Urban Hydrological Modeling of the Malden River Using the Storm Water
Management Model (SWMM)
By
Sara Greenberg
B.A. Environmental Science, 2009
M.A. Environmental Science & Policy, 2010
Clark University
Submitted to the Department of Civil and Environmental Engineering
in Partial Fulfillment of the Requirements of the Degree of
ARCHIVES
Master of Engineering
in Civil and Environmental Engineering
at the
MASSACHU SETTS INSTITUTE
OF rECHNOLOLGY
JUL 02 2015
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
LI BRARIES
June 2015
0 2015 Sara Greenberg. All Rights Reserved.
The author hereby grants to MIT permission to reproduce and to distribute publicly paper and electronic
copies of this thesis document in whole or in part
in any medium now known or hereafter created.
Signature redacted-
Signature of Author:
I
Certified By:.
Depharfient of Civil ad Environmenta-Ei'gineering
May 18, 2015
Signature redacted
Senior Lecturer of Ci
Certified By:
David Langseth
nd Environmental Engineering
Thesis Advisor
Signature redacted
'Harold
/
Hemond
William E. Leonhard (1940) Professor of Civil and Environmental Engineering
Thesis Advisor
Accepted By:
____Signature redacted___
1
1
Heidi Nepf
Donald and Martha Harleman Professor of Civil and Environmental Engineering
Chair, Departmental Committee for Graduate Students
Urban Hydrological Modeling of the Malden River Using the Storm Water
Management Model (SWMM)
By
Sara Greenberg
Submitted to the Department of Civil and Environmental Engineering On May 18, 2015 in
Partial Fulfillment of the Requirements of the Degree of Master of Engineering in Civil and
Environmental Engineering
Abstract
The portion of the Malden River in Malden, Massachusetts, has a long history of industrial
activity and urbanization, which has degraded the water quality and ecosystem of the River.
Following years of water quality testing, community groups are concerned about the River's
current state of ecological health and its safety for recreational use. A first step to understanding
the River's current state is to understand its hydrology and the effect of rainfall on stormwater
discharge to the River. This thesis develops a site specific model to characterize runoff from
rainfall as it flows across the watershed, through the drainage system, and into the River. The
Storm Water Management Model (SWMM), developed by the Environmental Protection Agency
(EPA), was used because it was deemed the most appropriate of the readily available models for
this urban setting. This model characterizes the impervious nature of the land surface in the study
site. Both the Curve Number and Green-Ampt infiltration methods were used for the runoff
processes, with the Curve Number methods producing higher runoff values. For the hydraulic
processes used to calculate flow in the drainage system, kinematic wave routing was used. The
results showed substantial internal flooding within the drainage system. Since such flooding has
not been observed, this is an indication that the hydraulics portion of this model requires further
refinement in order to achieve fully accurate simulation. However, this model does achieve the
goal of providing insight into the watershed's hydrology, and develops a foundation from which
more robust analyses can done to model water quality and pollutant loadings, which may be used
in support of remediation strategies for the Malden River.
Thesis Supervisor: David Langseth
Title: Senior Lecturer of Civil and Environmental Engineering
Thesis Supervisor: Harold Hemond
Title: William E. Leonhard (1940) Professor of Civil and Environmental Engineering
Acknowledgements
I would like to thank my advisors Dr. David Langseth and Dr. Harold Hemond for the technical
expertise, guidance and support they provided. Their broad knowledge base was invaluable to
our research and provided many interesting anecdotes to keep our weekly meetings lively.
I also want to wholeheartedly thank Maggie Jacques and Mia Smith for their constant support,
good humor and keeping my spirits high through this tough and trying process.
Thank you to the Mystic River Watershed Association for their interest in and work on this local
river, specifically Patrick Herron for his guidance and new ideas that helped further our research.
In addition I would like to thank Patrick Johnston for giving us a personal tour of the River and
to Gary Stead and Steve Fama who provided me with the data and information to input into my
model. This data was critical to establishing such a sophisticated model.
Thank you to my parents who have constantly pushed me to succeed. They have tirelessly
supported all my endeavors and helped me overcome many hurdles throughout the years. I am
also grateful for Jason Fisher who has lent advice and unwavering support and been my rock
throughout this year. Through all the challenges I faced, he had faith in my ability to succeed
reminding me push through to the end of the race.
5
Table of Contents
1.
Background
11
1.1 Introduction
11
1.2 Geography of the Malden River
11
1.3 Industrial Legacy and Urban Environment
14
1.4 Stormwater System
14
1.5 Sewer System
15
1.6 Malden River Regulatory Framework
19
1.7 Community Efforts
20
1.8 MIT Efforts
21
Stormwater Management
21
Microbial Risk Assessment
22
Investigation of Sediment Contamination
22
1.9 This Study
2.
3.
22
Literature Review
23
A Brief History of Hydrology
23
Hydrological Modeling
23
Hydrologic Models for Urban Application: A Brief Review
24
TR-20/55
24
STORM
24
HEC-HMS
25
SWMM
25
Methodology
27
3.1 Study Area Selection, Delineation and Discretization
27
3.1.1 Study Area Delineation
27
Watershed Delineation
29
3.1.2 Study Area Discretization
30
3.2 Modeling Process Selection
32
3.2.1 Infiltration
32
6
3.2.2 Channel and Pipe Flow Routing
34
3.3 SWMM Model Parameter Description
36
3.3.1 Subcatchment Land Surface Properties
Slope Calculations
36
Width Calculations
37
Impervious Surfaces Roughness Coefficient
38
Pervious Surfaces Roughness Coefficient
38
Depression Storage
40
Curve Number Infiltration
40
Green-Ampt Infiltration
42
42
3.3.2 Junction/Conduit Properties
4.
5.
6.
36
3.3.2.1 Drainage Network Aggregation
42
3.3.2.2 Junction Properties
43
3.3.2.3 Conduit Properties
44
3.3.3 Rain Gage Data
45
Results
47
4.1 Rainfall-Runoff
48
4.2 Runoff and Outflow
49
4.3 Flow Routing
51
4.4 Continuity Error
52
53
Discussion
5.1 Infiltration Parameters
53
5.2 Junction and Conduit Aggregation
53
5.3 Single-Sided Verification
54
57
Summary and Conclusions
6.1 Summary
57
6.2 Conclusions
58
References
60
Appendix A: Input Files and Tables
63
63
Subcatchment Names
7
Overland Flow Calculations
64
Infiltration Parameters
65
Subcatchment Properties
66
Conduit Properties
67
Junction Properties
68
Appendix B
70
Drainage Pipe Attributes: An Issue of Units
70
Introduction to ArcGIS
70
GIS Coordinate System
70
Vertical Datum Reference
71
Appendix C: Simulation Results and Hydrographs
April 2004
72
72
Status Report
74
October 2005
75
Status Report
77
May 2006
78
Status Report
80
March 2010
81
Status Report
83
Appendix D: SWMM Manual Tables
84
8
List of Figures
Figure 1-1. Malden River sub-watershed
12
Figure 1-2. Geography Surrounding the Malden River
13
Figure 1-3. Malden River Stormwater Outfall Locations.
13
Figure 1-4. Malden stormwater drainage system
15
Figure 1-5: SSO Map #15: Reported SSOs in the Malden River Waterhsed
17
Figure 1-6: SSO Map #16: Reported SSOs in the Malden River Waterhsed
18
Figure 3-1. Directional divide for stormwater flow
28
Figure 3-2. Study Area
29
Figure 3-3. discretized Subcatchments in the study area
31
Figure 3-5. Impervious Surface and Landuse
39
Figure 3-6. Impervious Surfaces And Hydrologic Soils
41
Figure 3-7. Study Area Modeled Within swmm
42
Figure 3-8. Study Area in swmm broken into subcatchments, junctions (i.e. curb gutters or
manholes) and conduits (i.e. swales, culverts or pipes).
43
Figure 4-1. Screen Shot of Simulation Options Dialogue Box
47
Figure 4-2. Hydrograph from surface runoff processes
50
Figure 4-3. Hydrograph of discharge at outlet after transport routing
50
Figure 5-1. Altered Hydrograph in response to urbanization
55
Figure 5-2. Normalized flow for study area outlet and aberjona rain gage using April 2004
storm precipitation data
56
List of Tables
Table 1-1. Surface Water Quality Standards for Class B Warm Waters
19
Table 1-2. Water Quality Impairment Causes On The Malden River
20
Table 3-1. Subcatchment Slope CalculationTable
37
Table 3-2. Depression Storage
40
Table 3-3. Total storm depth values
46
Table 4-1. Summary of Results for both infiltration methods from all four storm events
48
Table 4-2. Summary Table for Kinematic Wave Flow Routing Results
51
9
10
1. Background
This section describes the geography of the Malden River, including important landmarks and
characteristics, followed by an overview of the history of the River. A summary of the
watershed's stormwater and sewer systems are included. The last part of the section provides the
current status of the River including community groups and ongoing studies.
1.1 Introduction
The Malden River, located in the Greater Boston area of Massachusetts, has an extensive history
of industrial activity and urbanization along its banks. Centuries of abuse by these activities have
reduced the river to a degraded condition, leading to concern about the River's ecological health
and its suitability for recreational use. The Malden River is classified as a Class B warm water
and as such, should be suitable for primary and secondary recreation; however, it is currently
listed on the Massachusetts '303(d)' list of Impaired Water Bodies (MADEP, 2014). Over the
past few decades, the communities surrounding the Malden River have become interested in
improving its condition. This thesis presents one portion of a joint effort at the Massachusetts
Institute of Technology (MIT) to provide the community with further scientific information
about the Malden River. Studies include an evaluation of stormwater Best Management Practice
(BMP) alternatives, a microbial risk assessment, and an investigation of sediment contamination.
This thesis focuses on developing a site specific model to characterize runoff from rainfall as it
flows across the watershed, through the drainage system, and into the Malden River.
1.2 Geography of the Malden River
The Malden River is a tributary to the much larger Mystic River, which is located within the 76square-mile Mystic River Watershed. The Malden River Sub-Watershed covers 11 square miles
in the towns of Everett, Malden, Medford, Wakefield, Stoneham and Melrose within the Mystic
River Watershed Figure 1-1.
11
Eastern
MA
Stoneham
Melrose
Mie
Medford
----
Legend
Study Area
~
--
Outlet Location
Town Boundaries
Subwatershed*
Map by: Sara Greenberg
Civil & Enviornmental MEng. 2015
Massachusetts Institute of Technology
0
1.050
2.100
4.200 Meters
FIGURE 1-1. MALDEN RIVER SUB-WATERSHED
*Delineates the portion of the Malden River watershed that flows directIy into the Study Area Outlet
Location (ArcMap 10.2.2, 2010).
Much of the Malden River flows underground, where it is hidden from view by the urban
landscape. The River begins at Spot Pond in the Fells Reservation and flows completely covered
beneath the cities of Melrose and Malden, as shown in Figure 1-2. The River re-surfaces from
two stormwater culverts, shown circled in red in Figure 1-3, near the center of Malden. From the
two culverts, the River flows aboveground for two miles, before discharging into the Mystic
River. The Amelia Earhart Dam is located a short distance downstream of where the Malden and
Mystic Rivers converge.
12
I
4
Malden~
FIGURE 1-2. GEOGRAPHY SURROUNDING THE MALDEN RIVER
Source: Google Maps (Google, 2015)
FIGURE 1-3. MALDEN RIVER STORMWATER OUTFALL LOCATIONS.
Two stormwater culverts are shown in red (Nangle Associates, 2014).
13
1.3 Industrial Legacy and Urban Environment
The Malden River has a long legacy of abuse due to industrial activity (U.S. Army Corps of
Engineers, 2008). During the Industrial Revolution, the River provided an essential means of
transportation and waste disposal for chemical, coal gasification, and other manufacturing plants.
In order to support these industries, much of the existing wetlands were dredged and filled to
straighten the river channels. Many of these historical activities have resulted in the release of oil
and hazardous materials (OHM) into the River. These contaminants include fuel by-products,
volatile organic compounds, and various metals, which can leach into the groundwater or
directly contaminate the River through natural hydrological pathways. Although many of the
industrial plants were relocated after World War II, industrial waste and dredged materials still
remain.
The surrounding towns of Malden, Medford and Everett have continued to develop since the
Industrial Revolution, creating an increasingly urbanized environment in the Malden River
watershed. Urban environments are characterized by large areas of impervious surfaces, such as
roadways, buildings, and parking lots, which prevent natural ground infiltration of rainfall.
Instead of percolating through the ground, rainfall runs into the storm drainage system and
eventually into the River, which increases the frequency and intensity of flooding in extreme
stormwater events. This increased volume of stormwater runoff can cause a variety of
environmental problems, including increased erosion and reduced base flows into the River.
These reduced base flows result in low water velocities and poor mixing conditions between
storm runoff events, which ultimately contribute to high bacteria concentrations in the Malden
River (Herron, 2014). Other water quality concerns arise as urban pollution contaminates the
runoff before it discharges into the River.
