figure 4.6. This DSS is an
amalgamation of the different components of the Mike series of software produced by the Danish
Hydraulic Institute (DHI), emphasizing their interoperability and common database, Mike Info.
The database (relational and spatial) is the common link between separate functions and
applications of the DSS. The peripheral models include Mike-11 for urban drainage, Mike SHE
for distributed watershed modeling, WUS for river basin planning, and NAM for statistical
analysis of streamflow/unguaged catchments.
29
-------
wus
« fiver kajip planting
River basin studies
Runoff from
ngauged areas
Wetland dynamics
Irrigation
Figure 4.6: Danish Hydraulic Institute DSS, based on integrated water resources modeling (DHI,
1998)
30
-------
5.0 Application of GIS and DSS to Micro Storm Analysis
This chapter focuses upon the application of GIS, database management, and DSS to the urban
stormwater management problem. A textbook case study from Tchobanoglous (1981) is used to
develop a GIS and an accompanying relational database. The database is used with hydrologic
and hydraulic models, and a cost analysis module. The combination of these components
represents a systematic urban stormwater design tool. The tool is then interfaced with an
optimization software package to develop optimal designs of the proposed network. The costs of
these designs can then be compared with a decentralized approach to controlling runoff. This
was done by using the GIS in conjunction with the NRCS analysis, which computes the initial
abstraction storage volume that is lost as a result of development. Using unit costs developed in
Heaney et al. (1999a), the optimal suite of controls can be selected using linear programming
(LP).
A diagram of the process used in the chapter is found in figure 5.1. The reader may notice
similarities between some of the components of a DSS and figure 5.1. In particular, the problem
consists of a database, simulation tools, and evaluation tools, similar in concept to that of a DSS
presented by Reitsma et al. (1996). The database includes GIS and its inherent spatial database,
but also a cost database, and a hydrologic database. The simulation tools consist of the NRCS
curve number method for computation of initial abstraction, the hydrologic model spreadsheet
template, the hydraulic model spreadsheet template, and the costing module. The evaluation tool
consists of a genetic algorithm to optimize the stormwater network, and a linear programming
model to evaluate proposed controls based upon unit costs developed in Heaney et al. (1999a).
Although not integrated into a single software program, the process shown here closely parallels
that of a DSS. The utility of GIS (to the urban stormwater field) is enhanced by its close
integration with the database, models, and analysis tools used in the problem. Because of the
large investment in time and resources necessary to construct an urban GIS, there is a natural
tendency for the GIS system to move to center stage. However, the value of the GIS is when it is
fully integrated within a DSS which is then used to address complex processes that cannot be
easily solved by other means.
Key considerations are the concepts of accuracy and scale as they apply to GIS data. Since the
datasets presented here vary substantially in terms of their level of detail and scale, a discussion
of spatial scale becomes necessary.
31
-------
DSS
Evaluation Tools
Optimization
Linear Programming (LP)
Genetic Algorithms (GA)
Database \
Relational (nongraphic)
addresses
billing
unit costs
time series input data
GIS/Spatial Database
Themes
Topography
Soils
Land use
Streets
Right of way
Pipe network
Parcels
'Simulation Tools
NRCS CN Hydrologic Method
Rational Method
Hydraulic Design Template
Cost Template
Figure 5.1: Proposed DSS for microstorm analysis
5.1 Spatial Scale and GIS-Stormwater Modeling
A recent software development, BASINS 2.0, developed by TetraTech for the US Environmental
Protection Agency, has created interest in the development of model-graphical user interface-
GIS linkages within the water community. BASINS 2.0 runs within Arc View 3.0 and includes a
national dataset on the attributes listed in Table 5.1 (Battin, et al. 1998).
32
-------
Table 5.1: Available BASINS data attributes
(Battinetal. 1998)
Spatially Distributed Data
Land use/land cover (GIRAS)
Urbanized areas
Populated place location
Reach File, version 1 (RF1)
Reach File, version 3 (RF3)
Soils (STATSGO)
Elevation (DEM)
Major roads
Environmental Monitoring Data
Water quality monitoring station summaries
Water quality observation data
Bacteria monitoring station summaries
Weather Station Sites (477)
Clean Water Needs Survey
Point Source Data
Permit Compliance System
Industrial Facilities Discharge (IFD) sites
Toxic Release Inventory (TRI) sites
USGS Hydrologic unit boundaries
Drinking water supplies
Dam sites
EPA region boundaries
State boundaries
County boundaries
Federal and Indian Lands
Ecoregions
USGS gaging stations
Fish and wildlife advisories
National Sediment Inventory (NSI)
Shellfish Contamination Inventory
Resource Conservation & Recovery Act (RCRA) sites
Mineral availability system/mineral industry location
Superfund national priority list sites
BASINS 2.0 includes tools for automatic watershed delineation and handling of digital elevation
models (DEM). Its main data handling routines include: Target, which is a regional, or state
level broad-based watershed water quality or point source assessment tool; Assess, which
operates a smaller scale of one or a few watersheds and enclosed discharge points or water
quality stations; and Data Mining, which dynamically links water discharge stations and
geographic location information. Modeling tools include a nonpoint source model (later to be
enhanced by the addition of SWAT, the MS Windows based nonpoint source model developed
by the USD A), HSPF, Qual-2E, and Toxiroute. Model post processors include graphs (Battin,
Kinerson, and Lahlou 1998). EPA SWMM may be linked with BASINS in the future.
33
-------
The accepted accuracy levels of mapping work are listed in Table 5.2 (Shamsi et al. 1995). Most
of the BASINS work and modeling have been on a watershed or regional level scale. An
example is shown in figure 5.2. The size of this file relative to the area it represents reflects a
scale of about 1:2000.
Table 5.2: Minimum horizontal accuracy and example features for various map scales in urban
areas
(Shamsi et al. 1995)
Map Scale
1"=50'
l'=100'
1"=200'
1"=2000'
Minimum Horizontal
Accuracy, per National Map
Accuracy Standards
±1.25'
±2.50'
±5.00'
±40'
Examples of Smallest
Features Depicted
Manholes, catch basins
Utility poles, fence lines
Buildings, edge of pavement
Transportation, developed
areas, watersheds
£eft !$ew Jtheme Jaraphics Tcsget Assess Model fieport Lookup Utility
S3
Permit Complianc
ndustrial Facilities
National Priority Lis
ous int Soli
Qyjlity StJtk
Baettria Stations
Drinking Water Sup
A
0am Lccatioris
I
Reach F]i«, V1
Figure 5.2: BASINS dataset for Boulder, Colorado
Automatic watershed delineation of undeveloped areas may be appropriate at this scale.
However, urban systems have extremely altered topography. The topography in these types of
catchments can be represented by a dense DEM; however, development of watersheds based
34
-------
upon triangular irregular networks (TINs) from this information is not presently reliable. This is
not to say that the database information presented from a watershed level scale has no value.
Actually, having the information presented in figure 5.2 can provide the modeler with possible
alternative sources of data, possibly structures that may not have been considered, etc. However,
a key disadvantage of using GIS information from different scales of accuracy is that a vector
GIS cannot show any uncertainty. An assumption of the GIS model is that the points are known
to 100% accuracy. This leaves it up to the reader to verify locations and discrepancies,
particularly when the scales, and the resultant accuracy, differ widely.
In addition, the memory requirements for regional level stormwater-GIS modeling are
staggering. For example, the City of Boulder has an ongoing GIS project, a broad view of which
is shown in figure 5.3 (Brown and Caldwell and Camp, Dresser, and McKee, 1997).
Figure 5.3: Arc View coverage of Boulder, Colorado
(Brown and Caldwell and Camp, Dresser, and McKee, 1997)
Minor roads are outlined in light green, major roads are outlined in thick maroon; creeks are
shown in light blue, lakes in shaded blue, and sub-basins boundaries in black. Not shown for
better clarity, but available, are parcels, zoning, topography, watershed boundaries, and several
other miscellaneous themes. Also not shown is the database describing each graphic entity (for
example, the parcel database). Even at this finer resolution, urban stormwater modeling is at too
aggregate a scale to evaluate sets of alternatives that include micro-topographical changes to
implement BMPs.
35
-------
In order to evaluate the effects of source and neighborhood-level BMPs, the coverage as depicted
in figure 5.4 is needed. This area is a block in the University Hill neighborhood of Boulder. The
parcel theme is shown in red, the street centerline is shown in green, and the streams are shown
in blue. Topography is not shown, but exists in this database at the 40 foot contour interval,
reflecting a scale of about 1:200.
Figure 5.4: City of Boulder Arc View GIS coverage for University Hill neighborhood, Boulder,
Colorado.
36
-------
Moving towards a finer dataset, another parallel project at the City of Boulder, in the Public
Works/Public Utilities group, is an Automated Mapping/Facilities Management (AM/FM)
project in which the city's infrastructure is being mapped by street surveys and aerial
photography. The end product at the present time is a tiled set of AutoCAD maps representing
portions of the city. The representation of this project for the same block in the University Hill
neighborhood is shown in figure 5.5. The scale of this information is approximately 1:100. The
green layer signifies building rooflines, yellow is the street centerline and parking
areas/driveways, red is sidewalks, and black is the curblines. This file has been edited
extensively to eliminate extraneous lines and close polygons. Since the end product of the
project was a set of AutoCAD maps, manual and automatic processes on the digital photography
result in multiple lines whose ends may not match and polygons that do not close. Although
acceptable for graphic presentation, this information is of limited value for extracting data for
stormwater evaluations. Extensive cleanup is necessary for this information prior to inputting it
into a GIS. Topography for this information is available for an additional cost at a 2-foot contour
interval. At the present time, conversion of this data to Arclnfo and Arc View coverages is
underway.
5.2 Description of Happy Acres Case Study GIS
A textbook study area, nicknamed "Happy Acres", was selected from Tchobanoglous (1981). A
GIS coverage for this case study was developed. The study area was first digitized in AutoCAD,
then edited for geometric consistency, i.e., parallel lines were kept parallel, polygons were joined
from separated lines, to make the transition to GIS easier. The mix of land uses for the area is
laid out in table 5.3. The reconstructed AutoCAD drawing of the area is shown in figure 5.6.
The topography of the study area and the layout of the storm sewer system are shown in figure
5.7 (Tchobanoglous 1981). Land use is shown in figure 5.8. Soils data is shown in figure 5.9.
The entire study area is divided into 54 sub-areas that range in size from 0.8 to 5.4 acres in size.
A description of the attribute information in figure 5.6 is found in table 5.4.
Table 5.3: Mix of land uses in Happy Acres
Land Use
Residential, low density
Residential, medium density
Apartments
School
Commercial
Total
Acres
20.8
51.7
10.0
5.7
18.4
106.6
Dwelling
units/acre
2-3
6-8
10
N/A
N/A
37
-------
Figure 5.5: AutoCAD file for University Hills neighborhood, Boulder, Colorado.
38
-------
Figure 5.6: AutoCAD coverage for study area
(adapted from Tchobanoglous, 1981)
39
-------
IN
A
100
Figure 5.7: Study area topography
(adapted from Tchobanoglous, 1981)
40
-------
I Tchoban_roads2_region.shp
\lwgrd3
Apartment
Commercial
LD Residential
MD Residential
School
No Data
300 0 300 600 900 12001500180021002400 Feet
N
Figure 5.8: Study area land use
(adapted from Tchobanoglous, 1981)
41
-------
Tchoban_roads2_region.shp
Tchoban_drainage2_region.shp
Soilgrid
Clay
Rock
Silt
No Data
300 0 300 600 900 12001500180021002400 Feet
N
Figure 5.9: Study area soils
(adapted from Tchobanoglous, 1981)
42
-------
Table 5.4: AutoCAD layers for study area
Layer/Object Category
Streets
Manholes
Sewer lines
Land use boundary
Hydrologic boundary
Parcel
Rooflines
Driveways
Soils
Color
Not shown (for clarity)
Blue
Red
Aqua
Blue
Green
Magenta
Orange
Not shown (for clarity)
The AutoCAD layers shown in table 5.4 become the following Arc View themes:
1. Streets
2. Manholes
3. Sewer lines
4. Land use boundary
5. Hydrologic boundary
6. Parcel
7. Rooflines
8. Driveways
9. Soils
A relational database is associated with each graphic object, grouped according to type.