Just downstream of where the Mystic and Malden Rivers converge, the Amelia Earhart Dam
controls the flow of the Malden River (U.S. Army Corps of Engineers, 2008). The construction
of the dam has greatly changed the natural flushing of the River, leading to stratification and
depleted dissolved oxygen concentrations. These conditions have hindered the growth of a
healthy ecosystem in the Malden River.
1.4 Stormwater System
Much of the flow into the Malden River enters the system from stormwater drainage systems of
the towns located along the River. For example, the town of Malden has a series of conduits that
connect to form a stormwater drainage system, separate from the town's sanitary sewage system
as shown in Figure 1-4.
14
--------
Town Boundaes
Drainage
Map by: Sara Greenberg
Civil & Enviornmental MEng. 2015
0
1
EStormwater
Soo
1.000
2,000
Meters
Massachusetts Institute of Technology
FIGURE 1-4. MALDEN STORMWATER DRAINAGE SYSTEM
Source: (ArcMap 10.2.2, 2010).
1.5 Sewer System
All of the municipalities surrounding the Malden River have separate storm water and sewage
systems. Therefore there is no risk of Combined Sewer Overflows (CSOs) discharging into the
Malden River.
However, there have been incidents of the sanitary sewer systems around the Malden River
overflowing during extreme wet weather conditions. Under extreme weather conditions,
groundwater or stormwater can enter the sewer system at vulnerable points (such as blockages or
line breaks) and cause sewage to overflow downstream (US EPA, 2014). Figure 1-5 and Figure
15
1-6 show the locations of reported SSO incidents that have occurred in the Malden River
watershed.
Point 1+26 in Figure 1-5 shows the only known SSO that directly discharged into the Malden
River. This event occurred on March 29, 2010 and discharged over 1 million gallons of raw
sewage into the River (MWRA, 2015).
16
30LOM,
5~we a MAW
-wre
mahoe
OM5
AU~tA A&W PPAe,
CyO
---OM of
-w
svm
SSO map
1
FIGURE 1-5: SSO MAP #15: REPORTED SSOS IN THE MALDEN RIVER WATERHSED
Source: Massachusetts Water Resources Authority (MWRA, 2015)
17
300W OW O&VMW y amifA A#W ph=,
R"*CU b@U(fVN tt ~m
fltm *EIGIS
Location of SSO
-os
SSO map #
16
FIGURE 1-6: SSO MAP #16: REPORTED SSOS IN THE MALDEN RIVER WATERHSED
Source: Massachusetts Water Resources Authority (MWRA, 2015)
18
1.6 MaIden River Regulatory Framework
The Massachusetts Surface Water Quality Standards (314 CMR 4.00) categorize the Malden
River as a Class B warm water. Class B waters are designated as "a habitat for fish, other aquatic
life, and wildlife", for "primary and secondary contact recreation" and for irrigation, agricultural
and industrial process uses. Class B waters should also have "consistently good aesthetic value"
(MADEP, 2014). The standards that apply to the Malden River are summarized in Table 1-1.
TABLE 1-1. SURFACE WATER QUALITY STANDARDS FOR CLASS B WARM WATERS
Parameter:
Class B Standard:
Dissolved Oxygen
<5.0 mg/1
Where natural background conditions are lower, DO shall not be less than
natural background conditions.
<83 0 F
The rise in temperature due to a discharge shall not exceed 51F 12
6.5-8.3
No more than 0.5 units outside of the natural background range.
Bathing (non-bathing): E.coli as indicator - geometric mean of five most
recent samples taken during the same bathing season (within the most recent
six months) shall not exceed 126 colonies per 100 m and no single sample
shall exceed 235 colonies per 100 ml
Bathing (non-bathing): Enterococci as indicator- geometric mean of five most
recent samples taken during the same bathing season (within the most recent
six months) shall not exceed 33 colonies per 100 m and no single sample shall
exceed 61 colonies per 100 ml
Shall be free from floating, suspended and settleable solids in concentrations
and combinations that would impair any use assigned to this Class, that
would cause aesthetically objectionable conditions, or that would impair the
benthic biota or degrade the chemical composition of the bottom.
Shall be free from color and turbidity in concentrations or combinations that
are aesthetically objectionable or would impair any use assigned to this Class.
These waters shall be free from oil, grease and petrochemicals that
produce a visible film on the surface of the water, impart an oily taste to the
water or an oily or other undesirable taste to the edible portions of aquatic life,
coat the banks or bottom of the water course, or are deleterious or become
toxic to aquatic life.
None in such concentrations or combinations that are aesthetically
objectionable, that would impair any use assigned to this Class, or that would
Temperature
pH
Bacteria
Solids
Color and Turbidity
Oil and Grease
Taste and Odor
cause tainting or undesirable flavors in the edible portions of aquatic life.
Natural seasonal and daily variations that are necessary to protect existing and designated uses shall be maintained.
Source: (MADEP, 2014)
19
Currently, the Malden River is not in compliance with these surface water quality standards
(MADEP, 2013). Section 303(d) of the Clean Water Act requires each state to publish a list of
water bodies that do not meet state water quality standards. In compliance with this mandate, the
Malden River is included on the Massachusetts' 303(d) list. The specific causes of impairment
are listed in Table 1-2.
TABLE 1-2. WATER QUALITY IMPAIRMENT CAUSES ON THE MALDEN RIVER
Malden River: Impairment Causes
(Debris/Floatables/Trash*)
PCB in Fish Tissue
Chlordane
Phosphorus (Total)
DDT
Dissolved oxygen saturation
Secchi disk transparency
Secchi disk transparency
Escherichia coli
Fecal Coliform
Foam/Flocs/Scum/Oil Slicks
Oxygen, Dissolved
Sediment Bioassays -- Chronic Toxicity
Freshwater
Taste and Odor
Total Suspended Solids (TSS)
PCB in Fish Tissue
* TMDL not required (Non-pollutant)
This table is in agreement with the version in the proposed 2014 IntegratedList of Waters report.
Source: (MADEP, 2013)
After identifying the impaired water bodies, each state is also required to establish priorities for
development of Total Maximum Daily Loads (TMDL) that specify "the maximum amount of a
pollutant that a water body can receive and still meet water quality standards" (MADEP, 2014).
Massachusetts's current schedule for TMDL development makes no specific reference to the
Malden River. However, the Malden River is included under a broader priority to develop
watershed wide bacteria TMDLs for Boston Harbor. Final EPA approval of Boston Harbor
bacteria TMDLs is expected to occur in Fiscal Year 2015.
1.7 Community Efforts
In response to the Malden River's degraded water quality, there has been a growing community
effort to transform the River into a healthy ecosystem that can provide recreational space to the
public. Some key organizations leading this effort include the Mystic River Watershed
Association, Friends of the Malden River, and the Army Corps of Engineers.
The Mystic River Watershed Association (MyRWA) works to protect the entire Mystic River
watershed through advocacy, outreach and education, water quality monitoring, and restoration
20
efforts. MyRWA manages an extensive water quality monitoring program across the Mystic
River Watershed, including a sampling site on the Malden River at which samples have been
collected since July 2000.
Friends of the Malden River (FOMR) is a community group that champions environmental
conservation of the Malden River. FOMR advocates for an improved river ecosystem, focusing
directly on water quality, public access, outreach, and youth involvement (FOMR, 2015).
The Army Corps of Engineers (ACE) (2008) evaluated several strategies for ecosystem
restoration along the Malden River. The ACE expressed concern about the potential for toxic
pollution in the sediments of the Malden River, which would seriously threaten the local
ecosystems and potentially inhibit recreational use of the River. The ACE published a report
which includes an environmental assessment of the Malden River, an analysis of several
restoration activities, and a recommended plan for ecosystem restoration. The plan recommends
the creation of a wetland habitat through the removal of invasive plant species and the deposition
of sand and gravel in various areas along the Malden River. These activities aim to reduce the
inflow of contaminated sediments, groundwater, and urban stormwater runoff, which have all
been identified as major sources of water contamination on the Malden River.
1.8 MIT Efforts
The local communities would like to see the Malden River ecosystem restored to a level that
would allow the waterway to be used for recreational activity (including boating, swimming and
fishing). Led by organizations such as MyRWA and FOMR, the communities surrounding the
Malden River requested technical assistance to better understand the River's current state and
any possible risks associated with its use.
Several MIT studies were conducted to provide the communities surrounding the Malden River
with a better understanding of its current state. This report presents a hydrological model for
quantifying stormwater runoff into the Malden River. Other studies include an evaluation of
alternatives to stormwater management, microbial risk assessment, and an investigation of
sediment contamination. Brief summaries of these studies are presented below.
Stormwater Management
An evaluation of alternatives to manage stormwater runoff along the Malden River was
conducted (Smith, 2015). A feasibility and performance study was done to determine the best
options for the Malden River watershed.
21
Microbial Risk Assessment
A microbial risk assessment was conducted to determine the risks of recreational use of the
Malden River (Jacques, 2015). Rainfall and water quality data were analyzed to determine the
risk of illness assumed by recreational users of the Malden River.
Investigation of Sediment Contamination
Investigations of the sediment contamination of the Malden River were conducted (Sylman,
2015; Khweis, 2015; Oehmke, 2015). Sediment quality data was used to calculate the potential
concentration distributions of various contaminants in the Malden River. The potential for
sediment suspension into the water column was also calculated. Further, this information was
used to conduct a preliminary risk assessment of sediment exposure during recreational
activities.
1.9 This Study
This thesis develops a site specific model to characterize runoff from rainfall as it flows across
the watershed, through the drainage system, and into the River. The Storm Water Management
Model (SWMM), developed by the Environmental Protection Agency (EPA), was used because
it was deemed the most appropriate of the readily available models for this urban setting. Section
2 provides background on hydrological modeling and discusses a few of the alternative models.
Section 3 describes the methods used to collect the data, determine the appropriate hydrological
processes, and develop the site specific model using SWMM. Results are presented in Section 4.
22
2. Literature Review
Hydrology is multidisciplinary in its analyses of the occurrence, circulation, and distribution of
water storages and flows of the Earth (Bedient & Huber, 1992). The hydrologic cycle is complex
in its connections to weather patterns, soil types, topography and other geologic factors; blending
the boundaries between hydrology and other disciplines. Hydrology provides a framework for
calculating fluid flow across varying geographic surfaces. The hydrologic cycle is a continuous
process whereby precipitation falls onto the land and flows across the surface, into local streams
or rivers, and evaporates into the atmosphere, only to condense and fall back to the earth in the
form of precipitation (Bedient & Huber, 1992). Within this cycle, water that flows across the
land surface can also infiltrate into the soil, enter the groundwater, and ultimately rivers or
oceans, and return to the atmosphere through evapotranspiration.
A Brief History of Hydrology
The early history of hydrology shows the existence of water management practices throughout
the Middle East, China and Egypt in the form of irrigation, flood control activities, and the
damming of the Nile about 4000 B.C. (Bedient & Huber, 1992). Early hydrologic theories were
developed by Greek Philosophers, such as the hypothesis that the source of surface springs and
streams came from deep inside mountains. By the 18t Century, more complex theories were
developed, when Daniel Bernoulli investigated the forces present in a moving fluid, followed by
hydrologic advancements in the 19th Century with Darcy's law of flow and the Hagen-Poiseuille
capillary flow equation.
Hydrological Modeling
Hydrological modeling was established with the formulation of the rational method to relate
rainfall intensity with peak storm runoff (Singh & Woolhiser, 2002). At the turn of the 20
Century, Green and Ampt developed theories of infiltration based on simplified physics, which
was followed by Horton's analysis of overland flow and the development of an empirical
formula for rainfall infiltration (Singh & Woolhiser, 2002). In an attempt to quantify other
parameters in rainfall-runoff, such as depression storage, the U.S. Department of Agriculture
(USDA) Soil Conservation Service (SCS) developed the curve number (CN) method. Until the
1960s however, theories of individual hydrologic components, such as overland flow,
infiltration, or depression storage, formed much of the basis for models (Singh & Woolhiser,
2002). Additionally, other government agencies, such as the U.S. Geological Survey and Army
Corps of Engineers, performed hydrological research to improve upon hydrologic modeling
methods (Bedient & Huber, 1992).
23
During the 1960s, complex hydrologic models that simulated multiple components of the
rainfall/runoff process were developed, the first of which was the Stanford Watershed Model; the
first comprehensive simulation of all the major processes in the hydrologic cycle. Another
development in hydrological modeling during this period was the HEC-l watershed model,
created by the Army Corps of Engineers, which used simple loss functions and unit hydrographs
to simulate floods from rainfall data (Bedient & Huber, 1992). HEC-1 spurred the development
of other event models which employed storm event, hydrologic surface water models and applied
them to a watershed scale (Conservation Engineering Division, 2004).
Hydrologic Models for Urban Application: A Brief Review
Watershed development and increased impervious surfaces development leads to decreased
infiltration capacity and increased runoff velocity, effectively increasing the efficiency with
which water is transported to rivers (CWP, 2003). As rainfall and stormwater runoff from urban
areas were shown to be increasingly causes of river pollution and watershed harm, there was a
growing need to incorporate drainage system routing into these single and multiple storm event
models (Tsihrintzis & Hamid, 1998). There are many urban hydrological models widely used,
but not all of them have hydraulic routing capabilities. Four urban watershed models are
compared in terms of the hydrological processes. This review includes TR-20/55, STORM,
HEC-HMS and SWMM.