Attributes associated with parcels are address and land area; and with streets are right of way
width, length, land area, and street name. Soils and land use exist in separate tables, and this
information was combined with the parcel and street databases by performing an intersection
query on the two themes. The results of the query can also be output to an Excel spreadsheet by
using Arc View's Avenue script language and Microsoft's Dynamic Data Exchange (DDE). This
procedure was used to extract the relevant attribute information for parcels and streets.
The rights of way identified in figures 5.6 through 5.9 were assigned widths based upon the
following criteria. Minor streets within the development have a 50 foot right of way, a minor
arterial is given a 60 foot right of way, and a major arterial a 70 foot right of way. The profile of
each right of way is given in table 5.4. The reader is referred to Heaney et al. (1999a) for further
details on the database.
Table 5.5: Right of way characteristics
R/W
ft
50
60
70
Length,
ft
28,680
1,124
2,741
Curb
ft
4
4
4
Parking
ft
8
16
16
Landscaping
strip, ft
10
10
18
Sidewalk
ft
8
8
8
Traffic
Lanes, ft
20
22
24
43
-------
Note: Some of the parameters are summed from both sides of the street.
Lot characteristics for the two single lot residential land use classifications are presented in table
5.6. Lots were aggregated in this manner for the optimization; however the GIS contains the full
heterogeneity of each parcel.
Table 5.6: Lot characteristics for residential parcels
Land Use
MD Residential (6-8 DU/AC)
LD Residential (2-5 DU/AC)
No. of
Parcels
255
51
Roof
Area
SF
1,600
2,000
Patio
SF
200
400
Driveway
SF
600
800
Landscap-
ing
SF
3,600
9,800
Total
Area
SF
6,000
13,000
For the apartments, commercial, and school land uses, an aggregate analysis was used because
these land uses exhibited multi-parcel characteristics, such as for parking. A summary of these
characteristics is found in table 5.7
Table 5.7: Aggregate characteristics for commercial, apartments, and schools
Land Use
Apartments
Commercial
School
No. of
parcels
2
6
3
Stories
2
1
1
Parcel
Area
SF
162,680
481,070
149,407
Roof
Area
SF
46,927
152,839
69,080
Parking
Area
SF
75,083
304,678
51,807
Landscap-
ing
SF
40,670
23,553
28,521
5.3 Simulation Tools for Hydraulic Design
The storm sewer network for the Happy Acres subdivision is diagrammed in figure 5.10. A
spreadsheet template has been developed to simulate and optimize storm sewer design for the
Happy Acres neighborhood-see tables 5.8 to 5.10. The value of better data obtained using GIS
can be estimated by evaluating the designs with and without this better information. The
following columns in table 5.7 represent data that can be obtained partially or totally with a GIS
system for this example.
Column
5
6
7
Description
Sewer length
Stormwater area
Dwelling units per acre
The output from table 5.8 is the design peak discharge leaving each subcatchment. This
information is input to the sewer design table 5.9 that finds feasible combinations of pipe
diameters and slopes. The constraints on the design are:
Minimum depth of cover for the sewer, and
44
-------
Minimum velocity in the pipe.
The decision variables are pipe diameter (column 8) and slope (column 6). Trial and error
procedures are used to find a feasible solution to the design problem. In more sophisticated
analysis, the costs of the alternative systems are evaluated as shown in table 5.10. The
background for development of the cost relationships found in this table can be found in Heaney
et al. (1999a), and is based upon data obtained from R.S. Means (1996a). Additional GIS data
are helpful for the cost analysis. Specifically, soil conditions (column 8) affect the side slopes of
the sewer excavations, and the bedding costs.
Figure 5.10: Study area sewer network
(adapted from Tchobanoglous, 1981)
45
-------
Table 5.8: Sewer network design hydrology
(Heaneyetal. 1999)
*.**
«;»*
-s as
Jfiflt
t •» , J3 2#
__ J€ Si _ jr._jjry jP.._5&.._t: g.
b=
i .^
^LS^
* S-S
3 KNt
*«*
-B»S
46
-------
Table 5.9: Sewer network design hydraulics
(Heaney etal. 1999)
Street
MflSMfc** erff^K^
Mffipne-O edwaod
Ma&^t i?**?** K*T
KUit -SM? wm*n>nr
Waspr1 F--**rvnc
ftJ^T*
-Hr
-afc,
' ssfc
V* al-HP
"»« r^—
V» Ft «W
W Fwirsg
W Fw«*f
W Frareat
£• For««
£. F3r«!
KsiftamM-c/Hm
S^Carr^eCfcm
RycJifWXsVE^
S vcamoreyE: NTJ
S^C'SHPW*'1'©™
Svcarnof'erfTtni
Sj^cStT^WEfefi
A&Ju'fc-jarrU»in: !-
A'Btw'Aceri'aBii'S*'
As fii°&*r£¥7vferc *•
Aft WAcW iVB$e t"
Asrfis'ftOTm^tore:'-
AsWiftctsr-wSsrc i"
A*WAcomi¥Wr^
East Steffi
Fasd ftarrJN
Af. Cfedw
w C*taf
W Cedar
w r:«rt»r
E Ces^
E. CeOS?
.Stspefli
A*c*ri
W Ashe-crt
W Ajghrssdn!
WV ASJTfTti-SdTt
L A«!*nont
(f JM*WK?™
H*>qMa«s
MoMand
*toc«e
oak
W.afcw!
Fiaf«*
EJm
FCN«9*
S*^h
,ftR*en
,4»K£MT!C«!
«-f4*»a^d
r
Tw.
Nw-r
Bra*v-ft
»*-
lira-It
i&w-ri-
fe-^r
Srarer!"
B..,.'
&ai-c*-
gfrfliw"*
anarch
A am n
»w*J
n a,,*
Htarvi-
l&arajh
Branch
a-BITEf*
aWMh
Snaastt:
Bi-araift
B/areP
8?3r*ch
Bs-ancf--
«%mvr
S^anc"
S'ancf
•*arc»-
3 o«c*
tS-arcl*
*fl! *>
Bror-cr
BfHr-c!
Bnarrvf
3. aocn
a-ncarf
&a--cr
^anr*
tkdr^
B-arcf
&W""
»a™&
5^B«Cf
«3?a«v*
-5,-arrr
§n»*«cf
i&rsincf
£.-B!Xii-
J^grvt-
i^arjcr
&BrtCft
Tryi*
Trunfc
Tarn*;
Ton*
frywK
Tn^ik
Tnx*
Tf^^s
*^.^n*
1"T6irtfc
Tn^*
3
Mc«
FruCTf
l
4
-3
Jt
87
K.
Pf
QB=i
(™_^_
tOC
-Or
1DC
ice
10A
1?R
17A
1 SG
11F
IIP
IIS
tir-
!.0
1J A
' "SLT
fy
TJffc
"1C
;fo
1^*
ji"
- ir
'.«•"
KB
, '».-,
HE
14F
i5e
1*^A
IF-'
1*0
1*iA
'SC
i«r
17A
e
7
S
8
to
11
O
SJ
14
ts
1?
*
dm
To
J
#
5
«
!^C
M)
PJ,
'tiA
5
X
"•JT
10B
10A.
1O
llfl
13
S1^-
nr
HO
1«C
'1^
"ft
r ^
-JL
*-»o
•1C
*p4
B-a""^
2
2
2
2
2
2
2
2
?
2
2
2
J
J
2
2
~>
2
?
2
2
2
2
2
2
2
2
2
2
J
2
2
j.*
2
2
2
J
2
2
47
-------
Table 5.10: Sewer network design cost
(Heaney etal. 1999)
1
******
^toSd^
5^
Oa*
Oak
W FBTO»!
W Fsww&S
w l
-------
Using a new intelligent search technique called genetic algorithms (GAs), the optimal design was
found by having Evolver (Palisade Corp., 1998), a commercially available GA, evaluate different
combinations of pipe diameters and slopes until the least cost design is found.
5.4 Simulation Tools for Hydrologic Analysis
Heaney, Wright, and Sample (1999) describe a method for using the NRCS curve number (CN)
approach for evaluating micro storms. The fundamental principle is that development should not
reduce the initial soil moisture storage that existed prior to development. This initial soil
moisture storage is equivalent to the initial abstraction as calculated using the Natural Resources
Conservation Service (NRCS) curve number (CN) method. The initial abstraction is a good
measure of the ability of the soil system to filter the stormwater. The initial abstraction, as a
function of CN, is shown in table 5.11. Inspection of table 5.11 reveals the importance of CN. A
low CN of 30 corresponds to an initial abstraction of 4.67 inches. Even at a CN of 80, the initial
abstraction is still 0.5 inches. If the original CN is fairly low, then a significant amount of soil
moisture storage is lost if this area is rendered impervious by development.
Table 5.11: Initial abstraction as a function of curve numbers, CN
CN
20
30
40
50
60
la, inches
8
4.67
3
2
1.33
CN
70
80
90
100
la, inches
0.86
0.5
0.22
0.02
This method uses the concept of modifying the CNs for the developed condition so that the
modified CN is the same as the natural CN. The more cost-effective controls tend to focus on
utilizing the pervious area for more intensive infiltration. Alternatively, we seek to design
hydrologically functional landscapes as described in the next section.
5.4.1 Hydrologically functional landscaping
Traditional landscaping relies on covering most, if not all, of the pervious area with grass. The
lot is graded so that stormwater drains to the street and/or the rear of the lot as shown in figure
5.11 (Dewberry and Davis 1996). An example of a hydrologically functional landscape is shown
in figure 5.12 (Prince Georges County 1997). The general idea is to maximize the infiltration of
stormwater by providing depressions, draining runoff from impervious areas to pervious areas,
providing more circuitous routes for the stormwater to increase the time of concentration, etc.
49
-------
a) Lot Grading: Drainage Directed Toward Front of Dwelling
b) Lot Grading: Drainage Directed Toward Rear of Dwelling
c) Lot Grading: Drainage Directed Toward Front and Rear of Dwelling
Figure 5.11: Conventional storm drainage
(Dewberry and Davis 1996).
50
-------
100-Fcot Maximum Overlaid Flow el Minimum 1% Slope
I*
Channel Bottom ij|i|iViai Swale
© 2% miii
jh A A A A A ^ Jk A JL A
-Street-
PLAN VJEW
10 Feet -
Hi =1111=1 ill = 1.1'= .i.hKVI.Si.tKsl.l
\ I "" ]
i^A^N- ljv=U.S Ife
SP!:1 SI -I =Jhl SIli IS-' I! =11 ~^^"~" ii i I T i r ^|V~ !!| Sl'l 'Still £
ELEVATION
Figure 5.12: Illustration of hydrologically functional landscape
(Prince Georges County 1997).
51
-------
5.4.2 Determination of runoff volumes using NRCS method
Each developed land use is assigned a curve number (CN) based upon work done by the Soil
Conservation Service (1986). The initial abstraction, or available storage, is estimated by the
following equation:
/ =200-2 5.1
a CN
The final list of 10 permeable and 16 impermeable candidate land uses with their expected
effectiveness as measured by their curve number (CN) and the associated initial abstraction in
inches, calculated using equation 5.1, are shown in table 5.12. The CNs range from 25 to 98.
The initial abstraction associated with a CN of 25 is 6.00 inches of precipitation. Making this
land impervious increases the CN to 98 with an associated initial abstraction of only 0.04 inches,
a major loss of infiltration capacity. Using unit costs in $/square feet, which are developed in
section 5.5 (and detailed in Heaney et al. 1999a) and having determined the appropriate
abstraction, it is possible to convert the control option costs to $/gallon, which is done in the last
four columns of table 5.12. Several different functional land uses are given in table 5.12. These
include two kinds of aspens, fair, and good (referring to the health and density of the stand), two
kinds of driveways, permeable and impermeable, three types of grass cover, good, fair, and poor
(again referring to health and density), four types of parking, a traditional impervious surface,
and three of gradually increasing porosity, two types of patios, permeable and impermeable, two
kinds of roofs, with retention and without, two kinds of sidewalks, permeable and impermeable,
storage (detention pond), four types of streets, a traditional street profile with curb and gutter, a
street with curb and gutter and porous pavement, an impervious street with swales, and a street
with porous pavement and swales, two types of swales of progressively greater infiltration
capacity (and greater area), and two kinds of wooded areas, fair and poor, again referring to
health and density of the trees.
These values are unique to the soil type heading the column. The NRCS method aggregates clay
and silt together as soil type "B", and rock as soil type "D". Unit costs expressed as $/gallon are
useful for comparative purposes, as will be seen later.
5.4.3 Breakdown of calculated volumes per function
A functional analysis within each land use and soil classification was performed by adding the
total areas for the functions of roof, lawns, driveways, and parking (for non-right of way uses),
and streets, curbs, parking, sidewalks, and lawns for right of way areas. Volumes of developed
runoff can then be calculated by multiplying the initial abstraction by the appropriate area.
Predevelopment runoff can be calculated by using the composite curve number for Happy Acres
prior to development of 63.07, determining an initial abstraction for each soil group, and
multiplying this again by the area as done for the developed volumes. The result of this analysis
is found in table 5.13. This provides a snapshot of the increase in runoff volume for each land
use generated by development. Because the NRCS method is unique to soil characteristics, this
is further broken down by soil group.
52
-------
Table 5.12: SCS hydrologic classifications, and calculation of unit storage values, 1/99$
No.
1
2
1
2
3
4
5
6
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
Type
Permeable
Permeable
Impervious
Impervious
Permeable
Permeable
Permeable
Impervious
Impervious
Impervious
Impervious
Impervious
Impervious
Impervious
Impervious
Impervious
Impervious
Permeable
Impervious
Impervious
Impervious
Impervious
Permeable
Permeable
Permeable
Permeable
Cover Description
Cover type and hydrologic condition
Aspen-mountain brush mixture: Fair:30-
70% ground cover
Aspen-mountain brush mixture: Good:
>70% ground cover
Driveway
Driveway-porous pavement
Lawns, pasture, grassland: Fair condition
(grass cover 50-75%)
Lawns, pasture, grassland: Good condition
(grass cover >75%)
Lawns, pasture, grassland: Poor condition
(grass cover < 50%)
Parking
Porous parking 1
Porous parking 2
Porous parking 3
Patio
Porous patio
Roof
Roof with detention
Sidewalks
Sidewalks with porous materials
Storage-off-site in infiltration/detention
basins
Street with curb and gutter
Street with curb and gutter and porous
pavement
Street with swales
Street with swales and porous pavement
Swales 1
Swales 2
Woods: Fair: Woods are grazed but not
burned, and some forest litter
Woods:Good: Woods without grazing, and
adequate litter and brush
ID
Aspen F
Aspen G
Driveway 1
Driveway 2
Grass F
Grass G
Grass P
Parking 1
Parking 2
Parking 3
Parking 4
Patio 1
Patio 2
Roof 1
Roof 2
Sidewalk 1
Sidewalk 2
Storage
Street 1
Street 2
Street 3
Street 4
Swales 1
Swales 2
Woods F
Woods G
Curve Number
A
28
25
98
70
49
39
68
98
61
46
36
95
76
95
85
98
70
15
98
70
76
61
46
29
36
25
B
48
30
98
80
69
61
79
98
75
65
55
95
85
95
85
98
80
20
98
80
85
75
65
50
60
55
C
57
41
98
85
79
74
86
98
83
77
67
95
89
95
85
98
85
35
98
85
89
83
77
62
73
70
D
63
48
98
87
84
80
89
98
87
82
72
95
91
95
85
98
87
40
98
87
91
87
82
67
79
77
Initial Abstraction in inches
A
5.14
6.00
0.04
0.86
2.08
3.13
0.94
0.04
1.28
2.35
3.56
0.11
0.63
0.11
0.35
0.04
0.86
11.33
0.04
0.86
0.63
1.28
2.35
4.90
3.56
6.00
B
2.17
4.67
0.04
0.50
0.90
1.28
0.53
0.04
0.67
1.08
1.64
0.11
0.35
0.11
0.35
0.04
0.50
8.00
0.04
0.50
0.35
0.67
1.08
2.00
1.33
1.64
C
1.51
2.88
0.04
0.35
0.53
0.70
0.33
0.04
0.41
0.60
0.99
0.11
0.25
0.11
0.35
0.04
0.35
3.71
0.04
0.35
0.25
0.41
0.60
1.23
0.74
0.86
D
1.17
2.17
0.04
0.30
0.38
0.50
0.25
0.04
0.30
0.44
0.78
0.11
0.20
0.11
0.35
0.04
0.30
3.00
0.04
0.30
0.20
0.30
0.44
0.99
0.53
0.60
Unit
cost
$/sf
$2.00
$3.00
$0.23
$0.25
$0.81
$1.03
$0.70
$0.23
$0.25
$0.26
$0.28
$0.19
$0.19
$0.00
$1.50
$0.19
$0.19
$5.00
$0.25
$0.26
$0.27
$0.28
$3.00
$6.00
$0.80
$1.40
Unit Costs in $/gallons
A
$0.62
$0.80
$9.21
$0.47
$0.63
$0.53
$1.19
$9.21
$0.31
$0.18
$0.13
$2.89
$0.49
$0.00
$6.82
$7.44
$0.36
$0.71
$9.77
$0.49
$0.68
$0.35
$2.05
$1.97
$0.36
$0.37
B
$1.48
$1.03
$9.21
$0.80
$1.45
$1.29
$2.12
$9.21
$0.60
$0.39
$0.27
$2.89
$0.88
$0.00
$6.82
$7.44
$0.62
$1.00
$9.77
$0.84
$1.22
$0.67
$4.47
$4.81
$0.96
$1.37
C
$2.13
$1.67
$9.21
$1.13
$2.45
$2.35
$3.45
$9.21
$0.98
$0.71
$0.46
$2.89
$1.25
$0.00
$6.82
$7.44
$0.88
$2.16
$9.77
$1.19
$1.74
$1.09
$8.06
$7.85
$1.73
$2.62
D
$2.73
$2.22
$9.21
$1.34
$3.42
$3.30
$4.55
$9.21
$1.34
$0.97
$0.58
$2.89
$1.57
$0.00
$6.82
$7.44
$1.04
$2.67
$9.77
$1.41
$2.17
$1.49
$10.9
6
$9.77
$2.41
$3.76
Source: adapted from SCS, 1986
53
-------
Table 5.13: Calculation of developed and predevelopment stormwater volumes for
Happy Acres
Land Use
Apartments
Commercial
MD Residential
LD Residential
School
Streets
50
60
70
Function
Roof
Parking
Driveway
Lawns
Roof
Parking
Driveway
Lawns
Roof
Parking
Driveway
Lawns
Patio
Roof
Parking
Driveway
Lawns
Patio
Roof
Parking
Driveway
Lawns
ROW
Street with
curb and gutter
Parking
Sidewalks
curb
Lawns
ROW
Street with
curb and gutter
Parking
Sidewalks
curb
Lawns
ROW
Street with
curb and gutter
Parking
Sidewalks
curb
Lawns
Total
Soil
Types
B
sf
46927
75083
0
40670
95132
44810
0
6839
140800
0
52800
353666
17600
102000
0
40800
491233
20400
69080
51806
0
28521
659728
105556
105556
105556
52778
52778
87540
11672
23344
11672
5836
5836
13195
1508
3016
1508
754
754
Soil
Types
D, Total
sf
0
0
0
0
57707
259868
0
16714
267200
0
100200
538755
33400
0
0
0
0
0
0
0
0
0
774288
123886
123886
123886
61943
61943
0
0
0
0
0
0
189531
21661
43321
21661
10830
10830
Area, Total
sf
46927
75083
0
40670
152839
304678
0
23553
408000
0
153000
892420
51000
102000
0
40800
491233
20400
69080
51806
0
28521
1434016
229443
229443
229443
114721
114721
87540
11672
23344
11672
5836
5836
202726
23169
46337
23169
11584
11584
1724282
Volume
Developed, B
cf
412
255
0
4334
834
152
0
729
1235
0
180
37686
154
895
0
139
52344
179
606
176
0
3039
359
359
359
180
3952
40
79
40
20
437
5
10
5
3
56
Volume
Developed, D
cf
0
0
0
0
506
884
0
696
2344
0
341
22448
293
0
0
0
0
0
0
0
0
0
421
421
421
211
1966
0
0
0
0
0
74
147
74
37
344
Total Vol.
Developed
cf
412
255
0
4334
1341
1036
0
1425
3579
0
520
60134
447
895
0
139
52344
179
606
176
0
3039
780
780
780
390
5918
40
79
40
20
437
79
158
79
39
400
140882
Volume
Undev.,
B
cf
4580
7327
0
3969
9284
4373
0
667
13741
0
5153
34514
9954
0
3982
47939
6742
5056
0
2783
10301
10301
10301
5151
5151
1139
2278
1139
570
570
147
294
147
74
74
Volume
Undev.,
D
cf
0
0
0
0
49
86
0
68
229
0
33
2191
0
0
0
0
0
0
0
0
41
41
41
21
192
0
0
0
0
0
7
14
7
4
34
Tot. Volume
Undev.
cf
4580
7327
0
3969
9333
4459
0
735
13969
0
5186
36705
0
9954
0
3982
47939
0
6742
5056
0
2783
10342
10342
10342
5171
5343
1139
2278
1139
570
570
154
309
154
77
107
210758
54
-------
The functions were then compared across land uses by computing the difference between
the sum of the function's pre-development and post-development storage volumes. The
result is plotted as a bar chart in figure 5.13. The greatest impact is from streets and
roofs, with roughly equal values of storage volume reduction. Patios are insignificant in
this analysis. Lawns actually add a great deal of storage, offsetting somewhat the drastic
reductions from roofs and streets. Driveways and parking lots result in smaller
reductions in volume, however, the local impact may be significant.
140000
120000
100000
80000
.S> 60000
.Q
3
o
£
f= 40000
20000
DVolume, post development, (CF)
Volume, predevelopment (CF)
D Difference
-20000
-40000
Function
Figure 5.13: Allocation of available storage for initial abstraction and land use.
5.5 Simulation Tools for Cost Analysis
If the cost of modifying the CNs can be determined, then cost-effective strategies can be
developed for maintaining the undeveloped CN for each parcel or combination of parcels.
Since most BMPs are land intensive, a careful evaluation of their costs must include land
valuation. The costs used in the analysis were developed in Heaney et al. (1999), for
each control and each land use. The procedure for calculation of the land component of
controls within one land use, medium density residential, is outlined in table 5.14.