TR-20/55
The Soil Conservation Services (SCS) method utilizes soil storage information to predict
rainfall-runoff volumes, using unit hydrograph procedures to calculate the distribution of runoff
in time (hydrographs) for a given rainfall distribution in time (hyetograph) and watershed soil
characteristics. These procedures were created in 1965 and described in the number 20 of SCS's
Technical Releases (TR-20) (Soil Conservation Service, 1992). The TR-20 methods were
adapted to urban areas by adding urban land use curve number values, as described in Technical
Release 55 (TR-55) (Natural Resources Conservation Service, 1986). TR-55 is oriented toward
design procedures rather than simulating individual storm events, as it employs synthetic design
storms. The TR-20/55 methods simulate the rainfall/runoff process only, and do not include the
hydraulics of a stormwater drainage system.
STORM
The Storage, Treatment, Overflow and Runoff Model (STORM), developed by the Army Corps
of Engineers, is a continuous simulation model developed to alleviate combined sewer overflows
(Bedient & Huber, 1992). This model analyzes quantity and quality of runoff in order to aid in
the design of storage and treatment facilities. Thus, it is more heavily focused on pollution
24
control and treatment of water quality without the ability to model hydraulic flow through an
urban stormwater drainage system.
HEC-HMS
&
The Hydrologic Modeling Systems (HEC-HMS) was developed in the Hydrologic Engineering
Center by the Army Corps of Engineers. This model is a distributed model, easily adapted to a
link-node format, which converts rainfall into a runoff hydrograph for each watershed (Akan
Houghtalen, 2003). HEC-HMS allows the user to choose physically based processes to
characterize a specific watershed by allowing the user to change characteristics such as
impervious surface, soil moisture content and flow length (Akan & Houghtalen, 2003). The
model is structured in three components: the meteorological model, the watershed model, and
control specifications. The meteorological component links rainfall data to surface runoff in the
watershed model, from which control specifications can be made to transform runoff into
channel and reservoir routing, routing flow through an urban area. This program is often used for
large watersheds or applications in small urban areas. The program design has simplified model
formulation and flow representation, with the goal of shortening processing time and increasing
model efficiency (US Army Corps of Engineers, 2013). However, such simplification can be a
limitation to creating a tailored characterization of a system, which could require more complex
analysis. As with TR-20/55, the HEC-HMS model simulates the rainfall/runoff process only, and
does not include the hydraulics of a stormwater drainage system.
SWMM
The EPA's Storm Water Management Model (SWMM) was developed in 1971 and simulates
rainfall-runoff processes, flow through drainage system networks, and water quality from
developed urban and undeveloped or rural areas (James, Rossman, & James, 2010); Rossman,
2010). SWMM 5 is the current version of SWMM and runs on a Windows platform.
SWMM operates by tracking water and material flows between various environmental
compartments; atmosphere, land surface, groundwater, and the drainage system (James et al.,
2010). As described by James et al. (2010) the atmospheric compartment is characterized by
rainfall data, which is called the rain gage model component. The runoff component divides the
rainfall into surface runoff, infiltration, and depression storage, and also tracks pollutant loads.
The routing component transports the surface runoff as overland flow or through an underground
drainage system, represented as a series of connected drainage elements. By combining these
compartments and modeling a system, SWMM can simulate runoff quantity and pollutant
loading. During a simulation, the quantity and quality of runoff generated within each
25
subcatchment, and the flow rate, flow depth, and quality of water in each pipe and channel are
tracked.
To the end goal of characterizing stormwater runoff peaks and volumes, and predicting the
effects of watershed changes, there are many urban hydrologic models available, only a few of
which were reviewed here. Choosing a model that is widely accepted by engineers and
regulators, and is inexpensive, user friendly, flexible and technologically advanced narrowed the
choice to HEC-HMS and SWMM. HEC-HMS has more hydrologic simulation choices, but a key
advantage of SWMM is that the capability of hydraulic and pollutant load modeling are
integrated into one model. SWMM was thus an appropriate choice for this analysis.
26
3. Methodology
The Malden River watershed is largely in the communities of Malden, Melrose, and Everett, all
of which are heavily urbanized. This urbanization, along with the industrial history along the
River discussed in Section 1, causes many quality impacts on the Malden River. Increased storm
water runoff increases the frequency and severity of flooding, altering the stream bed
composition; it creates reduced base flow; and it increases entry of toxic substances such as
heavy metals, pesticides, oil, road salt, detergents, etc. and elevated nutrient inputs to the stream
(Klein, 1979; Smith, 2015). Smith (2015) provides further discussion of the impacts of
urbanization on stream water quality. The first step in modeling storm water runoff was to
choose a study area, which would be simulated with the SWMM model, from within the Malden
River watershed.
3.1 Study Area Selection, Delineation and Discretization
The criteria for selecting the study area were as follows:
*
The study area should not be too large to be addressed over the course of a 9-month
academic program.
* The study area should be heavily urbanized.
" The outlet location from the study area should be easily accessible for data collection, to
support future calibration efforts.
" The study area should be suitable for associated MIT efforts, notable the BMP evaluation
by Smith (2015).
As shown in Figure 1-3 the Malden River daylights from two culvert locations within the city of
Malden. Investigation of these locations revealed that the culvert to the east was a better outlet
location for the study area, as it was a more accessible location from which to collect data. After
a request for stormwater drainage information, the city of Malden's Engineering Department
responded by granting access to its engineered drainage system files. Furthermore, the city of
Malden is extremely urban, not too large in area, and was within an acceptable region for
Smith's (2015) analysis. Thus, the city of Malden fulfilled the selection criteria and was chosen
to be the city in which a study area was delineated for this thesis.
3.1.1 Study Area Delineation
The first step towards study area delineation was to analyze the stormwater drainage system
within the city of Malden. This system is divided into east and west, from Bryant Street in the
south to Maplewood and Lebanon Streets in the north, as shown Figure 3-1. This divide
27
/
represents two directions of flow within the city's stormwater drainage network. Any stormwater
inlets and pipes west of this divide flow into the Malden River, while all pipes east of this divide
and those connected to Bryant Street, flow east into Town Brook, as shown in Figure 3-1 (Stead,
2015). The dividing line on Figure 3-1 therefore shows the easternmost extent of the study area
watershed.
0
Ave
SWI
Aw"'
~
Malden
1*
4.
("Di
4.
14
f
5
/
.i~0e
woo
West Flow Outlet
/
21,
1~>
~.
~
East Flow Outlet
Dividing Line
FIGURE 3-1. DIRECTIONAL DIVIDE FOR STORMWATER FLOW
Sources: Google Maps (Google, 2015) and Malden City Engineering Department (Stead, 2015)
Figure 3-2 shows the selected sub-watershed and associated drainage network. The drainage
system includes a network of pipes that branch off of the main line, all of which route water
toward the indicated outlet location.
28
Legend
Study Area Outlet Location
Town
Boundary
Water Features
-- Stormwater Drainage
-- Study Area Drainage
Study Area
0
0.25
0.5
1
11
1 Miles
1 1I
Map by: Sara Greenberg
Civil & Enviornmental MEng. 2015
Massachusetts Institute of Technology
FIGURE 3-2. STUDY AREA
Source: ArcGIS (ArcMap 10.2.2, 2010)
There were locations where the boundary of the study area cut through a street or lot. In those
instances the direction and shape of the boundary line was determined site-specifically by
comparing aerial photography with a map of elevation. The perimeter was drawn so that it would
not interrupt an overland hydrologic flow path, using the procedures described below.
Watershed Delineation
To further ensure the study site boundary was within the boundary of natural subcatchments, it
was overlayed with a delineated watershed map, created using Environmental Systems
Resources Institutes' (ESRI) program ArcMap, part of the geospatial software ArcGIS (ArcMap
10.2.2, 2010). A Digital Elevation Model (DEM) map was imported into ArcGIS and
manipulated using the Terrain Processingtools in the ArcHydro extension. The Terrain
Processingtools are shown in italics. First, thefill sinks tool was used to fill in any possible
29
sinkholes that might be in the landscape, which would affect flow direction and pathways. Then,
the Flow Direction tool was used to create a raster image showing direction of flow from each
cell to its steepest downslope neighbor. The total area that was up slope or down slope of any
given cell was then calculated with Flow Accumulation. In order to assign a realistic number of
streams to this watershed, a stream threshold was determined using the stream definitions tool.
The stream threshold value determines how many flow catchment channels are represented as
streams, which varies depending on the topography and vegetative growth of the given
landscape. Based on maps showing actual stream locations and extent, the appropriate cutoff
value for stream formations was 1000 pixels. At a 5 meter resolution this equals an area greater
than 0.025 square kilometers.
Once the stream threshold was determined, stream segmentation was used to segment stream
sections based upon their area of flow and unique characteristics. These segmented streams were
then used as an input into the catchment delineation tool, which identified the unique catchment
that each stream came from. This output raster was then converted to a shapefile using
catchmentpolygon processingtool. In addition, this tool adds an ArcHydro identifier in the
attribute table for each unique catchment. These identifiers are important to have in the
catchment layer so they can be used as input values for other tools in the ArcHydro extension.
Finally, a separate point shapefile was created using batch point generation, which showed the
outlet location of interest. All of these files were combined into the batch watershedprocessing
tool, which produced a map showing the up slope area that flows to the outlet location.
The watershed and sub-catchment files were used to identify the extent of the watershed for the
selected outlet location (Figure 3-2) based on topography. The pipe network and topographic
catchment area were then used in combination to develop the final study area definition. The
study area's topography was also used as a guide to determine appropriate discretization of the
individual subcatchments within the study area, as drawn into the SWMM model and described
below.
3.1.2 Study Area Discretization
Once the study area was delineated it was discretized based upon the structure of the drainage
system. First, each sub-network of stormwater drains that branched off the main trunkline was
identified and used to define a subarea within the study area. Each sub-area was then further
divided into an upland and lowland portion. This process of disaggregation was used to identify a
total of 17 subcatchments to represent the entire drainage area, as shown in Figure 3-3 below.
This number of subcatchments represented enough hydraulic detail to define the system at a
reasonable level of detail, yet not too many to make data collection impossible during the time
available for this project.
30
Legend
Stormwater Drainage
Subcatchment
I
0.5 Miles
0.125 0.25
0
I
I
I
I
I
I
I
I
FIGURE 3-3. DISCRETIZED SUBCATCHMENTS IN THE STUDY AREA
Source: ArcGIS (ArcMap 10.2.2, 2010)
31
3.2 Modeling Process Selection
Within the SWMM model, the user must make selections pertaining to the processes governing
infiltration and transport routing. There are three methods for calculating infiltration: Horton,
Green-Ampt, and Curve Number methods. There are also three options for channel and pipe
flow routing: steady flow, kinematic wave, and dynamic wave flow routing. For this analysis,
both Curve Number and Green-Ampt infiltration methods were chosen for a comparison.
Kinematic wave routing was chosen to model channel and pipe flow routing.
The data required to characterize the study area in SWMM and calculate infiltration and flow
routing parameters needed to be collected from various sources. With ubiquitous and constantly
improving Geographic Information Systems (GIS) data, much of the physical and biological data
required for the SWMM model were available as spatial data files. Thus, using GIS datasets was
the most effective method of large scale data collection.
Many inputs were required for simulating the land surfaces, which were obtained from the
Massachusetts Office of GIS online data portal (Commonwealth of Massachusetts, 2015).
These data files, or layers, were viewed and analyzed ArcGIS (ArcMap 10.2.2, 2010). Data
layers obtained included current landuse, soils type, digital elevation model (DEM), topographic
contours, impervious surfaces and ortho imagery. The aerial photography (ortho imagery file)
was used as a reference to identify streets, buildings and spot check the locations where the
Malden study area was cut off. This was done to determine if the study area boundary was
inappropriately excluding portions of land surfaces that would drain into the drainage system and
therefore into the Malden.
3.2.1 Infiltration
Rainfall is distributed among capture in depression storage, infiltration (which becomes input to
the groundwater compartment), and surface runoff. The SWMM model simulates the process of
surface runoff generation by calculating depression storage and infiltration, and then subtracting
those values from the total rainfall. Infiltration modeling is thus a key process in the simulation
of surface runoff. SWMM offers three options for calculating infiltration: the Horton, Curve
Number, and Green-Ampt methods. Once the surface runoff generation rate for a given
subcatchment is calculated, SWMM calculates the flow across the land surface by combining the
continuity equation and Manning's overland flow equation. Each of the three infiltration
methods is discussed briefly below.
32
Horton infiltration is an empirical method that compares rainfall intensity to infiltration capacity
and generates surface runoff whenever the former is greater than the latter (James et al., 2010).
However, parameters for infiltration capacity and maximum infiltration capacity produce rates
that are often greater than typical rainfall intensities, especially during light rainfalls. With higher
rates of infiltration, more water will infiltrate into the ground and not enough water will runoff
the surface and enter the drainage system. SWMM has modified Horton's model to correct this
problem, making infiltration capacity a function of water infiltrated rather than a function of
time, which ignores other effects.
The Curve Number (CN) method is the infiltration method used in TR-20/55, which was
discussed in Section 2. This is among the most commonly used infiltration modeling method
and as such support for calculating curve numbers are readily available. For example, soil type
classifications in terms of Hydrologic Soil Groups, as needed for the Curve Number method, are
readily available through the MassGIS Office (Natural Resources Conservation Service, 1986).
Green-Ampt infiltration is a method developed in 1911 and based on physical parameters of the
soil. This method is based on a saturated front moving uniformly downward through the vadose
zone, which has a specified initial moisture content. The Green-Ampt calculation is based on
physical parameters including hydraulic conductivity and soil suction head (James et al., 2010).
Within SWMM, the formulation happens in two stages, as shown below. First, the volume of
water that infiltrates before the surface becomes saturated is predicted, then the infiltration
capacity is predicted using the Green-Ampt equation.
For F < F: f = i and F = (S=)(lMD)
LKs-1
And for F > Fs: f =
f
and f = K (1 + ()(IMD)
Where f = infiltration rate (ft/s)
fp = infiltration capacity (ft/s)
i = rainfall intensity (ft/s)
F = cumulative infiltration volume, this event (ft)
F; = cumulative infiltration volume required to cause surface saturation (ft)
Su = average capillary suction at the wetting front (ft water)
IMD = initial moisture deficit for the event (ft/ft)
Ks = saturated hydraulic conductivity of soil (ft/s)
33
I selected the Curve Number and Green-Ampt infiltration methods for the SWMM modeling of
the study area. The Curve Number infiltration method is commonly used and the parameters
required to input are readily available or easily quantified using GIS land cover and soil data. I
selected the Green-Ampt infiltration method in order to include a methodology that was based on
physical soil parameters, for which documentation on these calculations is readily available in
the SWMM manual. Horton infiltration was excluded from the comparison within this SWMM
model because its approach is more empirical in nature.
3.2.2 Channel and Pipe Flow Routing
Within the flow routing portion of the model there are three levels of modeling; steady flow
routing, kinematic wave routing, and dynamic wave routing (James et al., 2010). All three of
these flow routing equations use Hazen-Williams equation for circular conduits and Manning's
equation for all other conduit shapes.
Steady routing is the simplest option and is based on uniform at each time step. Upstream
hydrographs are translated downstream without attenuation and channel storage or backwater
effects cannot be taken into account (James et al., 2010). This option can be appropriate for longterm continuous simulation, however this analysis is simulating flow and rainfall changes during
short storm events, and thus steady routing was not selected.
Kinematic wave method uses the continuity equation along with momentum equation to model
flow in only the downstream direction. If there is excess water it can only be modeled as a loss to
the system, or pond at the node and be routed once the channels have room again. The kinematic
wave method cannot account for backwater effects.
The dynamic wave method completely solves the Saint-Venant equation and allows for
pressurized flow and backwater effects to be modeled (James et al., 2010). Dynamic wave
routing couples the solution for both water level at nodes and flow in conduits and is well suited
for any general system. This method is the best choice for systems subjected to significant
backwater effects due to downstream flow restrictions and with flow regulation via weirs and
orifices (James et al., 2010).
According to Stead (2015) Malden has not experienced storm drain system flooding (i.e.,
situations in which the stormwater that has entered the storm drain system rises to the ground
surface), and my purposes in performing this work did not include detailed modeling of flooding
within the drainage system. Further, I did not represent all the conduits in the system, but rather
aggregated certain portions of the conduit system within each subcatchment, as will be discussed
in more detail in 3.3.2.1 Drainage Network Aggregation. Thus, there would be no value added
34
by selecting the more complex dynamic routing method. Given that I did want to represent
routing dynamics within a storm, but did not need the dynamic routing capabilities, I selected the
kinematic wave approach for flow routing.
35
3.3 SWMM Model Parameter Description
Once the modeling processes for infiltration and flow routing were selected, and the
subcatchments defined, the subcatchment land surface, junction/conduit, and rain gage properties
needed to be defined. Within SWMM a backdrop image of the study area and the discretized
catchments was loaded. This was used as a background on top of which to trace the
subcatchments, junctions, and conduits, in order to draw the most realistic representation of the
drainage system.
3.3.1 Subcatchment Land Surface Properties
Parameters entered into the subcatchment properties dialogue box included: area, slope, width,
percent impervious, Manning's roughness coefficient (n) for impervious and pervious surfaces,
and depression storage. With the exception of n and depression storage, the other subcatchment
properties were calculated using ArcGIS. Within subcatchments properties, infiltration method
parameters were also entered, which were analyzed and calculated in ArcGIS. For Curve
Number and Green-Ampt infiltration, the following parameters were calculated: curve number,
saturated hydraulic conductivity, suction head and initial deficit. The calculations for the
subcatchments parameters can be seen in various input tables in Appendix A: Input Files and
Table.
Slope Calculations
Digital Elevation Model (DEM) maps were used to analyze elevation change of the terrain,
which affects the rate and path of rainfall-runoff. The map is a raster dataset, which represents
the surface elevation across the area, including depth below sea level. Using this map, average
slope for the study area was calculated by measuring the elevation change from the inlet to the
outlet, and dividing it by the corresponding horizontal distance. Seven measurements were
taken, which were representative of elevation variations throughout the study area. These slope
values were then averaged, as shown in Table 3-1.
Slope (%) =
*100; Where H= elevation (ft) and d = distance (ft)
36
TABLE 3-1. SUBCATCHMENT SLOPE CALCULATIONTABLE
slope
(%)
elevation
(ft)
distance
(ft)
154
4858
3.17
138
5970
2.31
75
2086
3.62
69
1893
3.64
46
3798
1.21
46
4257
1.08
46
8062
0.57
Average Slope (%)
2.23
The average slope, calculated for the study site, is close to the typical value of 2% given for
urban areas in the SWMM manual (James et al., 2010). Since the study site is 65% impervious,
and known to be a highly urban environment, this slope seemed reasonable. This value was
applied to all of the subcatchments in the SWMM model.
Width Calculations
Subcatchment width was calculated for each subcatchment by measuring overland flow path
length using the DEM map in ArcGIS. As stated in the manual, subcatchment width is calculated
by dividing each subcatchment area by its overland flow length, as follows:
Width (ft) = AIDOF, where A = area (ft2 ) and DOF = overland flow length (ft)
The SWMM manual states that overland flow length for urban areas can be represented as the
distance from the back of a typical lot to the middle of the street (James et al., 2010). Since each
subcatchment had urban neighborhoods with some green spaces interspersed, the following three
flow lengths were calculated: the shortest distance (lot to street), a medium distance, and the
longest distance. However, the more frequently occurring flow path lengths were the short and
medium. Thus, a weighted average was used to calculate a representative flow length for each
subcatchment. To aid in visual measurement of distance across the land surface, the DEM layer
was used in conjunction with a contour map that had clearly defined isolines of elevation.
37
Impervious Surfaces Roughness Coefficient
A raster file of impervious surfaces was used to identify the percentage of each subcatchment
within the study area that contained impervious surfaces. Using ArcGIS the impervious area was
divided by total area to calculate percent impervious for each of the 17 subcatchments. A
Manning's roughness coefficient was calculated by analyzing the distribution of surfaces found
within the impervious surface layer, which is defined as constructed surfaces such as buildings,
roads, parking lots, brick, asphalt, and concrete. In order to see the distribution of surface types,
aerial photography was overlain with the impervious layer. The n values for impervious surfaces
were taken from the American Society of Civil Engineers manual (ASCE, 1969), where a range
of values is given for each of the following impervious surfaces: asphalt, concrete and brick. The
midpoint of each range of values was the same value, which was chosen to represent the
impervious n for each subcatchment.
Pervious Surfaces Roughness Coefficient
In order to calculate the Manning's n for pervious surfaces, land cover maps were analyzed
within ArcGIS. The land use maps were created from ortho imagery captured in 2005 and used a
semi-automatic process to assign the 40 land cover/land use categories, which contain
modifications of previous land use datasets (Commonwealth of Massachusetts, 2015). Within the
study area, there were 11 land use/land cover categories. Using the clip function in the Analysis
Tools, the land use map was overlain with impervious surfaces to analyze only the land use
categories that were within the pervious region of the study area. The distribution of land
cover/land use types within the region of the study area considered pervious was analyzed, as
shown in Figure 3-4.
38
Legend
Stormwater Drainage
Landuse
Cemetery
Commercial
Forest
High Density Residential
Industrial
Medium Density Residential
Multi-Family Residential
Participation Recreation
Transportation
Urban Public/institutional
Water
0
0.125 0.25
I I I I I
I
0.5 Miles
I
Impe
FIGURE 3-4. IMPERVIOUS SURFACE AND LANDUSE
Source: ArcGIS (ArcMap 10.2.2, 2010)
The pervious areas were comprised of primarily multi-family residential, cemetery, and
or
recreation land use categories. The pervious land cover for these categories was mostly lawns
grassy areas. Thus, the n value for dense grass was taken from the SWMM manual (James et al.,
2010) and applied to all 17 subcatchments for pervious surfaces.
39
Depression Storage
Depression storage was calculated for impervious and pervious surfaces. Both values were taken
from Section 24.5 of the SWMM manual (James et al., 2010). Since the majority of the
pervious region was dense grass or lawns, the value for lawns was used to represent depression
storage in all subcatchments. Given that the study area is in a highly urbanized area, other
pervious surfaces such as forest and pasture land were are unlikely. Values and detailed input
properties for all 17 subcatchments can be found in Appendix A: Input Files and Table.
The depression storage was calculated as an average of all the pervious surfaces and impervious
depression storage was taken as the low end of the range, from section 24.5 in the SWMM
manual, and as shown in Table 3-2 below (James et al., 2010).
TABLE 3-2. DEPRESSION STORAGE
Pervious Surface
D-Store (in)
Lawns
Pasture
Forest Litter
Average Pervious
Impervious Surface
0.10 - 0.20
0.2
0.3
0.2 inches
0.05
Source: Section 24.5 Tables (James et al., 2010)
Curve Number Infiltration
In order to calculate the parameters for Curve Number infiltration, a data layer of soil type was
analyzed in ArcGIS. This data layer was mapped and approved by the NRCS and is the most
detailed and field verified soil geographic dataset (Commonwealth of Massachusetts, 2015).
These soils are classified based on a complex taxonomy and defined as "minerals and organic
matter, liquid, and gases that occur on the land surface, occupies space and are characterized by
one or both of the following: horizons... or the ability to support rooted plants" (U.S.
Department of Agriculture, 1999). Each soil type has a description which can be associated with
an SCS hydrologic soil group: A, B, C, and D. These four types of soils are categorized based on
their infiltration capacity (James et al., 2010). Soils in group A have higher infiltration capacity
and are comprised of excessively drained sands and gravels with high infiltration rates. Soils in
group D have lower infiltration capacity with higher runoff potential, composed of clays or a
shallow layer of soil above an impervious surface. Using the clip function in the Analysis Tools,
the soil type map was overlain with impervious surfaces to isolate soils within the pervious
region of the study area, as shown in Figure 3-5.
40
Legend
--
Drainage System
Impervious Surfaces
Hydrologic Soils
A
D
0
0.125
0.25
0.5 Miles
Map by: Sara Greenberg
Civil & Enviornmental MEng. 2015
Massachusetts Institute of Technology
FIGURE 3-5. IMPERVIOUS SURFACES AND HYDROLOGIC SOILS
Source: ArcGIS (ArcMap 10.2.2, 2010)
Each soil type was then assigned its corresponding hydrologic soil group, which comprised only
A and D soils within the study area, as shown in Figure 3-5. These soil groups were then
(see
assigned the Curve Number corresponding to the soil type and dense grass land cover
discussion above), as given by the SCS and found in the SWMM manual. For each
soil
subcatchment, the CN value was spatially averaged based on total area of each hydrologic
group.
41
Green-Ampt Infiltration
The hydrologic soil groups, as shown in Figure 3-5, were used to identify the parameters for
Green-Ampt infiltration: saturated hydraulic conductivity, suction head value, and initial deficit.
Each soil group corresponded to a range of saturated hydraulic conductivities, as given in the
SWMM manual (James et al., 2010). The midpoint of each range of conductivities was used and
a weighted average was calculated based on the representative areas of soil groups A and D
within each subcatchment. These conductivities were then matched with a suction head value,
from the SWMM manual, that corresponded to the appropriate soil type (James et al., 2010). As
described in the SWMM manual, initial deficit was calculated as the difference between soil
porosity and field capacity, assuming completely drained soils. If the initial state were based on
moist or wet antecedent conditions, a lower value should be used (James et al., 2010).
3.3.2 Junction/Conduit Properties
3.3.2.1 Drainage Network Aggregation
The 17 subcatchments, as described in 3.1.2 Study Area Discretization, were drawn into the
SWMM model, as shown in Figure 3-6.