55
-------
Table 5.14: Land valuation for medium density lot, 1/99$
Component
Roof-house
Roof-garage
Driveway
Yard
Patio
Total
SF
1200
400
600
3600
200
6000
%of
total
20.0%
6.7%
10.0%
60.0%
3.3%
100.0%
$/sf
$56.25
$34.00
$4.00
$1.00
$4.00
Construction
Cost, $
$67,500
$13,600
$2,400
$3,600
$800
$87,900
Total Land $
$8,790
$2,930
$4,395
$26,370
$1,465
$43,950
Unimproved
Land, $
$5,860
$1,953
$2,930
$17,580
$977
$29,300
An estimate of the cost in $/sf is found in column 4 of table 5.14. Next, the construction
cost (column 5) is obtained by multiplying column 2 by column 4. Next, the percentage
in column 3 is multiplied by the total of column 5 to obtain an estimate of the land cost,
in column 6. Column 7, the unimproved land cost, is obtained by multiplying the values
in column 6 by 2/3. The value of the 3,600 square feet of land for the yard function is
$26,370.
Next, opportunity costs must be calculated. This procedure is illustrated in table 5.15.
The value of $26,370 is annualized, using an interest rate of 6%, and an infinite term (as
in equation 6.2), to obtain $l,582/year. Then, this value is spread over 25 years at 6%, to
obtain $20,226. Dividing this value by 3,600 square feet gives $5.62/square feet. This
value is used for all grass types as the underlying value of the land is assumed to be
constant irrespective of the type of grass. Landscaping costs were developed from RS
Means (1996b), and updated to January 1999, and are presented in table 5.15 (for a
medium density residential lot). The initial capital investment consists of the cost of soil
preparation including sod, topsoil, and soil conditioners, and an irrigation system. For a
good lawn, the present value of the initial landscaping investment is $2.22 per square
foot. Costs for lesser quality lawns drop to $1.71/sf and $.95/sf for fair and poor quality
lawns. For the good lawn system, operation and maintenance costs add an additional
$2.45 per square foot bringing the total to $10.29 per square foot. An estimated 10
percent of this total cost is allocated to stormwater management. Similar estimates were
made for fair and poor lawns. The resulting total costs per square foot vary from $0.70 to
$1.03 per square foot. Better lawns have a lower CN and are thereby preferable from the
viewpoint of being able to store more water.
Similar estimates were made for the land valuation for low-density residential lots,
commercial, apartments, and schools. A similar procedure was followed for these uses,
except that the commercial, apartments, and schools are aggregated as one lot. However,
they also cost more. The cost for each control was then estimated using these land
valuations. The matrix of controls and land uses is presented in table 5.16. A linear
programming model is used to find the least costly mix for each land use. See Heaney et
al. (1999b) for a more detailed explanation of this method.
56
-------
Table 5.15: Cost analysis of landscaping for medium density lot, 1/99$
Item
A. Initial Capital Investment
1 . Soil preparation
Initial cost of sod
Initial cost of topsoil, 6"
Spreading topsoil, 6"
Soil conditioners
Sprinkler system
2. Opportunity Cost of Land
Land Investment Cost
Opportunity cost investment rate
Annual cost, $/yr.
Interest rate per year
Present worth over 25 years
Cost in $/ft2
Total of initial capital investment
B. Operation & Maintenance Costs, $
Lawn watering
Inches per year
% of pervious area that is irrigated
Cost of water, $/1 ,000 gallons
Present worth factor
Present worth, $/ft2
Lawn maintenance
Weeks per year
$/week
Maintenance area, ft2
Present worth, $/ft2
Sprinkler system maintenance
Total operation and maintenance costs, $
C. Total Cost, $/ft2
Portion attributable to stormwater
Assumed %
D. Cost for Stormwater
Input
Data
$26,370
6%
$1,582
0.06
$20,226
20
80%
$1.50
12.78
26
$8.46
2880
10%
Good
$/ft2
$0.43
$0.50
$0.64
$0.03
$0.62
$2.22
$5.62
$7.84
$0.24
$0.98
$0.25
$1.46
$9.31
$0.93
Fair
$/ft2
$0.34
$0.40
$0.51
$0.02
$0.44
$1.71
$5.62
$7.33
$0.15
$0.50
$0.15
$0.80
$8.13
$0.81
Poor
$/ft2
$0.26
$0.30
$0.38
$0.01
$0.00
$0.95
$5.62
$6.57
$0.09
$0.35
$0.00
$0.44
$7.01
$0.70
57
-------
Table 5.16: Calculation of unit costs for controls, including opportunity costs for land,
1/99$
ID
Aspen F
Aspen G
Driveway 1
Driveway 2
Grass F
Grass G
Grass P
Parking 1
Parking 2
Parking 3
Parking 4
Patio 1
Patio 2
Roof 1
Roof 2
Sidewalk 1
Sidewalk 2
Storage
Street 1
Street 2
Street 3
Street 4
Swales 1
Swales 2
Woods F
Woods G
LDRes
$/sf
$2.00
$3.00
$0.23
$0.25
$0.60
$0.69
$0.49
$0.23
$0.25
$0.26
$0.28
$0.19
$0.19
$0.00
$1.50
$0.19
$0.19
$5.00
$0.25
$0.26
$0.27
$0.28
$3.00
$6.00
$0.80
$1.40
MDRes
$/sf
$2.00
$3.00
$0.23
$0.25
$0.60
$0.69
$0.49
$0.23
$0.25
$0.26
$0.28
$0.19
$0.19
$0.00
$1.50
$0.19
$0.19
$5.00
$0.25
$0.26
$0.27
$0.29
$3.00
$6.00
$0.80
$1.40
Commercial
$/sf
$2.00
$3.00
$0.23
$0.25
$2.12
$2.18
$2.01
$0.23
$0.25
$0.26
$0.28
$0.19
$0.19
$0.00
$1.50
$0.19
$0.19
$5.00
$0.25
$0.26
$0.27
$0.28
$3.00
$6.00
$0.80
$1.40
School
$/sf
$2.00
$3.00
$0.23
$0.25
$2.49
$2.56
$2.38
$0.23
$0.25
$0.26
$0.28
$0.19
$0.19
$0.00
$1.50
$0.19
$0.19
$5.00
$0.25
$0.26
$0.27
$0.28
$3.00
$6.00
$0.80
$1.40
Apartments
$/sf
$2.00
$3.00
$0.23
$0.25
$1.22
$1.29
$1.11
$0.23
$0.25
$0.26
$0.28
$0.19
$0.19
$0.00
$1.50
$0.19
$0.19
$5.00
$0.25
$0.26
$0.27
$0.28
$3.00
$6.00
$0.80
$1.40
RW50
$/sf
$2.00
$3.00
$0.23
$0.25
$0.60
$0.69
$0.49
$0.23
$0.25
$0.26
$0.28
$0.19
$0.19
$0.00
$1.50
$0.19
$0.19
$5.00
$0.25
$0.26
$0.27
$0.28
$3.00
$6.00
$0.80
$1.40
RW60
$/sf
$2.00
$3.00
$0.23
$0.25
$0.60
$0.69
$0.49
$0.23
$0.25
$0.26
$0.28
$0.19
$0.19
$0.00
$1.50
$0.19
$0.19
$5.00
$0.25
$0.26
$0.27
$0.29
$3.00
$6.00
$0.80
$1.40
RW70
$/sf
$2.00
$3.00
$0.23
$0.25
$0.60
$0.69
$0.49
$0.23
$0.25
$0.26
$0.28
$0.19
$0.19
$0.00
$1.50
$0.19
$0.19
$5.00
$0.24
$0.26
$0.27
$0.28
$3.00
$6.00
$0.80
$1.40
58
-------
5.6 Optimization of Control Options for Happy Acres
The results of the LP optimizations are summarized in tables 5.17 and 5.18. The results
are allocated along functional grouping within each soil class in table 5.17, and
aggregated for each land use type in table 5.18. The least cost design allocates the
appropriate control option to the appropriate soil type and land use (soil is reflected in its
predevelopment CN, land use is reflected in the influence of land valuation on the cost of
the control). The changes in control options affect the appearance of the neighborhood,
and this is evident by inspection of table 5.17. For example, porous pavements were
selected (with curb and gutter) for the street design in the rocky soil. In the clay and silt
soils where more percolation can take place, the LP model selected a street design with
porous pavement and swales instead of curb and gutter. A similar allocation took place
with parking areas; both were porous, however, the more permeable soils resulted in a
design that had a higher infiltration capacity. The more permeable driveway, patio, and
sidewalk choices were chosen in both soil types. Good grass was selected over the other
options for all soil areas, except in commercial areas where poor was selected in silts and
clays, and fair was selected in rock. This is due to the relatively small amount of
landscaped area in commercial areas. There may be other aesthetic factors with
commercial areas that would put a higher premium on a higher quality grass other than
for a stormwater quality function. Aspens were chosen, but in small amounts, so it would
not look significantly different than a typical subdivision. The roof choice remained the
standard, rather the roof with detention, due to its relatively high unit cost. Storage was
chosen when no other controls were feasible, the highest values, as expected, were in
commercial areas with rocky soils, which would not have much infiltration capacity.
The cost of the optimal solution for each soil class and land use is found in table 5.18.
The total cost for the controls would be $5.2 million, some of which overlaps with money
that would be spent for landscaping anyway. About half of this amount is used to attempt
to control runoff from transportation related functions.
What differs from a traditional subdivision development is the allocation of use. A
traditional subdivision would have allocated everything in ground cover to the high
quality grass, (particularly for commercial areas) and neglected the woods and aspens
(although some exceptions to this exist, mainly for aesthetics). In commercial areas, the
detention storage, would have been utilized. For sidewalks, patios, streets, and parking
lots, nonporous pavement would have been chosen. Curb and gutter would have replaced
swales along street rights of way.
An important note here is that this DSS cannot dynamically change land uses. For
example, the net amount of area used for rights of way, 39 acres out of the 106 total (see
table 5.18), must remain the same. Likewise, the amounts and locations for medium
density and low density, as well as the other land uses, must remain the same. What has
been done here, however, is to attempt to allocate storage optimally throughout each of
these land uses. A more general problem exists which would allow tradeoffs between the
land uses. This problem is extremely complex because it involves re-creation of the GIS
for each iteration.
59
-------
Table 5. 17: Resu
Its of LP o
Street 1
Street 2
Street 3
Street 4
Sidewalk 1
Sidewalk 2
Grass P
Grass F
Grass G
Swales 1
Swales 2
Storage
Parking 1
Parking 2
Parking 3
Parking 4
Roof 1
Roof 2
Driveway 1
Driveway 2
Patio 1
Patio 2
Woods F
Woods G
Aspen F
Aspen G
ptimization-land use allocation by function (includes opportunity costs)
Land Use Area in Soil Group B in acres
50
9.69
2.42
3.03
1.00
60
1.08
0.21
0.26
0.06
70
0.03
0.01
0.01
0.00
LD
11.23
2.34
0.94
0.47
0.25
MD
6.49
3.23
1.21
0.40
0.79
HD
0.57
0.04
1.72
1.08
0.36
Comm
0.16
0.14
2.04
1.03
Sch
0.65
0.24
1.19
1.59
Land Use Area in Soil Group D in acres
50
11.38
0.50
1.12
0.83
60
0.00
0.00
0.00
0.00
70
2.74
0.50
1.12
0.83
LD
0.00
0.00
0.00
0.00
0.00
0.00
MD
4.60
0.35
6.13
2.30
0.77
9.20
HD
0.00
0.00
0.00
0.00
0.00
Comm
0.38
2.15
1.32
5.97
Sch
0.00
0.00
0.00
0.00
0.00
60
-------
Table 5.18: Least-cost LP solutions for land Use/BMP options (including land costs) for Happy
Acres.
Land Use
50 ft ROW
60 ft ROW
70 ft ROW
Low Density Residential
Medium Density Residential
Apartments
Commercial
School
SUM
SoilB
Area (acres)
15.15
1.55
0.05
15.02
12.97
3.73
3.37
3.43
55.27
SoilD
Area (acres)
17.78
0.00
4.35
0.00
21.57
0.00
7.67
0.00
51.37
Total
(acres)
32.92
1.55
4.41
15.02
34.54
3.73
11.04
3.43
106.64
Land Use
50 ft ROW
60 ft ROW
70 ft ROW
Low Density Residential
Medium Density Residential
Apartments
Commercial
School
Cost in Soil B, $
$443,554
$36,463
$1,058
$376,677
$361,197
$98,633
$39,267
$106,305
$1,463,153
Cost in Soil D, $
$1,484,917
$-
$247,981
$-
$1,509,515
$-
$517,237
(D
J>-
$3,759,650
TOTAL
Sum, $
$1,928,471
$36,463
$249,039
$376,677
$1,870,712
$98,633
$556,503
$106,305
$5,222,803
$5,220,000
5.7 Decision Support Systems and the Happy Acres Case Study
The previous sections have illustrated how a simple hydrologic model can be constructed with
basic GIS information. The methods presented in this report allow hydrologic and economic
analysis to be performed on micro scales not traditionally used in urban analysis. These micro
scales, although unfamiliar, must be used to properly evaluate BMPs for the control of locally
generated stormwater runoff. This same information can be used as building blocks for SWMM.
SWMM aggregates information in a manner controlled by the user, into an equivalent
rectangular catchment. Several methods of aggregation are available within SWMM add-on
packages (such as PC SWMM). Unfortunately, this method homogenizes the parcels within each
subcatchment, i.e., they lose their unique hydrologic characteristics. The aggregation was
typically done so that the user was not overwhelmed by data, as most had to be handled
manually. However, within the context of a DSS, appropriate tools can be used to process the
data, so smaller scales may be evaluated.
A disadvantage of the DSS process used in this case study and outlined in figure 5.1 is that most
of the analysis is one way, i.e., there is not a true interchange of information between the
modules. The most obvious example is the GIS. It would be desirable to optimize land use in a
general form of a land allocation model considering the effects of land valuation, soils, and
control options. In order to do this efficiently, the spatial database underlying the parcel
delineation must be re-created for each iteration of the model. Of course, this level of integration
is also the most difficult and expensive.
61
-------
6.0 Summary and Conclusions
6.1 Summary
In summary, GIS has transformed our approach to the urban stormwater management problem.
Not only are input parameters in the model itself becoming more easily obtainable, but also the
scale of possible evaluations has decreased to a point that it is now possible to effectively
evaluate source controls. The case study process shown in figure 5.1 provides a preliminary
evaluation of the complex urban stormwater problem and the linked problem of allocation of
land use. Several models exist that utilize GIS information; the degree of integration that is
desirable remains debatable. Due to the widely disparate spatial scales involved, and the detailed
amount of information available in a GIS, it is quite possible for the analyst to be drowned in
data that may not be needed in evaluating the problem. The urban stormwater problem needs to
be of primary concern to the analyst; rather than the micro maintenance of the GIS. The problem
should be the primary focus, even more so than the model, or the database used. As the models
evolve into more general Decision Support Systems, they will tend to become more data
centered, and computational engines more interchangeable. The GIS data will become more
available and standardized, and will be an important tool. One lesson to be learned from the 90s
and the computer software explosion that has transformed the working world is that too much
reliance on any one technology can lead to obsolescence. DSS promises to be the technology
that links many of these tools together to enable the analyst to explore new challenging problems
in old contexts.
6.2 Conclusions
Advances in development of computer software have produced two key linked technologies:
relational databases and geographic information systems. The combination of these two has
affected the development of another technology, decision support systems, that has been applied
to complex unstructured water resources and environmental problems. Most DSSs include these
two technologies, with the addition of simulation models, an evaluation tool (can include
optimization), and a graphical user interface. The graphical user interface, mainly the MS-
Windows interface, is another advance that has both transformed software as well changed the
standard of model development. Construction of programs within this environment tends to be
more difficult due to its object oriented architecture, however, it is also inherently more dynamic
than constructing programs within older environments such as FORTRAN-77. This is primarily
due to the advent of structured programming techniques that tend to keep data handling
processes out of the main program files, which tends to advance a more data centric approach to
modeling. The structured techniques also avoid the use of "spaghetti code" in which it is
difficult to debug code due to vague loops and "GOTO" statements that branch the program in
many different directions.
New types of solvers are now available that can serve as better evaluation tools for a DSS.
These include genetic algorithms (GA), simulated annealing (SA), and the relative ease with
which linear programming (LP) solvers are used. These optimization tools allow rapid
evaluation of both linear and nonlinear problems, which can assist the designer in finding the
better or best solution.
62
-------
Urban stormwater models have been created according to specific needs and available funding.
The predominant US model, SWMM, was created in the late 60s and early 70s. There is an
active user community for this largely public domain model. Several enhancements to the
model, namely PC SWMM, Visual Hydro (XP SWMM), and MikeSWMM, are now available in
the private domain as well. These enhancements contain facilities that include graphical user
interfaces for ease of program use, GIS and CAD interfaces for construction of models based
upon the best available system mapping, and external links to available databases to enhance the
use of available system data. European models, in particular the DHI and the HR-Wallingford
series, have been significantly ahead of the US modeling community in the use of GUIs and GIS.
The reason for this gap is primarily the result of funding. Funding for urban stormwater
modeling in the US ceased in the early 80s. Meanwhile, the European models were developed
and enjoyed significant funding during the 80s and early 90s from both national governments as
well as the European Union. These models may have become self-supporting by the creation of
companies that sell the licensed product. This enables future enhancements in the models to be
made, as well as user support from a centralized source.
The US should focus its efforts on the use of linked technologies to take advantage of significant
savings that can be realized by avoiding the re-creation of common tools currently available. For
example, spreadsheet technology in the US has been effectively standardized upon MS Excel
(even if you don't use it, you use a program that can read these files). Input and output
processing within new models could make use of this application, which would allow the user
greater flexibility in terms of pre- and post-processing of model output. Visual Hydro provides a
good example of the use of spreadsheet tools for data input and output. The US has been a
leader in the GIS and database software development field; available links to these programs will
continue to evolve and interfaces with GIS should become easier to construct than those at
present. A significant portion of this effort is the development of both the graphic features of the
GIS and the associated system attributes as well. The case study outlined in this report, although
using a simplified hydrologic model, provides a possible outline of the use of this data for
problems that have remained intractable to this point, for example, the selection of the
appropriate BMP control technology for each parcel. Further work needs to be done to enhance
the development of DSS technology to the urban stormwater field. The funding resources should
carefully target the development of models and DSSs that link available tools rather than re-
create them, and provide a common set of technologies that the user may combine with other
available software. The funding should also seek to complement or prod the development of
existing commercial software, rather than supplant the market by the introduction of competing
products. A possible model could also be the European model community, in which the
government funds the initial development of the model, then licenses it to a nonprofit company
that markets and sells the model at a self-sustaining price.
Care should be taken in that as the model interfaces become easier to run, they may be used
inappropriately. A stated goal within the DSS community is to bring the computing power to the
level of the decision-maker, rather than an intermediary. This works well if the decision-maker,
or their assistant, is trained in the field of urban stormwater. The field of urban stormwater
modeling involves the use of complex boundary conditions. Using GIS involves the use of
wildly different scales where the uncertainty in the information may not be immediately evident
63
-------
to the user. Such complex problems require a technically competent professional to carefully use
and evaluate the information the DSS presents. Rather than simply using a sophisticated set of
tools to solve the same problem more efficiently than we can at present, the problems evaluated
will become more complex as well as the possible array of solutions to them. The advent of DSS
and its inherent technologies, relational databases and GIS, have transformed the field of urban
stormwater modeling and allow the evaluation of previously intractable problems.
64
-------
7.0 References
Azzout, Y., Barraud, S., Cres, F. N., and Alfakih, E. (1995) Decision Aids for Alternative
Techniques in Urban Storm Management, Water Science and Technology, 32 (1):
41-48.
Barbe, D.E., Miller, H., and Jalla, S. (1993) Development of a Computer Interface among
GDS, SCADA and SWMM for Use in Urban Runoff Simulation. In Harlin, J.M
and Lanfear, KJ. (eds.) Proc. of the Symposium on Geographic Information
Systems and Water Resources. American Water Resources Association,
Bethesda, MD. p. 113-120.
Battin, A., Kinerson, R., and Lahlou, M. (1998) EPA's Better Assessment Science
Integrating Point andNonpoint Sources (BASINS)-A Powerful Tool for Managing
Watersheds. Internet file retrieved 11/6/98 from The Center for Research in
Water Resources, The University of Texas at Austin.
http://www.crwr.utexas.edu/gis/gishyd98/epa/battin/p447.htm.
Belial, M., Sillen, X., and Zech, Y. (1996) Coupling GIS with a Distributed Hydrological
Model for Studying the Effect of Various Urban Planning Options on Rainfall-
Runoff Relationships in Urbanized Watersheds. In Kovar, K., and Nachtnebel,
H.P. (eds.) HydroGIS '96: Application of Geographic Information Systems in
Hydrology and Water Resources Management. International Association of
Hydrologic Sciences Publication No. 235. IAHS Press, Wallingford, UK. p. 99-
106.
Brown and Caldwell and Camp, Dresser and McKee (1997) Boulder Creek Watershed
Study, Phase I, November, 1997. Prepared for the City of Boulder.
Butler, D. and Maksimovic, C. (eds.) (1998) UDM '98 Fourth International Conf. on
Developments in Urban Drainage Modeling. Imperial College of Science,
Technology & Medicine, London, UK.
CAiCHE (1998) Visual Hydro Software, Tampa, FL.
Charnock, T.W., Hedges, P.O., and Elgy, J. (1996) Linking multiple process models with
GIS. In Kovar, K., and Nachtnebel, H.P. (eds.) HydroGIS '96: Application of
Geographic Information Systems in Hydrology and Water Resources
Management. International Association of Hydrologic Sciences Publication No.
235. IAHS Press, Wallingford, UK. p. 29-36.
Cluis, D., Martz, L., Quentin, E., and Rechatin, C. (1996) Coupling GIS and DEM to
Classify the Hortonian Pathways of Non-point Sources to the Hydrologic
Network. In Kovar, K., and Nachtnebel, H.P. (eds.) HydroGIS '96: Application
of Geographic Information Systems in Hydrology and Water Resources
Management. International Association of Hydrologic Sciences Publication No.
235. IAHS Press, Wallingford, UK. p. 37-44.
Computational Hydraulics International (CHI) (1998) PCSWMM, PCSWMM GIS
Software, Guelph, Ontario, Canada.
da Costa, J.R, Lacerda, M., and Jesus, H.B. (1995) The Portuguese Water Resources
-------
Information System: Using OOP to Integrate Time Series and GIS. Proc. of
1995 ESRI User Conference. Internet file retrieved from
http ://www. esri. com/library/userconf/ proc95/to3 00/p296 .html
da Costa, J.R., Jesus, H.B., and Lacerida, M. (1996) Integrating GIS and Time Series
Analysis for Water Resources Management in Portugal. In Kovar, K., and
Nachtnebel, H.P. (eds.) HydroGIS '96: Application of Geographic Information
Systems in Hydrology and Water Resources Management. International
Association of Hydrologic Sciences Publication No. 235. IAHS Press,
Wallingford, UK. p. 289-297.