Subcatchments
*
Junctions
Conduits
Rain Gage
FIGURE 3-6. STUDY AREA MODELED WITHIN SWMM
Source: SWMM (U.S. EPA, 2015)
42
Due to time and information availability constraints, the drainage system within each
subcatchment was aggregated for simplification. For each subcatchment a single inlet junction
was identified to represent the point where all the precipitation within that subcatchment area
would flow to. This was based on an analysis of elevation, where the intersection of the flow
path and the drainage network was identified. This inlet junction was then connected to another
junction using a conduit, to represent the stormwater drain pipe. The multitude of pipes were
aggregated and grouped based on diameter. Two to three of the largest diameter sizes were
selected to represent all conduits in the subcatchment. A weighted average was applied to get a
representative pipe length and slope for each of the pipe diameters used to represent the conduits
in the subcatchment. The study site, as conceptualized in SWMM, is shown in Figure 3-6 and
Figure 3-7 with and without background aerial imagery.
-
Subcatchments
unctions
0 Codut
Rain Gage
FIGURE 3-7. STUDY AREA IN SWMM BROKEN INTO SUBCATCHMENTS, JUNCTIONS (I.E. CURB
GUTTERS OR MANHOLES) AND CONDUITS (I.E. SWALES, CULVERTS OR PIPES).
Source: SWMM (U.S. EPA, 2015)
3.3.2.2 Junction Properties
Data for the junction properties was procured from the city of Malden's Engineering
Department. The office provided two GIS data files for the city's stormwater drainage system.
One file contained the drainage location and lengths of conduits in the system, and the other
contained junction locations (such as manholes, curb gutters and catch basins). These datasets
43
included attributes for the conduits and junction, which described the dimensions and
engineering specifications of the components within the drainage system.
The junction properties that were entered into the SWMM model were invert elevations and
maximum water depth. SWMM uses the invert elevations of both the inlet and outlet nodes to
calculate conduit slope for flow routing equations. From the GIS data files, the locations of curb
gutters were identified to define inlet locations where the surface runoff would enter the drainage
system. In addition, each file contained junction information such as invert and rim elevation
(the surface elevation above the junction), which were used to determine the maximum water
depth during high-flow events. The file, however, had gaps in the junction file where data points
were not entered for either invert or rim elevation. Without the rim elevation there was no point
of vertical reference, preventing these invert elevations from being used.
As an alternative method, the invert elevations were calculated from the conduit slopes, which
were provided in the GIS data file for conduits. For each conduit diameter, average slope, total
length of pipe and percentage of total representation was summarized. Each subcatchment was
then represented by its most prevalent pipe size and corresponding average slope. The invert
elevation was then back calculated starting with the ground elevation and subtracting slope, pipe
diameter and a safety distance (dc,,), as follows:
Ei = Ef - D - dcov
Where: Ei invert elevation (ft)
Eg ground elevation (ft)
D = pipe diameter (ft) (12 ft pipe diameter was used, if the pipe was a larger diameter it could be
entered as maximum cross sectional depth)
dcov = distance from the top of the storm conduit to the ground surface elevation (4 feet was used
based upon standard minimum of 2 feet)
The maximum water depth for each junction could be calculated as the distance from the pipe's
invert elevation to the ground surface, or entered as a zero to represent height of the highest
connecting node. A zero was entered as the maximum water depth in order to force the
maximum depth to be the distance from the invert to the top of the highest connecting link.
3.3.2.3 Conduit Properties
As described above, data for the conduit properties was procured from the city of Malden's
Engineering Department. The data file for conduits contained the locations and lengths of each
44
pipe in the system, as well as attribute information necessary for calculating the conduit
properties in the SWMM model. The conduit properties included conduit shape, maximum
depth, conduit length, and roughness. The pipe shapes, maximum depth and conduit length were
values taken from the attribute tables of the GIS files, provided by the city of Malden (Stead,
2015). The conduit shape was primarily circular for all the pipes branching off of the main
trunkline. Within the study area, the majority of pipes were 12 feet in diameter, for those that
branch off the main trunkline. However, the conduits that formed the trunkline were rectangular
in shape. Their sizes ranged from 6 foot width by 6 foot height to 7 foot width by 12 foot height.
The parameter for maximum depth was equal to the cross sectional height of each conduit, or the
diameter in the case of circular pipes. The length of each conduit was calculated separately for
each subcatchment, as follows:
Lconduit,x -
'tot,x
n
, where
is the length of the conduit (of a given diameter, x) entered into the
SWMM model
ltot,x = total length of all pipes with a diameter of x
n = number of conduits within that subcatchments
Lconduit,x =
Finally, the Manning's roughness coefficient (n) was taken from a table of n values for closed
conduits, in section 24.7 of the SWMM manual (James et al., 2010). In the GIS attribute table,
the pipe materials varied, and thus an average was used that was on the high end for the ranges of
values given.
3.3.3 Rain Gage Data
In order to run a simulation, each subcatchment was required to have rainfall data, entered as a
Rain Gage in the SWMM model. Precipitation data can be loaded as an external file or manually
added as a time series or loaded as an external file. For the study area, four storm hyetographs
were identified and entered manually as time series. Input properties for the precipitation data
included date, time interval and precipitation magnitude, which was entered as volume in inches.
'Refer to Appendix B for discussion of drainage pipe attributes.
45
There are a variety of precipitation sources, which vary in time interval frequency and distance
from the study area. Three main sources of data were identified: NOAA's hourly data at Boston
Logan International Airport, USGS's 15-minute data in Cambridge, and the Boston Water and
Sewer Commission's (BWSC) 5-minute data in Charlestown. However, the USGS and BWSC
files had gaps in data and the years of data available was shorter than NOAA's data. Thus, using
either source would have introduced too much error. Instead, NOAA's precipitation data was
used as the primary source of data, even though the time intervals were 1 hour, which is longer
than preferred for a short rain event.
Rain gage data was obtained for the years 2001 to 2013. From this dataset the four largest storms
were identified, as shown in Table 3-3 below. The incremental, hourly rainfall information was
entered into SWMM as a time series.
TABLE 3-3. TOTAL STORM DEPTH VALUES
Start/End Date
Month
Year
Total Rain (in)
1-2
14-15
April
October
2004
2005
5.47
3.69
13-14
May
2006
7.2
6.98
March
2010
13-15
Source: National Climatic Data Center (NOAA, 2015)
46
4. Results
A total of eight simulations were run with the SWMM model. For each of the four storms, a
simulation was run for both infiltration methods: Curve Number and Green-Ampt. The options
specified for each simulation included type of infiltration model, routing model and the start and
end date and times for each storm. Figure 4-1 shows an example Simulation Options dialogue
box for one of the storms using Green-Ampt infiltration method.
Miscellaneous
Process Models
Amlow Ponding
Rainfall/Runoff
Rainfall Dependent Lq
r Report Control Actions
Snow Melt
E Report kiput Summary
Groundwater
Minimum Conduit Slope
F1 low Routing
0
(
[
Water Quality
Infillration Model
0 Horton
(D Modified Horton
RoutingModel
0
Steady Flow
* 1nmnatic Wave
* Green Ampt
e Curve Number
Dynamic Wave
FIGURE 4-1.SCREEN SHOT OF SIMULATION OPTIONS DIALOGUE BOX
Source: (U.S. EPA, 2015)
Results from a simulation can be displayed and analyzed in tables, on graphs, in map views and
within status reports. Table results provide tabulated values for infiltration, runoff, outflow and
storage at each 15-minute interval. Graphs can be created and viewed in SWMM as a profile,
a
time series or scatter plot. For each graph, an object (i.e. subcatchments, node, system, etc.) and
variable (i.e. precipitation, runoff, infiltration, etc.) can be selected and viewed in a graph form.
For this report, table results were exported and graphs were created from the tabular data in an
external program. Results can also be viewed in map form, which allows the user to select
values of certain input parameters and simulation results and view them on the Study Area Map.
be
For a given setting on the Map Browser, the subcatchments, nodes and links of the map will
the storm
colored according to their respective Map Legends and change at each time interval for
47
duration. The status report provides details such as a summary of the simulation options in effect,
a list of any errors encountered during the run, and system wide mass continuity errors (James et
al., 2010).
4.1 Rainfall-Runoff
The results from the eight simulations were compiled into a summary table to compare total
precipitation, infiltration loss, surface runoff, peak runoff, and percentage runoff, for each storm
and infiltration method, as shown in Table 4-1. For all four storms, the surface runoff volume
from the study site was greater when the Curve Number infiltration method was used compared
to Green-Ampt infiltration. As can be seen in Table 4-1, Green-Ampt infiltration shows 66% to
68% runoff for all storm events, while Curve Number infiltration produces much higher runoff
rates in the range of 81% to 88%. Similarly, peak runoff is much higher for each storm using the
Curve Number method, except for the October 2005 storm where the peak runoff values are
much closer to each other. Given the large proportion of impervious surface in this watershed,
the Curve Number method results seem likely to be more accurate, but model calibration based
on measured flow rates will be needed to determine which is more accurate.
TABLE 4-1. SUMMARY OF RESULTS FOR BOTH INFILTRATION METHODS FROM ALL FOUR
STORM EVENTS
Svnto
Event
April 2004
October
2005
Peak
Runoff
Infiltration
Loss
(106 gal)
(106 gal)
Green Ampt
81.69
26.11
55.34
214.96
68
Curve Number
81.69
10.97
69.51
256.75
85
Green Ampt
Curve Number
55.11
18.54
36.26
125.51
66
55.11
9.15
44.63
149.12
81
Green Ampt
107.37
35.42
71.71
223.79
67
Curve Number
107.37
12.20
94.05
169.82
88
Green Ampt
104.24
35.07
68.86
219.24
66
Curve Number
104.24
12.16
90.96
160.82
87
InMltron
Method
May 2006
March
2010
Surface
Runoff
Total
Precipitation
48
(106
gal)
(ft3/s)
Runoff
4.2 Runoff and Outflow
As the results for the surface runoff in Table 4-1 show, the difference between the total
precipitation that lands on the surface, infiltration lost to groundwater, and surface storage is
called surface runoff. This runoff value is the amount of water entering the drainage system,
called inflow, as shown in Table 4-2. Outflow from the kinematic wave routing process
represents the total water inflow to the drainage system minus any internal outflow. Thus, runoff
is the water leaving the surface and entering the drainage conduit system, and outflow is the
water leaving the drainage conduit system at the downstream end of the modeled system.
The April 2004 storm event is presented, as an example, to analyze and compare runoff and
outflow from the SWMM model results. Appendix B shows these and other results for all the
storm events. The April 2004 storm had a total rainfall of 5.47 inches over approximately 1.5
days. Results were viewed within SWMM in table form, from which values for runoff and
outflow were extracted and plotted against time to create Figure 4-2 and Figure 4-3, which are
runoff and outflow hydrographs, each showing results for both for the Green-Ampt and Curve
Number infiltration methods.
Both the runoff and discharge hydrographs show that Curve Number Infiltration method creates
higher surface runoff than Green-Ampt method for the Malden River study site, though for the
first approximately 12 hours into the storm, the Curve Number and Green-Ampt methods
produce about the same runoff. Figure 4-2 and Figure 4-3 show a delay in runoff and discharge
from the precipitation for both infiltration methods. The surface runoff shows a smaller delay
than the discharge at the outlet location. These relatively small difference between the rainfall
peaks ad runoff peaks illustrates the short travel time for overland flow in this highly urbanized
setting, and the small difference between the timing of runoff and of outflow illustrates the short
travel time in the conduit system.
49
Runoff vs. Precipitation - April 2004 Storm
0.8
300
mPrecipitation
--
250
0.7
Green-Ampt
-Curve
Number
0.6
200
0.5
0.
150
0.
0
0.3
100
-.
0.2
0.1
0
6,0
10
0
20
30
40
50
Elapsed Time (hours)
FIGURE 4-2. HYDROGRAPH FROM SURFACE RUNOFF PROCESSES
Discharge at Outlet vs. Precipitation - April 2004 Storm
1
45
4Precipitation
-
40
0.9
-Green-Ampt
35
--
-
_
-Curve
--
0.8
Number
0.7
30
0.6
25
0.s .2
a
0.4
'
20
0
15
0.3
--
10
0.2
0
0
0
10
20
30
40
50
Elapsed Time (hours)
FIGURE 4-3. HYDROGRAPH OF DISCHARGE AT OUTLET AFTER TRANSPORT ROUTING
50
....
..
. ...
4.3 Flow Routing
Kinematic wave routing was used for flow calculation within the hydraulic system. Table 4-2
summarizes inflow, external outflow, internal outflow and percent flooded for each storm.
Inflow is the surface water runoff from the previous overland flow routing methods. Thus, inflow
for each storm event in Table 4-2 is equal to the surface runoff in Table 4-1 (for Curve Number
infiltration method). The simulations for only the Curve Number method are chosen to be
represented here in the results section. External outflow represents the water which was routed
through the drainage system and discharged at the outlet location. Internal outflow occurs when
there is surcharge that creates flooding within the system. Surcharge happens when all pipes
entering a node are full or when the water surface at the node is between the crown of the highest
entering pipe and the ground surface (James et al., 2010). Flooding is a special case of surcharge
in which water is lost from the storm water node to the overlying surface system due to the
hydraulic grade line breaking the ground surface (James et al., 2010).