Davis, J. R., Nanninga, P. M., Biggins, J., and Laut, P. (1991) Prototype Decision
Support System for Analyzing Impact of Catchment Policies, Journal of Water
Resources Planning and Management, 111 (4): 399-414.
DeVantier, B.A. and Feldman, A.D. (1993) Review of GIS Applications in Hydrologic
Modeling, Journal of Water Resources Planning and Management, 119 (2): 246-
261.
Dewberry and Davis (1996) Land Development Handbook. McGraw-Hill, New York.
DHI, Inc. (1998) Mouse, Mike, MikeSHE, NAM, WUS, Mikelnfo, MikeSWMM
Software, Copenhagen, Denmark.
Dion, T.R. (1993) Land Development for Civil Engineers. Wiley-Interscience, New York.
Driscoll, E. D. (1993) A Decision Support System for Highway Runoff Monograph: in:
James, W., (Ed.) New Techniques for Modeling of Stormwater Quality Impacts, p.
101-122, Guelph, ON.
ESRI (1998) Internet page: http://www.esri.com.. Redlands, CA.
Fankhauser, R. (1998) Automatic Determination of Imperviousness in Urban Areas from
Digital Orthophotos. In UDM '98, the Fourth International Conference on
Developments in Urban Drainage Modeling, Butler, D. and Maksimovic, C.
(Eds.) 21-24 September, 1998, London, UK, IAWQ/IAHR, pages 321-326.
Federal Highway Administration (1996) Urban Drainage Design Manual, HEC No. 22,
FHWA-SA-96-078, Washington, D.C.
Fedra, K. and Jamieson, D. G. (1996) The 'Water Ware' Decision-Support System for
River-Basin Planning. 2. Planning Capability, Journal of Hydrology, 177(3/4),
p. 177-198.
Feinberg, D., and Uhrick., S.W. (1997) Integrating GIS with Water and Wastewater
Hydraulic Models. Proc. of 1997 ESRI User Conference. Internet file retrieved
from http://www.esri.com/library/userconf/proc97/PROC97/TO200/
PAP199/P199.HTM
Fuchs, L., and Scheffer, C. (1996) Hydroinformatic Tools in Urban Drainage. In Seiker,
F., and Verworn, H.R. (eds.). IAHR/IAWQ Proc. of the 7th Annual Conf. on
Urban Storm Drainage. Hannover, Germany. Vols. Ill, p. 1587-1592.
Fulp, T., Harkins, J., Williams, B., Vickers, B., King, D., Martin, K., Shiao, L (1994)
-------
Decision Support for Water Resources Management in the Colorado River,
United States Bureau of Reclamation; Boulder City, NV.
Greene, R.G. and Cruise, J.F. (1995) Urban Watershed Modeling Using Geographic
Information System, Journal of Water Resources Planning and Management, 121
(4): 318-325.
Gregory, M. and James, W. (1995) Management of Time-Series Data for Long-Term,
Continuous Stormwater Modeling. Chapter 8 in James, W. (Ed.) Advances in
Modeling the Management of Stormwater Impacts, Computational Hydraulics
International, Guelph, Ontario, Canada.
Hallam, C.A., Salisbury, J.M., Lanfear, K.J., and Battaglin, W.A. (eds.) (1996) Proc. of
the A WRA Annual Symposium: GIS and Water Resources. American Water
Resources Association, Herndon, VA.
Harlin, J.M and Lanfear, K.J. (eds.) (1993) Proc. of the Symposium on Geographic
Information Systems and Water Resources. American Water Resources
Association, Bethesda, MD.
Hauber, S.M. and Joeres, E.F. (1996) Using a GIS for Estimating Input Parameters in
Urban Stormwater Quality Modeling, Water Resources Bulletin 32(6), p. 1341-
1352.
Heaney, J. P., Wright, L., and Sample, D. (1998) Research Needs in Wet Weather Flows,
Final Report of Project 96-IRM-l, Water Environment Federation, Alexandria,
VA.
Heaney, J.P., Sample, D., and Wright, L. (1999a) Costs of Urban Stormwater Systems,
Report to the US EPA, Edison, NJ.
Heaney, J.P., Wright, L., and Sample, D. (1999b) Innovative Methods for Optimization
of Urban Stormwater Systems, Report to the US EPA, Edison, NJ.
Hell weger, F. (1996) TABHYD-TR55 Tabular Hydrograph Method in Arc View. Internet
file retrieved from The Center for Research in Water Resources, The University
of Texas at Austin.
http://www.crwr.utexas.edu/gis/gishyd98/runoff/webfiles/TABHYD/
TABHYD.HTM
Hellweger, F. and Maidment, D. (1999) Definition and Connection of Hydrologic
Elements using Geographic Data, Journal of Hydrologic Engineering, 4 (1): 10-
18.
Herath, S., Musiake, K., and Hironaka, S. (1996) Development and Application of a
GIS Based Distributed Catchment Model for Urban Areas. In Seiker, F., and
Verworn, H.R. (eds.) IAHR/IAWQ Proc. of the 7th Annual Conf. on Urban Storm
Drainage. Hannover, Germany. Vols. Ill, p. 1695-1700.
Hora, J., Kuby, R., and Suchanek, M. (1998) Information Technologies and
Hydroinformatic Tools in the Town of Pilsen. In Butler, D. and Maksimovic, C.
(eds.) UDM '98 Fourth International Conf. on Developments in Urban Drainage
Modeling. Imperial College of Science, Technology & Medicine, London, UK.
-------
p. 481-486.
HR-Wallingford, Inc. (1998) Hydroworks, Infoworks Software, Wallingford,
Oxfordshire, UK.
Huber, W.C. and Dickinson, R.E. (1988) Storm Water Management Model User's
Manual, Version 4, EPA/600/3-88/00la (NTIS PB88-236641/AS), Environmental
Protection Agency, Athens, GA, 1988.
Jamieson, D. G. and Fedra, K. (1996a) The 'Water Ware' Decision-Support System for
River-Basin Planning. 1. Conceptual Design, Journal of Hydrology, 111 (3/4), p.
163-175.
Jamieson, D. G. and Fedra, K. (1996b) The 'Water Ware' Decision-Support System for
River-Basin Planning. 3. Example Applications, Journal of Hydrology, 111
(3/4), p. 199-211.
Kim, H.-B., Kim, K.-M., and Lee, J.-C. (1998) Sewer Alternative Generation Using GIS
and Simulation Models in a Planning Support System. Internet file retrieved from
the ESRI1998 International User Conference, http://www.esri.com/library/
userconf/proc98/PROCEED/
Kopp, S.M. (1996) Linking GIS and Hydrologic Models: Where Have We Been,
Where Are We Going? In Kovar, K., and Nachtnebel, H.P. (eds.) HydroGIS '96:
Application of Geographic Information Systems in Hydrology and Water
Resources Management. International Association of Hydrologic Sciences
Publication No. 235. IAHS Press, Wallingford, UK. p. 133-139.
Kovar, K., and Nachtnebel, H.P. (eds.) (1996) HydroGIS '96: Application of Geographic
Information Systems in Hydrology and Water Resources Management.
International Association of Hydrologic Sciences Publication No. 235. IAHS
Press, Wallingford, UK.
Lavallee, P., Marcoux, C., Bonin, R., (1996) Performance of an Integrated Real Time
Control System: Application to CSOs Control, in WEF Urban Wet Weather
Pollution, Controlling Sewer Overflows and Stormwater Runoff, p. 12.13-12-22,
Alexandria, VA.
Litman, T. (1998) Transportation Cost Analysis: Techniques, Estimates and Implications.
Victoria Transport Policy Institute, Victoria, British Columbia.
Makropoulos, C., Butler, D., and Maksimovic, C. (1998) A GIS Based Methodology for
the Evaluation of Suitability of Urban Areas for Source Control Application. In
Butler, D. and Maksimovic, C. (eds.) UDM '98 Fourth International Conf. on
Developments in Urban Drainage Modeling. Imperial College of Science,
Technology & Medicine, London, UK. p. 59-66.
Mark, O., van Kalken' T., Rabbi, K., and Kjelds, J. (1997) A Mouse GIS Study of the
Drainage in Dhaka City. Proc. of the 1997 ESRI User Conference. Internet file
retrieved from http://www.esri.com/library/userconf/proc97/
PROC97/TO500/PAP487/P487.HTM
Mercado, R.M. (1996) Geographic Information Systems (GIS) and Image Processing for
-------
Stormwater Management Modeling Using XPSWMM in Tallahassee, Florida. In
Hallam, C.A., Salisbury, J.M., Lanfear, K.J., and Battaglin, W.A. (eds.) Proc. of
the A WRA Annual Symposium: GIS and Water Resources. American Water
Resources Association, Herndon, VA. p. 305-313.
Meyer, S.P., Salem, T.H., and Labadie, J.W. (1993) Geographic Information Systems in
Urban Storm-Water Management, Journal of Water Resources Planning and
Management, 119 (2): 206-228.
Miles, S.W. and Heaney, J.P. (1988) Better than "Optimal" Method for Designing
Drainage Systems. Journal of Water Resources Planning and Management,
114(5): 477-499.
Olivera, F, Reed, S., and Maidment, D. (1998) HEC-PrePro v.2.0: An ArcView Pre-
Processor for HEC's Hydrologic Modeling System. 1998 ESRI User's
Conference, San Diego, CA.
http://www.esri.com/library/userconf/proc98/PROCEED.
HTM
Olivera, F., Maidment, D.R., and Charbeneau, RJ. (1996) Spatially Distributed Modeling
of Storm Runoff and Non-Point Source Pollution Using Geographic Information
Systems (GIS). CRWR Online Report 96-4. Internet file retrieved from The
Center for Research in Water Resources, The University of Texas at Austin.
http://www.crwr.utexas.edu/gis/ gishyd98/library/olivera/ header.htm
Palisade Corporation (1998) @Risk, Evolver Software, Newfield, NY.
Prince Georges County (1997) Low-Impact Development Design Manual, Prince Georges
County, Largo, MD, November, 1997.
Pryl, K., Vanecek, S., and Vasek, P. (1998) Data Processing and Manipulation Tools
Used for Urban Drainage Systems. In Butler, D. and Maksimovic, C. (eds.) UDM
'98 Fourth International Conf. on Developments in Urban Drainage Modeling.
Imperial College of Science, Technology & Medicine, London, UK. p. 327- 332.
R.S. Means (1996a) Heavy Construction Cost Data, 10th Annual Edition, R.S. Means
Company, Inc., Kingston, MA.
R.S. Means (1996b) Landscaping Unit Cost Data, R.S. Means Company, Inc., Kingston,
MA.
Refsgaard, J.C., Storm, B. and Refsgaard, A. (1995) Recent Developments of the
Systeme Hydrologique Europeen (SHE) Towards the MIKE SHE. In Simonovic,
S., Kundzewicz, Z., Rosbjerg, D., and Takeuchi, K. (eds.) Modeling and
Management of Sustainable Basin-Scale Water Resource Systems. International
Hydrologic Sciences Publication No. 231. IAHS Press, Wallingford, UK. p.
427-434.
Reitsma, R. F. (1996) Structure and Support of Water-Resource Management and
Decision-Making, Journal of Hydrology, 111 (3/4), p. 253-268.
Reitsma, R. F., Zagona, E.A., Chapra, S.C., and Strzepek, K.M. (1996) Decision Support
Systems (DSS) for Water Resources Management, chapter 33 in Mays, L.W.
-------
(Ed.) Water Resources Handbook, McGraw-Hill, New York, NY.