TABLE 4-2. SUMMARY TABLE FOR KINEMATIC WAVE FLOW ROUTING RESULTS
Storm
Event
Apr-04
Oct-05
May-06
Mar-10
infiltration
Method
Curve
Curve
Curve
Curve
Number
Number
Number
Number
Inflow
(10 gl)
69.51
44.63
94.05
90.96
Internal
External
Otflow
Etrw
Outflow
Outflow
(10 gal)10'
ga
l
)
24.93
14.07
27.03
29.61
45.41
31.22
67.79
62.00
Percent
Continuity Flooded
Flooded
Error (%)
-1.49
-2.45
-1.21
-1.11
65
70
72
68
Results from all simulations showed that there was internal outflow, or flooding, within the
drainage network during flow routing. The percentage flooded was calculated as internal outflow
divided by inflow, which exceeded 65% for each storm event. However, due to the combination
of conduit aggregation in the subcatchments and uncertain conduit data, there is little confidence
in the accuracy of these flooding results. The city of Malden's drainage system has not had
flooding events due to these storms (Stead, 2015), thus there is little confidence in the flooding
results and they will not be presented in detail here. Furthermore, while the simulated flooding
may have a small impact on the timing of the water arrival at the study area outfall, it does not
have a significant impact on the total flow rates simulated by SWMM. This was seen in Figure
4-2 and Figure 4-3, which showed both surface runoff rates and system outlet flow rates.
51
4.4 Continuity Error
Continuity errors are quality assurance calculations performed by SWMM. There are two
continuity errors calculated; one for runoff modeling and one for flow routing. These errors are
calculated for the system by summing final storage and total outflow, then subtracting it from the
initial storage plus total inflow (James et al., 2010). The continuity errors should not exceed 10
percent, otherwise the validity of the system and model should be questioned and re-examined.
The value considered to be a good continuity error is often a broad range and is subjective; some
researchers state that less than 1 percent as excellent and less than 5 percent as acceptable
(Dickinson, 2010). Thus, as can be seen in the status reports in Appendix B, the continuity
errors for all eight simulations are below 1.5% for runoff routing. The continuity errors for the
flow routing portion of the model were higher, as shown in Table 4-2, at approximately 2%.
Based on the criteria that less than 5% continuity is acceptable, these errors are well within that
range.
52
5. Discussion
The results from all eight simulations showed that Curve Number infiltration had higher rates of
surface runoff than the Green-Ampt method of infiltration. The flow routing processes for the
transport compartment showed that there was internal outflow, or flooding, in the system for
each storm event; however, due to the uncertainty of conduit aggregation and uncertainty in data,
there is little confidence in the accuracy of these flooding results.
5.1 Infiltration Parameters
As summarized by Rosa (2013), multiple studies have identified sensitive parameters in SWMM,
where small changes in inputs can have a large impact on the model output. Subcatchment
parameters to which SWMM model outputs are generally most sensitive include: percent
impervious, impervious and pervious area depression storage, subcatchments width, and
Manning's n for impervious and pervious areas (Rosa, 2013). Other parameters to which the
model results are sensitive include infiltration parameters, such as saturated hydraulic
conductivity, capillary suction head, and initial soil moisture deficit. These parameters are
physical properties of the soil which are used primarily for Green-Ampt infiltration method. The
process of averaging these parameters may not fully represent the infiltration capacity of the
study area, leading to Green-Ampt method infiltration showing a smaller percentage of runoff.
Thus, the individual soil parameters which were calculated for Green-Ampt may reflect a higher
infiltration capacity whereas CN method values were calculated based on land cover and density.
These land cover characteristics could have excluded some nuances of soil properties and
changed infiltration and storage capacity.
5.2 Junction and Conduit Aggregation
The method of aggregating junctions and conduits could have led to the inaccuracies of the
flooding results from hydraulic flow routing. First, the inlet locations were chosen at the
downslope elevation where the majority of runoff would enter the hydraulic system. Too few
inlet locations to properly model the study area may have been selected. As shown in other
studies, disaggregation of the overall watershed into sub-watersheds can be an appropriate
modeling approach, provided that any storage lost is replaced. In SWMM adding a drainage
network to the model adds storage to the system, allowing attenuation and delays of the
hydrograph peaks. Thus, larger volumes of runoff would enter at each location, causing the
conduits to fill above capacity, surcharge and flood. This may also have affected the timing of
flow, effectively routing water faster through the conduit system than it really flows, which
would concentrate the flow in the downstream trunk conduits faster than in real storms.
53
Adjustment of this aggregation process would help to refine the model, which can be done by
adjusting the subcatchment width or varying slope or roughness (James et al., 2010).
Second, calculation of invert elevations may have introduced inaccuracies in representing the
hydraulic system. There were instances where data was lacking or was uncertain, such as no
elevation data entered for either invert or rim elevations. Without rim elevations as a vertical
reference point, invert elevations were instead back-calculated using pipe slope information from
the conduit data file, in combination with a DEM file. The pipe sizes representing the majority of
total pipes in the subcatchments were averaged along with the corresponding slope for that pipe
diameter. Within the conduit data file, there were many pipes missing a slope value, which could
have skewed the averages that were entered into the SWMM model. Thus, slope lengths used to
calculate invert elevations could have been overestimated or underestimated. An increase in
velocity of flow through the pipes could have caused large volumes of water to build up faster
within the junctions and conduits. An important next step would be to obtain accurate invert
elevations either by field measurement or confirmation from as-built schematics and enter the
actual measurements into the junction parameters.
Lastly, the method of back calculating invert elevations used a 4 foot cover distance, a depth
below the ground at which pipes were buried. These guidelines were based on a different city's
design standards and may not reflect the stormwater drainage design guidelines for the city of
Malden.
Detailed and accurate calculations for individual pipes and conduits were not performed since
not all of pipes in the system were represented. There is little value in adding precise parameters
to a system whose structure was loosely modeled and lacked good reliable data for all pipes. I
recommend that such fine tuning be added once data gaps are filled in and any data uncertainty is
resolved.
5.3 Single-Sided Verification
Due to lack of rain events during the spring semester, field data for storm events could not be
collected to calibrate the model, which would be an ideal way to validate the model. As an
alternative, a single-sided verification method was employed to determine if the model
simulation outputs were reasonable for the study area and showed characteristics expected from
m highly urbanized watersheds, as compared to less urbanized watershed in the same area. The
discharge from the study area outlet was compared to flow from the Aberjona River; a less
developed watershed. The natural hydrology of a watershed changes as it is developed,
increasing in impervious surfaces and consequently higher flow velocities and volumes.
Increased watershed imperviousness causes a greater fraction of rainfall to be converted to
54
surface runoff, causing runoff to occur more quickly, and peak flows to become larger (CWP,
2003). Early discharge arrival and high peak flow for an urban watershed is illustrated in Figure
5-1 and should be shown in the results of this verification method.
L899e
Storm
/
Pre- developmenI
Hogher and M eRapid Peak Drachasge
Post devoiopmenl
Small
Staff"
Mote Aunot Volumer
LO3vgh Anlid Less
-
TIME
FIUE5-1. ALTERED HYDROGRAPH IN RESPONSE TO URBANIZATION
Source: (CWP, 2003)
The Aberjona River drains into the Upper Mystic Lake, eventually flowing into the Mystic River
and converging with the Malden River. The drainage area of the USGS gage 01102500 is 23.9
square miles. This gage has a data collection platform with hourly updates for river stage and
velocity sensors (U.S. Geological Survey, 2015). River discharge, in cubic feet per second, was
obtained for the April 2004 storm and compared to the SWMM model results for the same storm
duration. The Malden River (SWMM model) and Aberjona River were each normalized by their
drainage area to put the flow rates on a comparable basis, as follows:
Q
where
QA= -,
A
Q
A
flow per unit area (ft 3 /s/ft2 ), normalized by the drainage area
=discharge or runoff (ft 3/s)
=
=
drainage area (ft2
)
QA
55
Comparison of Flow - Maiden River Study Area vs. Aberjona River
3131/2004 19:00 - 4/2/2004 7:00
1.20E-05
r
-Aberjona
River Flow
1.00E-05
I
--
8.OOE-06
-SWMM
4!
6.OOE-06
Model Results
-
-
4.00E-06
"
'
2.OOE-06
ill
II1i
Al
6
0.OOE+00
0
20
60
40
80
100
12 0
Elapsed Time (hours)
FIGURE 5-2. NORMALIZED FLOW FOR STUDY AREA OUTLET AND ABERJONA FLOW GAGE
(01102500) USING APRIL 2004 STORM PRECIPITATION DATA
Figure 5-2 shows normalized flow for the SWMM model results at the study area outlet
compared to the Aberjona River for the April 2004 storm. Discharge from the study area shows
peak flow rates occurring earlier into the storm and at larger rates than the Aberjona River flow.
Figure 5-2 shows a pattern very similar to that shown in Figure 5-1, providing confidence that
the SWMM representation of the study area is properly reflecting the urbanized character of the
watershed.
56
6. Summary and Conclusions
6.1 Summary
This thesis was a first attempt to model the response of the Malden River watershed area to
rainfall. The River is in an extremely urbanized environment, with inflow to the river arriving
primarily in the form of stormwater runoff from the surrounding urban areas, with very little
baseflow between storms. This urban rainfall-runoff is heavily polluted with chemical
contaminants, bacteria, and various solid particles that are washed into the stormwater drainage
network and discharged into the Malden River. For such an urbanized study area with highly
impervious surfaces, EPA's Storm Water Management Model (SWMM) was chosen and
determined to be a reasonable choice for hydrological modeling.
A section of the city of Malden was selected as the study area, with an outlet location as shown
in Figure 3-2. The study area extent was delineated based primarily on the drainage network,
with adjustments around the perimeter based on topography. Once the study site was defined, the
area was further divided into 17 subcatchments. Within these subcatchments the networks of
drainage pipes were aggregated to be represented by no more than four conduit connections.
These subcatchments were drawn into the SWMM model, and the subcatchment, junction, and
conduit properties were estimated and entered. These properties were required to tailor the runoff
processes and flow routing equations for the study area. The infiltration methods used for the
runoff processes were Green-Ampt and Curve Number methods. Both of these methods required
input parameters pertaining to the physical characteristics of the soil and surface impervious
values. Most of this data was obtained from the Massachusetts GIS Office in the form of GIS
data files, which were analyzed and manipulated with ESRI's ArcMap software. The data files
required were impervious surfaces, soil type, landuse type, and a raster digital elevation model
(DEM) map. Once in the storm drain conduit system, the flow routing equation used for this
model was kinematic wave routing. The hydraulic system of junctions and conduits required
input values obtained from the city of Malden Engineering Department, including invert
elevations and slope, shape, and diameter of the pipes.
NOAA's hourly data at Boston Logan International Airport was obtained and the four largest
storm events in the time period of 2001 to 2013 were identified (NOAA, 2015). The hyetographs
for these four rainstorms were entered, as a time series, into a rain gage in the SWMM model.
Eight simulations were run; both Curve Number and Green-Ampt infiltration methods for each
of the four storms.
57
The results of the simulations showed that for a given storm the Curve Number method predicted
a higher runoff (less infiltration) than the Green-Ampt method. The flow routing step showed
there was a large amount of internal flooding in the system. More than 65 percent of the runoff
that entered the drainage system reached capacity and flooded internally for each of the four
storms.
6.2 Conclusions
Overall, the model seems to represent the study site reasonably well for the runoff processes on
the land surface, producing a runoff response to precipitation that is typical of a highly urbanized
area. Increased imperviousness causes larger runoff volume and higher velocity, compared to a
less developed watershed, which was shown in the runoff hydrograph in Figure 4-2.
Furthermore, the single-sided verification method, as illustrated in Figure 5-2, demonstrated that
the model produced the expected behavior of an urbanized area, with earlier discharge arrival
and higher peak flow during a storm event.
The flow routing processes modeled in SWMM showed internal flooding. The methods of
aggregating the hydraulic system within each subcatchments, and calculating conduit and
junction properties, may not have reflected the true parameters of the system. Filling in gaps
within the stormwater drainage data file, which was incomplete in defining invert and rim
elevations for some locations and slope for some conduits, is critical to creating a more realistic
representation of the study area. Furthermore, the pipe diameters were listed in one column, but
possibly with more than one unit of measurement (as described in Appendix B). In addition to
filling in some of these gaps, the model and GIS data files should be cross checked with as-built
drawings of the city's storm water drainage system. Further analysis is recommended to make
adjustments and improvements in representing the hydraulic system of the study area.
Once the proper adjustments are made and the flow routing process shows minimal internal
flooding during a storm event, the model should be calibrated. As mentioned, due to lack of rain
events during the spring term, field data could not be collected to calibrate the model. Calibration
is essential to validate the model as an accurate representation of the Malden River drainage area.