Ribeiro, C.T. (1996) Impact of Land Use on Water Resources: Integrating HSPF and a
Raster-Vector GIS. In Kovar, K., and Nachtnebel, H.P. (eds.) HydroGIS '96:
Application of Geographic Information Systems in Hydrology and Water
Resources Management. International Association of Hydrologic Sciences
Publication No. 235. IAHS Press, Wallingford, UK. p. 349-356.
Riley, A. (1998) Restoring Streams in Cities, A Guide for Planners, Policymakers, and
Citizens. Island Press,
Rodriguez, F., Andrieu, H., Creutin, J.D., and Raimbault, G. (1998) Relevance of
Geographic Information Systems for Urban Hydrological Analysis. In Butler, D.
and Maksimovic, C. (eds.) UDM '98 Fourth International Conf. on Developments
in Urban Drainage Modeling. Imperial College of Science, Technology &
Medicine, London, UK. p. 333-340.
Scarborough, R.W., and Yetter, C.H. (1998) A Case Study Evaluation of the
ArcView/Non-point Source Module Interface in EPA's BASINS 2.0 Watershed
Model. Internet file retrieved from the 1998 ESRIInternational User's
Conference, http://www.esri.com/library/
userconf/proc98/PROCEED/ABSTRACT/A247.HTM
Seiker, F., and Verworn, H.R. (eds.) (1996) IAHR/IAWQ Proc. of the 7th Annual Conf. on
Urban Storm Drainage. Hannover, Germany. Vols. I, II, and III.
Shamsi, U. M., Benner, S. P., and Fletcher, B. A. (1995) A Computer Mapping Program
for Sewer Systems, chapter 7 in Advances in Modeling the Management of
Stormwater Impacts, CHI: Guelph, Canada, W. James, Editor, 1995.
Shamsi, U.M. (1997) SWMM Graphics. Chapter 7 in James, W. (Ed.), Advances in
Modeling the Management ofStormwater Impacts, Volume 5. Computational
Hydraulics International, Guelph, Ontario, p. 129-153.
Shamsi, U.M. (1998) Arc View Applications in SWMM Modeling. Chapter 11 in James,
W. (ed.) Advances in Modeling the Management of Stormwater Impacts, Volume
6. Computational Hydraulics International, Guelph, Ontario, Canada, p. 219-
233.
Shamsi, U.M., and Fletcher, B.A. (1996) Arcview Applications in Stormwater and
Wastewater Management. In Hallam, C.A., Salisbury, J.M., Lanfear, K.J., and
Battaglin, W.A. (eds.) Proc. of the AWRA Annual Symposium: GIS and Water
Resources. American Water Resources Association, Herndon, VA. p. 259-268.
Simonovic, S., Kundzewicz, Z., Rosbjerg, D., and Takeuchi, K. (eds.) (1995)Modeling
and Management of Sustainable Basin-Scale Water Resource Systems.
International Hydrologic Sciences Publication No. 231. IAHS Press,
Wallingford, UK.
Singh, V. P. and Fiorentino, M. (1996) Geographical Information Systems in Hydrology,
Kluwer Academic Publishers, Boston, MA.
Sorensen, H.R., Kjelds, J.T., Deckers, F., and Waardenburg, F. (1996) Application of
-------
GIS in Hydrological and Hydraulic Modeling: DLIS and MIKE11-GIS. In
Kovar, K., and Nachtnebel, H.P. (eds.) HydroGIS '96: Application of
Geographic Information Systems in Hydrology and Water Resources
Management. International Association of Hydrologic Sciences Publication No.
235. IAHS Press, Wallingford, UK. p. 149-156.
Sotic, A., Despotovic, J., Petrovic, J., Babic, B., Djukie, A., and Prodanovioc, D. (1998)
Hydroinformatic Approach in Sewer System Design-Kumodraz case study. In
Butler, D. and Maksimovic, C. (eds.) UDM '98 Fourth International Conf. on
Developments in Urban Drainage Modeling. Imperial College of Science,
Technology & Medicine, London, UK. p. 341-346.
Sponemann, P., Beeneken, L., Fuchs, L., Prodanovic, D., and Schneider, S. (1996)
Criteria for Geographic Information Systems Used in Urban Drainage. In Seiker,
F., and Verworn, H.R. (eds.) (1996) IAHR/IAWQ Proc. of the 7th Annual Conf.
on Urban Storm Drainage. Hannover, Germany. Vols. III. p. 1689-1694.
Tchobanoglous, G. (1981) Wastewater Engineering: Collection and Pumping of
Wastewater, McGraw-Hill, New York, New York.
Tskhai, A., Shirokova, S., Konev, D., Koshelev, K., and Tskhai, T. (1995) GIS
"Hydromonitoring" and Optimization Model of Enterprise Water Protection
Activity. In Simonovic, S., Kundzewicz, Z., Rosbjerg, D., and Takeuchi, K.
(eds.) Modeling and Management of Sustainable Basin-Scale Water Resource
Systems. International Hydrologic Sciences Publication No. 231. IAHS Press,
Wallingford, UK. p. 263-270.
University of Texas (1998) Internet page: http://www. crwr.utexas.edu/ The Center for
Research in Water Resources.
Urban Land Institute (1989) Project Infrastructure Development Handbook. Urban Land
Institute, Washington, D.C.
VanGelder, P., and Miller, M. (1996) GIS as an Aid in the Evaluation of Drainage
Facilities at the Albany County Airport, New York. In Hallam, C. A., Salisbury,
J.M., Lanfear, K.J., and Battaglin, W.A. (eds.) Proc. of'the AWRA Annual
Symposium: GIS and Water Resources. American Water Resources Association,
Herndon, VA. p. 295-303.
Walsh, M.R. (1993) Toward Spatial Decision Support Systems in Water Resources,
Journal of Water Resources Planning and Management, 119(2): 158-169.
Wang, Y, and James, W. (1997) Integration of US Army Corps of Engineers' Time-
Series Data Management System with Continuous SWMM Modeling. Chapter 2
in James, W. (Ed.) Advances in Modeling the Management ofStormwater
Impacts, volume 5, Computational Hydraulics International, Guelph, Ontario,
Canada.
Wolf-Schumann, U., and Vaillant, S. (1996) Time View: A Times Series Management
System for GIS and Hydrologic Systems. In Kovar, K., and Nachtnebel, H.P.
(eds.) HydroGIS '96: Application of Geographic Information Systems in
Hydrology and Water Resources Management. International Association of
-------
Hydrologic Sciences Publication No. 235. IAHS Press, Wallingford, UK. p. 79-
87.
Wright, L.T., Nix, S.J., Hassett, J.M. and Moffa, P.E. (1995) A Preliminary Urban Non-
point Source Management Plan: A Modeling Approach. Chapter 4 in James, W.
(ed.), Modern Methods for Modeling the Management ofStormwater Impacts.
Computational Hydraulics International, Guelph, Ontario, p. 51-62.
Xu, Z.X., Schultz, G.A., and Ito, K. (1998) GIS Application in a Watershed-Based Water
Resources Management. In Butler, D. and Maksimovic, C. (eds.) UDM '98
Fourth International Conf. on Developments in Urban Drainage Modeling.
Imperial College of Science, Technology & Medicine, London, UK. p. 487-494.
Xue, R.Z., and Bechtel, TJ. (1997) Integration of Stormwater Runoff and Pollutant
Model with BMP Assessment Model Using Arc View GIS. Proc. of 1997 ESRI
User Conference. Internet file retrieved 11/6/98 from
http://www.esri.com/library/userconf/proc97/
PROC97/TO700/PAP656/P656.HTM
Xue, R.Z., Bechtel, T.J., and Chen, Z. (1996) Developing a User-Friendly Tool for BMP
Assessment Model Using a GIS. In Hallam, C.A., Salisbury, J.M., Lanfear, K.J.,
and Battaglin, W.A. (eds.) Proc. of the AWRA Annual Symposium: GIS and
Water Resources. American Water Resources Association, Herndon, VA. p. 285-
294.
-------
Appendix: Happy Acres Database
Table A-l: Parcel attributes
Ad-
dress
100
101
200
200
201
100
200
201
105
110
120
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
123
125
127
129
100
101
102
106
108
110
120
121
130
131
140
141
150
151
160
161
170
171
180
181
190
191
151
160
161
165
Street
Alpine Street
Alpine Street
Cedar Street
Ashmount Street
Ashmount Street
Highland Street
Birch Avenue
Birch Avenue
Center Street
Center Street
Center Street
Maple Street
Maple Street
Maple Street
Maple Street
Maple Street
Maple Street
Maple Street
Maple Street
Maple Street
Maple Street
Maple Street
Maple Street
Maple Street
Maple Street
Maple Street
Maple Street
Maple Street
Maple Street
Maple Street
Maple Street
Maple Street
Maple Street
Maple Street
Maple Street
Maple Street
Maple Street
Oak Street
Oak Street
Oak Street
Oak Street
Oak Street
Oak Street
Oak Street
Oak Street
Oak Street
Oak Street
Oak Street
Oak Street
Oak Street
Oak Street
Oak Street
Oak Street
Oak Street
Oak Street
Oak Street
Oak Street
Oak Street
Oak Street
Acorn Street
Acorn Street
Acorn Street
Acorn Street
Soil
Silt
Silt
Clay
Rock
Rock
Rock
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Clay
Clay
Clay
Clay
Land Use
Apartments
Apartments
Commercial
Commercial
Commercial
Commercial
Commercial
Commercial
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
LD Residential
MD Residential
MD Residential
MD Residential
MD Residential
Area
SF
50320
112360
25957
154915
72968
80450
100139
46642
14235
18488
6844
15082
9927
11751
9742
11025
8744
11441
7667
12942
11518
11728
7707
12053
14291
17653
8015
13857
13778
11207
18674
15565
13029
14017
16758
19500
22449
14049
10172
11049
11131
11239
11681
11993
12611
12127
12680
12646
12749
13048
12818
12950
12886
13016
12955
13412
13618
14363
11552
6019
5286
3926
3853
Roof
SF
0
46927
0
57707
0
0
95132
0
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
1600
1600
1600
1600
Parking
SF
37740
37343
24659
89462
69319
76427
0
44810
Drive-ways, SF
0
0
0
0
0
0
0
0
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
600
600
600
600
Patios
SF
0
0
0
0
0
0
0
0
400
400
400
400
400
400
400
400
400
400
400
400
400
400
400
400
400
400
400
400
400
400
400
400
400
400
400
400
400
400
400
400
400
400
400
400
400
400
400
400
400
400
400
400
400
400
400
400
400
400
400
200
200
200
200
Imperv-
ious, SF
37740
84270
24659
147169
69319
76427
95132
44810
3200
3200
3200
3200
3200
3200
3200
3200
3200
3200
3200
3200
3200
3200
3200
3200
3200
3200
3200
3200
3200
3200
3200
3200
3200
3200
3200
3200
3200
3200
3200
3200
3200
3200
3200
3200
3200
3200
3200
3200
3200
3200
3200
3200
3200
3200
3200
3200
3200
3200
3200
2400
2400
2400
2400
Pervious
SF
12580
28090
1298
7746
3648
4022
5007
1832
11035
15288
3644
11882
6727
8551
6542
7825
5544
8241
4467
9742
8318
8528
4507
8853
11091
14453
4815
10657
10578
8007
15474
12365
9829
10817
13558
16300
19249
10849
6972
7849
7931
8039
8481
8793
9411
8927
9480
9446
9549
9848
9618
9750
9686
9816
9755
10212
10418
11163
8352
3619
2886
1526
1453
-------
Ad-
dress
170
171
176
179
180
181
182
100
101
110
111
120
121
131
135
139
141
150
151
160
161
170
171
180
181
190
191
100
101
111
121
131
141
150
151
154
155
161
165
166
170
171
180
181
190
191
100
101
110
111
112
116
120
121
131
141
151
161
180
190
101
111
121
131
141
181
191
100
110
120
130
Street
Acorn Street
Acorn Street
Acorn Street
Acorn Street
Acorn Street
Acorn Street
Acorn Street
Ash Street
Ash Street
Ash Street
Ash Street
Ash Street
Ash Street
Ash Street
Ash Street
Ash Street
Ash Street
Ash Street
Ash Street
Ash Street
Ash Street
Ash Street
Ash Street
Ash Street
Ash Street
Ash Street
Ash Street
Ash-Acorn Connec
Ash-Acorn Connec
Ash-Acorn Connec
Ash-Acorn Connec
Ash-Acorn Connec
Ash-Acorn Connec
Ash-Acorn Connec
Ash-Acorn Connec
Ash-Acorn Connec
Ash-Acorn Connec
Ash-Acorn Connec
Ash-Acorn Connec
Ash-Acorn Connec
Ash-Acorn Connec
Ash-Acorn Connec
Ash-Acorn Connec
Ash-Acorn Connec
Ash-Acorn Connec
Ash-Acorn Connec
Birch Avenue
Birch Avenue
Birch Avenue
Birch Avenue
Birch Avenue
Birch Avenue
Birch Avenue
Birch Avenue
Birch Avenue
Birch Avenue
Birch Avenue
Birch Avenue
Birch Avenue
Birch Avenue
Cedar Street
Cedar Street
Cedar Street
Cedar Street
Cedar Street
Cedar Street
Cedar Street
Elm Street
Elm Street
Elm Street
Elm Street
Soil
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Land Use
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
Area
SF
5543
3926
5800
3926
4788
3926
4783
5750
6785
6600
6765
6620
6744
6724
6703
6683
6662
3919
6642
4481
6621
4763
6601
4878
6581
4326
6560
3127
3180
3039
3157
2994
3086
4739
3157
5648
3109
3089
3149
5648
4630
3349
4818
2948
4551
2686
6469
6554
6477
6522
6484
6492
6499
6490
6457
6425
6360
6328
6560
6568
6572
6580
6588
6595
6603
6663
6671
6481
6448
6416
6384
Roof
SF
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
Parking
SF
Drive-ways, SF
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
Patios
SF
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
Imperv-
ious, SF
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
Pervious
SF
3143
1526
3400
1526
2388
1526
2383
3350
4385
4200
4365
4220
4344
4324
4303
4283
4262
1519
4242
2081
4221
2363
4201
2478
4181
1926
4160
727
780
639
757
594
686
2339
757
3248
709
689
749
3248
2230
949
2418
548
2151
286
4069
4154
4077
4122
4084
4092
4099
4090
4057
4025
3960
3928
4160
4168
4172
4180
4188
4195
4203
4263
4271
4081
4048
4016
3984
-------
Ad-
dress
140
150
160
170
106
101
111
120
140
141
150
151
100
101
120
121
141
161
100
101
121
140
100
101
120
141
100
101
120
141
101
100
101
110
111
120
121
130
131
140
141
150
151
156
160
161
165
166
170
171
180
181
190
191
193
101
110
120
130
140
150
156
158
160
170
180
190
161
130
170
190
Street
Elm Street
Elm Street
Elm Street
Elm Street
Forest Avenue
Main Street
Main Street
Main Street
Main Street
Main Street
Main Street
Main Street
Street A
Street A
Street A
Street A
Street A
Street A
Street B
Street B
Street B
Street B
Street C
Street C
Street C
Street C
Street D
Street D
Street D
Street D
Street E
Sycamore Street
Sycamore Street
Sycamore Street
Sycamore Street
Sycamore Street
Sycamore Street
Sycamore Street
Sycamore Street
Sycamore Street
Sycamore Street
Sycamore Street
Sycamore Street
Sycamore Street
Sycamore Street
Sycamore Street
Sycamore Street
Sycamore Street
Sycamore Street
Sycamore Street
Sycamore Street
Sycamore Street
Sycamore Street
Sycamore Street
Sycamore Street
Ashmount Street
Ashmount Street
Ashmount Street
Ashmount Street
Ashmount Street
Ashmount Street
Ashmount Street
Ashmount Street
Ashmount Street
Ashmount Street
Ashmount Street
Ashmount Street
Main Street
Street A
Street A
Street A
Soil
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Clay
Rock
Rock
Rock
Rock
Rock
Rock
Rock
Rock
Rock
Rock
Rock
Rock
Rock
Rock
Rock
Rock
Land Use
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
Area
SF
6351
6319
6286
6254
6428
4993
5154
6770
6636
6323
4939
6323
5072
4644
5072
4789
4934
5079
4787
4953
4953
4787
5609
4737
5609
4888
5254
5461
5254
5461
5192
6480
6511
6460
6712
6439
6470
6419
6492
6399
6514
6378
6536
6358
6337
6558
6580
6317
6296
5931
6276
5744
6255
6274
5919
6649
5611
5524
6461
6805
6624
6875
6554
6693
6533
6461
5691
6323
5072
5072
5072
Roof
SF
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
Parking
SF
Drive-ways, SF
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
Patios
SF
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
Imperv-
ious, SF
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
Pervious
SF
3951
3919
3886
3854
4028
2593
2754
4370
4236
3923
2539
3923
2672
2244
2672
2389
2534
2679
2387
2553
2553
2387
3209
2337
3209
2488
2854
3061
2854
3061
2792
4080
4111
4060
4312
4039
4070
4019
4092
3999
4114
3978
4136
3958
3937
4158
4180
3917
3896
3531
3876
3344
3855
3874
3519
4249
3211
3124
4061
4405
4224
4475
4154
4293
4133
4061
3291
3923
2672
2672
2672
-------
Ad-
dress
141
160
180
181
190
191
160
161
171
190
191
180
181
190
191
100
120
151
171
190
191
126
130
136
140
150
160
170
171
181
191
193
151
155
161
165
171
175
179
101
111
121
131
141
151
176
180
181
190
191
193
195
201
221
231
241
244
250
251
254
260
261
270
274
280
281
290
291
100
101
110
Street
Street B
Street B
Street B
Street B
Street B
Street B
Street C
Street C
Street C
Street C
Street C
Street D
Street D
Street D
Street D
Street E
Street E
Street E
Street E
Street E
Street E
Birch Avenue
Birch Avenue
Birch Avenue
Birch Avenue
Birch Avenue
Birch Avenue
Birch Avenue
Birch Avenue
Birch Avenue
Birch Avenue
Birch Avenue
Cedar Street
Cedar Street
Cedar Street
Cedar Street
Cedar Street
Cedar Street
Cedar Street
Elm Street
Elm Street
Elm Street
Elm Street
Elm Street
Elm Street
Elm Street
Elm Street
Elm Street
Elm Street
Elm Street
Elm Street
Elm Street
Elm Street
Elm Street
Elm Street
Elm Street
Elm Street
Elm Street
Elm Street
Elm Street
Elm Street
Elm Street
Elm Street
Elm Street
Elm Street
Elm Street
Elm Street
Elm Street
Forest Avenue
Forest Avenue
Forest Avenue
Soil
Rock
Rock
Rock
Rock
Rock
Rock
Rock
Rock
Rock
Rock
Rock
Rock
Rock
Rock
Rock
Rock
Rock
Rock
Rock
Rock
Rock
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Land Use
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
Area
SF
4953
4787
4787
4953
4787
4953
5609
5039
5189
5609
5340
5254
5461
5254
5461
6520
6520
5363
5533
6520
5704
6507
6515
6522
6530
6537
6545
6552
6345
6939
7911
5095
6610
6618
6625
6633
6641
6648
6656
6663
6667
6671
6676
6680
6684
6070
6675
6688
6941
6693
4843
4131
6416
6106
6452
6627
6706
6894
6665
6256
6865
6682
6463
6886
6909
6699
6765
6716
6312
7572
6424
Roof
SF
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
Parking
SF
Drive-ways, SF
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
Patios
SF
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
Imperv-
ious, SF
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
Pervious
SF
2553
2387
2387
2553
2387
2553
3209
2639
2789
3209
2940
2854
3061
2854
3061
4120
4120
2963
3133
4120
3304
4107
4115
4122
4130
4137
4145
4152
3945
4539
5511
2695
4210
4218
4225
4233
4241
4248
4256
4263
4267
4271
4276
4280
4284
3670
4275
4288
4541
4293
2443
1731
4016
3706
4052
4227
4306
4494
4265
3856
4465
4282
4063
4486
4509
4299
4365
4316
3912
5172
4024
-------
Ad-
dress
111
120
130
140
141
150
151
160
161
170
171
180
181
186
190
191
200
201
205
210
211
220
221
230
231
240
241
250
251
261
270
271
280
281
290
291
293
121
125
100
101
Street
Forest Avenue
Forest Avenue
Forest Avenue
Forest Avenue
Forest Avenue
Forest Avenue
Forest Avenue
Forest Avenue
Forest Avenue
Forest Avenue
Forest Avenue
Forest Avenue
Forest Avenue
Forest Avenue
Forest Avenue
Forest Avenue
Forest Avenue
Forest Avenue
Forest Avenue
Forest Avenue
Forest Avenue
Forest Avenue
Forest Avenue
Forest Avenue
Forest Avenue
Forest Avenue
Forest Avenue
Forest Avenue
Forest Avenue
Forest Avenue
Forest Avenue
Forest Avenue
Forest Avenue
Forest Avenue
Forest Avenue
Forest Avenue
Forest Avenue
Main Street
Center Street
Walnut Street
Walnut Street
Soil
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Silt
Land Use
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
MD Residential
School
School
School
Area
SF
6971
6294
6313
6353
6998
6333
6875
6372
6694
6392
6619
8120
6724
6312
6079
6599
6558
6500
6389
6562
6266
6566
6326
6570
6133
6575
6025
6579
6193
6379
6583
6169
6587
5411
3196
5894
3230
5200
8600
97601
43206
Roof
SF
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
0
69080
0
Parking
SF
8600
0
43206
Drive-ways, SF
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
0
0
0
Patios
SF
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
0
0
0
Imperv-
ious, SF
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
8600
69080
43206
Pervious
SF
4571
3894
3913
3953
4598
3933
4475
3972
4294
3992
4219
5720
4324
3912
3679
4199
4158
4100
3989
4162
3866
4166
3926
4170
3733
4175
3625
4179
3793
3979
4183
3769
4187
3011
796
3494
830
2800
0
28521
0
-------
Table A-2: Right of way attributes
Street Name
Acorn Street
Alpine Street
Ash Street
Ash-Acorn Connector
Ashmount Street
Ashmount Street ext.
Aspen Street
Birch Avenue
Cedar Street
Center Street
Elm Street
Forest Avenue
Highland Street
Main Street
Maple Street
Oak Street
Street A
Street B
Street C
Street D
Street E
stub between Elm
and Forest
Sycamore Street
Walnut Street
Total
RW
width,
ft
50
50
50
50
50
50
50
50
50
60
50
50
50
70
50
50
50
50
50
50
50
50
50
50
RW
length,
ft
1640
1125
1205
844
870
1620
851
2574
2899
1124
2639
2622
831
2741
2153
1751
490
465
517
415
397
519
1086
1167
Area,
sf
81990
56272
60251
42214
43492
80981
42537
128701
144940
67445
131944
131119
41568
191895
107667
87540
24491
23267
25829
20756
19875
25951
54281
58349
1693357
------- |