Once the hydrology and hydraulic parameters are validated in the flow routing portion, SWMM
also has the capability to model pollutants routed through a system and measure contaminant
flow at the discharge. This water quality flow routing behaves like a continuously stirred tank
reactor as the water travels through the pipe network. As described in the manual, SWMM can
model any of the following water quality issues (James et al., 2010):
0
dry-weather pollutant buildup over different land uses;
58
*
"
*
*
*
pollutant washoff from specific land uses during storm events;
direct contribution of rainfall deposition;
reduction in dry-weather buildup due to street cleaning;
reduction in washoff load due to LIDs and BMPs;
entry of dry weather sanitary flows and user-specified external inflows at any point in the
drainage system;
* routing of water quality constituents through the drainage system; and
* reduction in constituent concentration through treatment in storage units or by natural
processes in conduits (pipes and channels).
Thus, there are many additional modeling capabilities that SWMM offers which were not
addressed in this thesis. The hydrology and hydraulics portion of the model should be refined
further, as discussed above, before relying on the water quality modeling capabilities of SWMM.
If the water quality capabilities of the SWMM model for the Malden River watershed were
developed, SWMM could be sued to support work such as that performed by Smith (2015) and
Jacques (2015). Smith (2015) used the Select Model created by the Water Environment Research
Foundation to analyze BMP options for the city of Malden to mitigate water quality of the
discharge. This evaluation could be enhanced using the model since SWMM allows for more
customization to a given study site. Using SWMM to simulate the amount of microbial
contamination entering the Malden River during storms would contribute to effort such as
Jacques' (2015) microbial risk assessment of the Malden River.
This thesis contributes insight to the hydrologic behavior of the Malden River watershed through
a tailored urban rainfall-runoff model. This is a foundation on which more robust analyses can be
built to assess water quality and pollutant loadings of the River. Further analysis will aid in the
identification and development of remediation strategies to improve the health of the Malden
River ecosystem and support safe recreational use.
59
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Addison-Wesley Publishing Company.
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Khweis, M. (2015). OrganicSediment Analysis and Distributionon the Malden River (Senior
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60
MADEP. (2013). Massachusetts Year 2012 IntegratedList of Waters. Commonwealth of
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http://www.mass.gov/eea/docs/dep/water/resources/07v5/12list2.pdf
MADEP. Massachusetts Surface Water Quality Standards, 314 CMR 4.00 (2014). Retrieved
from http://www.mass.gov/eea/docs/dep/service/regulations/314cmrO4.pdf;
http://water.epa.gov/scitech/swguidance/standards/wqslibrary/upload/mawqs figures tables.pdf
MWRA. (2015, March 5). Malden Watershed SSO Locations.
Nangle Associates. (2014). Malden River Outfalls.
Natural Resources Conservation Service. (1986, June). Urban Hydrology for Small Watersheds
TR-55. U.S. Department of Agriculture.
NOAA. (2015). National Climatic Data Center. Retrieved May 3, 2015, from
http://gis.ncdc.noaa.gov/map/viewer/#app=cdo
Oehmke, T. (2015). Potentialfor Sediment Re-suspension and Transportin the Malden River
(Senior Capstone Project). Massachusetts Institute of Technology.
Rosa, D. (2013). Post-audit Verification of the Model SWMMfor Low Impact Development.
University of Connecticut.
Rossman, L. A. (2010). Storm water management model user's manual, version 5.0. National
Risk Management Research Laboratory, Office of Research and Development, US
Environmental Protection Agency. Retrieved from
ftp://1 52.66.121.2/Oktatas/Epito2000/KozmuhalozatokTervezeseSP2/swmm/epaswmm5 manual.pdf
Singh, V., & Woolhiser, D. (2002). Mathematical Modeling of Watershed Hydrology. Journalof
Hydrologic Engineering, 7(4), 270-292. http://doi.org/10.1061/(ASCE)10840699(2002)7:4(270)
Smith, M. (2015, June). Evaluation of Stormwater BMP Alternatives in the Malden River
Watershed. Massachusetts Institute of Technology, Cambridge, MA.
Soil Conservation Service. (1992, February). Technical Release 20 Computer Program for
Project Formulation Hydrology. U.S. Department of Agriculture. Retrieved from
ftp://ftp.wcc.nrcs.usda.gov/wntsc/H&H/other/tr20userManual.pdf
Stead, G. (2015, February). Personal Communication, Malden City Drainage.
61
Sylman, S. (2015). Inorganic Contaminants in the Sediments of the Malden River: Distributions
and Associated Risks (Senior Capstone Project). Massachusetts Institute of Technology.
Tsihrintzis, V. A., & Hamid, R. (1998). Runoff quality prediction from small urban catchments
using SWMM. HydrologicalProcesses, 12, 311-329.
U.S. Army Corps of Engineers. (2008). Malden River Ecosystem Restoration:DetailedProject
Report & EnvironmentalAssessment.
US Army Corps of Engineers. (2013, December). Hydrologic Modeling Systems HEC-HMS,
User's Manual.
U.S. Department of Agriculture. (1999). Soil Taxonomy. Retrieved from
http://www.nres.usda.gov/lnternet/FSE DOCUMENTS/nres142p2 051232.pdf
US Department of Commerce, N. (2015, April 15). National Geodetic Survey - Vertical Datums.
Retrieved April 15, 2015, from http://www.ngs.noaa.gov/datums/vertical/
&
U.S. EPA. (2015). Storm Water ManagementModel Version 5.1. U.S. EPA.
US EPA, 0. (2014, September 9). Sanitary Sewer Overflows and Peak Flows [Overviews
Factsheets]. Retrieved April 29, 2015, from http://water.epa.gov/polwaste/npdes/sso/
U.S. Geological Survey. (2015). Aberjona River at Winchester, MA. Retrieved May 4, 2015,
from http://waterdata.usgs.gov/nwis/uv/?site no=01102500&agency cd=USGS
62
Appendix A: Input Files and Tables
Subcatchment Names
o
E
Kirs
Ma
Subcatchments
Junctions
Conduits
Rain Gage
63
Overland Flow Calculations
Long
Path
Weighted
Avg. (m)
Overland
Flow (ft)
Area
(ft^2)
width
(ft)
45
86
51.2
168.0
1.78E+06
10601
34
27
50
34.4
112.9
9.80E+05
8681
NewmanUp
25
45
55
39
128.0
2.55E+06
19918
KirsteadE
20
65
120
58
190.3
Short
Path
Short
Path
(1)
(2)
BellE
40
FerryE
Subcatchment
Name
1.44E+06
J~'U~A $~~O5
7585
633
Roosevelt-mid
20
45
100
46
150.9
1.49E+06
9899
SalemE
20
36
73
37
121.4
6.82E+05
5616
SalemW
24
45
66
40.8
133.9
1.90E+06
14223
MaldenE
40
65
90
60
196.9
1.02E+06
5207
64
Infiltration Parameters
Subcatchment
CN
Drying
Initial
K (just
K
Suction
Initial
Time
(days)
Deficit
perv)
(w/imp)
Head
Deficit
BellE
70
7
0.3
0.12
0.07
9.5
0.23
Ferry E
77
7
0.3
0.36
0.16
8.1
0.24
NewmanUp
76
7
0.3
0.29
0.13
8.9
0.22
Kirstead-E
73
7
0.3
0.14
0.08
9.2
0.23
Roosevelt-mid
84
7
0.3
0.22
0.11
8.9
0.22
SalemE
70
7
0.3
0.22
0.11
8.9
0.22
Salem W
70
7
0.3
0.19
0.10
9.0
0.22
MaldenE
70
7
0.3
0.03
0.04
10.0
0.11
65
Subcatchment Properties
Subcatchment
Area
(Acres)
Area
(ft^2)
width
(ft)
%
Imperv
%
Slope
nimp
nperv
dstore
d-store
perv
imp
(in)
(in)
BellW
58.52
2i549,197
16,054
60
2
04015
0.24
05
0.2
BellE
40.88
1,780,744
10,601
55
2
0.015
0.24
0.05
0.2
Ferry_W
25-53
1,11,90
6,672
73
2
0.D15
0.24
AX5
0.2
Ferry_E
22.49
979,798
8,681
75
2
0.015
0.24
0.05
0.2
Newman Low
49.83
2,170,732
19,460
57
2
0,015
0.24
005
0.2
Newman Up
58.51
2,548,579
19,918
68
2
0.015
0.24
0.05
0.2
KirsteaciW
14.09
1636 6
3848
68
2
&015
0.24
0.05
0.2
KirsteadE
33.14
1,443,420
7,585
75
2
0.015
0.24
0.05
0.2
Roosevlt_E
12.91
562,417
4433
75
2
&015
024
4.05
0.2
Rooseveltmid
34.30
1,493,995
9,899
58
2
0.015
0.24
0.05
0.2
0.24
005
0.2
0.2
RooseveltW
20.81
9K6339
,911
61
2
W015
SalemE
15.65
681,786
5,616
55
2
0.015
0.24
0.05
Salem 'idE
32.19
A0,37
12,281
73
2
A.15
0.24
A05
0.2
SalemW
43.71
1,903,804
14,223
58
2
0.015
0.24
0.05
0.2
Salem Up
29.25
1,273,949
9471
58
2
015
0.24
0.05
0.2
MaldenE
23.53
1,024,988
5,207
85
2
0.015
0.24
0.05
0.2
Maiden W
12.32
536,742
3,087
91
2
0.015
0.24
0.05
0.2
66
Conduit Properties
Subcatchment
Conduit
Max.
Depth
(ft)
conduit
length (ft)
Roughness
C2
12
1906
0.015
C4
12
4362
0.015
C6
12
1781
0.015
B&W
fleRy
04
FerryE
C8
12
2429
0.015
C0
12
3572
0.015
C12
12
2890
0.015
C14
32
1734
0.015
C16
12
1026
0.015
C18
15
2047
0.015
C20
12
729
0.015
C22
12
2860
0.015
C24
8
903
0.015
C26
18
271
0.015
C28
12
3639
0.015
C30
12
100
0.015
C32
12
2669
0.015
SalemUp
C34
12
2468
0.015
Maiden_W
C36
12
453
0.015
NewmanLw
Newman_Low
-
Rbwe"#, E
RooseveltW
Salem midE
Salem
67
Junction Properties
Junction
Conduit
Slope
fl
Ground
Elevation t)
(
Subcatchment
Invert Elev.
(ft)
-.
BellW
J2
0.0080
42.1
26.1
BellE
J4
0.0100
81.7
65.7
FerryW
J6
9.8
-6.2
Newman-Up
Newman Low
J8
0.0109
46.8
30.8
J10
0.0300
28.1
12.1
6.6
-9.4
J12
J14
0.0087
46.4
30.4
J16
0.0074
75.3
59.3
J18
0.0134
107.9
91.9
J20
0.0033
13.2
-2.8
KirsteadE
J22
RooseveltE
J24
JZS
J26
Roosevelt-mid
24.4
8.4
0.0100
13.5
-2.5
9.8
0.0080
47.0
31.0
9.8
J32
J34
SalemE
0.0024
J28
J30
RooseveltW
9.8
0.0080
J36
33.0
9.8
357.: #OI$V
68
17.0
Subcatchment
Conduit
Conduit
S!ope
Ground
Invert Elev.
Elevation (ft)
(ft)
0.0080
52.1
36.1
0.0080
59.1
43.1
J42
0.0071
20.2
4.2
J44
0.0040
35.2
19.2
Junction
Junction
J39
Ground
Invert Elev.
Sa1bmmtd
J46
9.8
J48
6.6
42.
MaLW~
69
Appendix B
Drainage Pipe Attributes: An Issue of Units
The pipe diameters entered into the SWMM model, were taken from the GIS attribute table of
the stormwater drainage network data file. The column, "height," was used to represent pipe
diameter if for round shaped pipes. For rectangular shaped pipes, such as culverts, the height
simply represented the height of the pipe walls. This column did not refer to any units, and the
Engineering Department was contacted for verification that the unit of measurement was feet.
This information was used to enter pipe diameter values as feet into the conduit properties in the
SWMM model. However, some of the values were extremely large and were not realistic for
drainage pipes buried below ground. Many of the larger values were concluded to be in inches,
even though they were in the same column as the pipe diameters in feet. If these larger values
were in inches, they were more realistic pipe sizes that would be found in stormwater drainage
construction. Thus, this conclusion was a reasonable assumption to make.
Introduction to ArcGIS
The computer program ArcMap, was used to manipulate and analyze the various datasets used
for the SWMM model. These layers were analyzed using a variety of geoprocessing tools, which
are called toolboxes. These toolboxes are found in the ArcToolbox window. Each toolbox
performs a general function within with there are often multiple tools, which carry out a specific
analysis within that general function. The toolboxes, and tools, used for this assessment are as
follows:
"
"
The tools for watershed delineation are found in:
o ArcHydro Tools 4 Terrain Preprocessing
The tools used in overlaying impervious surfaces with landuse and soil type:
"
Distances measurements were done using the ruler tool, ( 2).
o
Analysis Tools 4 Extract 4 Clip
GIS Coordinate System
In order to use the GIS datalayers and the results created, a consistent coordinate system had to
be defined for all layers to ensure agreement between length calculations amongst the various
datasets. The geographic coordinate system was chosen to be North American Datum from 1984
with a projected state plane coordinate system from 2001 for the state of Massachusetts using the
linear unit of meters. These were chosen since all of the Massachusetts GIS office data files are
70
provided in this coordinate system which would allow for seamless integration of the various
data layers. However, the drainage shapefile obtained from the Malden City engineering office
was in the state plane coordinate system with the unit of feet. Thus, the project raster tool in the
ArcGIS toolbox was used to convert this file into the appropriate coordinate system.
Vertical Datum Reference
When using invert elevations to calculate the flow routing within SWMM the vertical datum had
to be identified to ensure the town engineering data was referenced from the same height that the
state GIS office used for its elevation data. A vertical datum, or geodetic datum in this case, is
used as an absolute value for elevation to which other elevation points are reference to. NOAA's
National Geodetic Survey is in charge of maintaining vertical datums of which the current one is
North American Vertical Datum of 1988 (NAVD 88) (US Department of Commerce, 2015).
However, these datums get changed and adjusted every few decades as needed. Thus in order to
do engineering work, towns and cities will create their own reference point which will stay
constant. Thus, identifying the datum is necessary to determine if the elevations within the
drainage file from the city of Malden represents the same vertical elevation from the DEM maps
provided by the state GIS office. Within the metadata for the conduit and junction GIS
shapefiles there was no information on the vertical datum reference. When the Engineering
Department was contacted, it was inconclusive as to a specific datum but approximated to be
close to the city of Boston vertical reference. Due to the uncertainty of a definite vertical
reference, the elevation information from the town was not used in combination with the DEM
elevation.
71
Appendix C: Simulation Results and Hydrographs
April 2004
Runoff vs. Precipitation - April 2004 Storm
300
0.8
Precipitation
250
-
0.7
-- Green-Ampt
-Series2
--
0.6
200
0.5
0.4
150
0
0.3
100
0.2
50
0.1
'0
0
10
20
30
50
40
Elapsed Time (hours)
Discharge at Outlet vs. Precipitation - April 2004 Storm
45
1
4Precipitation
~Green-Ampt
-Curve
35
0.8
Number
0.7
#A
30
06
25
0
C
:0.5
.2
u
.
20
0.4
CI
0.3
0.2
0
0
0
0L
--
-CL
15
10
20
30
Elapsed Time (hours)
72
40
50
Infiltration vs. Precipitation - April 2004 Storm
- s - --Precipitation
-Curve
Number
Green-Ampt
0.4
-
-
-
-
-- -0.4
0.3
03
0
C
00
C 0.2
~
--
0.2
0.
0.1
0.
0
0
10
30
20
Elapsed Time (hours)
73
40
50
Status Report
Curve Number Infiltration
Green Ampt Infiltration
Runoff Quantity
Continuity
Total Precipitation
Volume
(acre-ft)
Depth
(in)
Volume
(10A6
gal)
250.71
5.47
81.7
Evaporation Loss
0
0
Infiltration Loss
33.67
0.74
11.0
Surface Runoff
213.33
4.65
69.5
0.09
1.3
Volume
(acre-ft)
Depth
(in)
Volume
(10A6
gal)
Total Precipitation
250.71
5.47
81.69
Evaporation Loss
0
0
Infiltration Loss
80.15
1.75
26.11
Surface Runoff
169.85
3.71
55.34
Final Surface Storage
1.13
0.03
0.37
Continuity Error (%)
-0.167
Runoff Quantity
Continuity
Final SurfaceFiaSufcStrg
Storage
Continuity Error (%)
% Runoff
4.10
-0.157
85
% Runoff
Kinematic Wave Routing
Flow Routing Continuity
Dry Weather Inflow
Wet Weather Inflow
External Outflow
Internal Outflow
Final Stored Volume
Continuity Error (%)
Percentage Flooded (%)
Volume
(acre-ft)
0
Volume
(10A6 gal)
213.327
0
69.516
76.491
24.926
139.34
45.406
0.674
0.219
-1.49
65
74
68
October 2005
Runoff vs. Precipitation - October 2005 Storm
-
160
-
---
-
-
------0.5
---------------
0.45
140
-Precipitation
-Curve
Number
120
0.4
-I--- --I---
0.35
Green-Ampt
0.3
-
100
C
0.25 .2
80
0
0.2
0.
60
0.15
40
0.1
20
0
0.05
0
. -.W
0
5
10
30
25
20
15
35
Elapsed Time (hours)
Discharge at Outlet vs. Precipitation - October 2005 Storm
0.5
25
0.45
Precipitat ion
----
20
- 0.4
Curve Nu mber
--
Green-Am Pt
0.35
-0.3
15
(A
FA 10
0.25
0
0.2
CL
0.15
- 0. 1
0.05
-
-
0
-
0
0
5
10
20
15
Elapsed Time (hours)
75
25
30
35
0
Infiltration vs. Precipitation - October 2005 Storm
0.4
0.4
0.35
-
-Curve
0.3
0.35
Precipitation
Number
0.3
-Green-Ampt
,
0.25
0.25
C
C
o
4.'
0.2
0.2
0.15
0.15
0.1
0.1
0.05
0.05
0
0
0
5
10
15
20
Elapsed Time (hours)
76
25
30
35
0
4.
Status Report
Curve Number Infiltration
Green Ampt Infiltration
Volume
Runoff Quantity
Continuity
Total Precipitation
Evaporation Loss
Infiltration Loss
Surface Runoff
Final Surface Storage
Continuity Error (%)
%Runoff
Volume
(acre-ft)
169.13
0
28.08
136.98
4.29
-0.131
81
Depth
(in)
3.69
0
0.61
2.99
0.09
Volume
(10A6
gal)
55.11
Runoff Quantity
Continuity
Total Precipitation
Evaporation Loss
Infiltration Loss
Surface Runoff
Final Surface Storage
Continuity Error (%)
% Runoff
9.15
44.63
1.40
Kinematic Wave Routing
Flow Routing Continuity
Volume
(acre-ft)
Volume
(10A6 gal)
Dry Weather Inflow
Wet Weather Inflow
0
136.975
0
44.635
External Outflow
Internal Outflow
Final Stored Volume
43.182
14.072
95.793
31.215
1.354
0.441
Continuity Error (%)
-2.449
Percentage Flooded (%)
70
77
Volume
(acre-ft)
169.13
0
56.90
111.29
1.13
-0.115
66
Depth
(in)
3.69
0
1.24
2.43
0.03
(10A6
gal)
55.11
18.54
36.26
0.37
May 2006
Runoff vs. Precipitation - May 2006 Storm
0.7
250
=Precipitation
-Curve
200
0.6
Number
Green-Ampt
U'
0.5
150
0.4
0.3
00
.
0
0
1.
0.2
CL
50
0.1
-0
0
20
10
30
50
40
Elapsed Time (hours)
Discharge at Outlet vs. Precipitation - May 2006 Storm
40
0.5
35
30
-Precipitation
0.45
-Curve
0.4
Number
0.35
-Green-Ampt
IA
-
25
-
-
--------
0.3
te20
0.25 .2
0.2
#A 15
C.
-
0.15 9L
10
0.1
5
0.05
0
0
0
10
30
20
Elapsed Time (hours)
78
40
50
Infiltration vs. Precipitation - May 2006 Storm
------
0.5
_
4-_
0.4
1 0.5
0.45
-
.Precipitation
o.3s
--
--
-
0.3
Curve Number
-Green-Ampt
-
-
-
-
-
0.3
0.25 .2
0 .2s5-----------0 .2
0.35
---
---
0.4
0.2
--
0.15
-
0.1
-
CL
-0.15
-0.1
0.05
0.0
0
10
30
20
Elapsed Time (hours)
79
40
.
--
0.4
50
Status Report
Curve Number Infiltration
Green Ampt Infiltration
Volume
Runoff Quantity
Volume
Continuity
Total Precipitation
Evaporation Loss
Infiltration Loss
Surface Runoff
Final Surface Storage
Continuity Error (%)
%Runoff
(acre-ft)
329.54
0
37.46
288.65
3.86
-0.128
88
Volume
Depth' (10^6
(in)
7.19
0
0.82
6.30
0.08
gal)
107.37
12.20
94.05
1.26
Kinematic Wave Routing
Flow Routing Continuity
Volume
(acre-ft)
Volume
(10A6 gal)
Dry Weather Inflow
Wet Weather Inflow
0
288.649
0
External Outflow
Internal Outflow
82.934
208.044
Final Stored Volume
Continuity Error (%)
Percentage Flooded (%)
1.15
94.061
27.025
67.794
0.375
-1.206
72
80
Runoff Quantity
Volume
Depth
(10A6
Continuity
Total Precipitation
Evaporation Loss
Infiltration Loss
Surface Runoff
Final Surface Storage
Continuity Error (%)
%Runoff
(acre-ft)
329.54
0
108.69
220.10
1.13
-0.116
67
(in)
7.19
0
2.37
4.80
0.03
gal)
107.37
35.42
71.71
0.37
March 2010
Runoff vs. Precipitation - March 2010 Storm
250
0.9
=Precipitation
200
0.8
Curve Number
-
0.7
Green-Ampt
i
0 C'
0.6
150
C
0.s .2
0
C
100
_-
-A
50
0.4
0.
0.3
M
0.2
0.1
0
0
0
10
20
60
50
40
30
Elapsed Time (hours)
Discharge at Outlet vs. Precipitation - March 2010 Storm
40
0.9
-
35
Precipitation
-Curve
0.8
Number
30
-i
-Green-Ampt
t
0.6
-
25
0.5-
20
1
0.4
15
s
0.3
10
0.2
5
0.1
50
6
-0
0"
0
10
20
40
30
Elapsed Time (hours)
81
0
0.
*u
Runoff vs. Precipitation - March 2010 Storm
250
0.9
200
=Precipitation
4
-Curve
0.8
Number
0.7
-Green-Ampt
o5
C
.
0.6
150
S4.
S100
~0.4
I
0.3
0.2
50
b
0.1
0
0
0
10
20
40
30
Elapsed Time (hours
82
50
60
0.
a
Status Report
Green Ampt Infiltration
Curve Number Infiltration
Runoff Quantity
Continuity
Total Precipitation
Evaporation Loss
Infiltration Loss
Surface Runoff
Final Surface Storage
Continuity Error (%)
%Runoff
Volume
(acre-ft)
319.92
0
37.31
279.15
3.66
-0.066
87
Depth
(in)
6.98
0
0.81
6.09
0.08
Volume
Volume
(10A6
gal)
104.24
Runoff Quantity
Continuity
Total Precipitation
Evaporation Loss
Infiltration Loss
Surface Runoff
Final Surface Storage
Continuity Error (%)
% Runoff
12.16
90.96
1.19
Kinematic Wave Routing
Flow Routing Continuity
Dry Weather Inflow
Wet Weather Inflow
External Outflow
Internal Outflow
Final Stored Volume
Continuity Error (%)
Percentage Flooded (%)
Volume
(acre-ft)
0
279.153
90.852
190.265
1.12
-1.105
68
Volume
(10A6 gal)
0
90.966
29.605
62.001
0.365
83
Volume
(acre-ft)
319.92
0
107.64
211.33
1.13
-0.057
66
Depth
(in)
6.98
0
2.35
4.61
0.03
(10A6
gal)
104.24
35.07
68.86
0.37
Appendix D: SWMM Manual Tables
24.2 Soil Characteristics
4
FC
WP
0.437
0.062
0.024
0.105
0.047
0.453
0.190
0.085
3.50
0.463
0.232
0.116
0.26
6.69
0.501
0.284
0.135
Sandy Clay Loam
0.06
8.66
0.398
0.244
0.136
Clay Loam
0.04
8.27
0.464
0.310
0.187
Silty Clay Loam
0.04
10.63
0.471
0.342
0.210
Sandy Clay
0.02
9.45
0.430
0.321
0.221
Silty Clay
0.02
11.42
0.479
0.371
0.251
0.01
12.60
0.475
0.378
0.265
Soil Texture Class
K
TP
Sand
4.74
1.93
Loamy Sand
1.18
2.40
Sandy Loam
0.43
4.33
Loam
0.13
Silt Loam
Clay
K
=
T
=
saturated hydraulic conductivity. in/hr
suction head, in.
=
porosity, fraction
=
field capacity, fraction
wilting point. fraction
FC
WP
=
Source: (James et al., 2010)
84
24.3 NRCS Hydrologic Soil Group Definitions
Saturated Hydraulic
ConductiiIty
Group
A
(in/hr)
Meaning
Low runoff potential. Soils having high infiltration rates
0.45
even when thoroughly wetted and consisting chiefly of
deep. well to excessively drained sands or gravels.
B
Soils having moderate infiltration rates when thoroughly
wetted and consisting chiefly of moderately deep to deep.
moderately well to well-dramed soils with moderately fine
to moderately coarse textures. E.g. shallow loess. sandy
0.30- 0.15
loam.
C
Soils having slow infiltration rates when thoroughly wetted
and consisting chiefly of soils with a layer that impedes
downward movement of water, or soils with moderately
0.15 -0.05
fine to fine textures. E.g.. clay loams. shallow sandy loam.
D
High runoff potential. Soils having very slow infiltration
rates when thoroughly wetted and consisting chiefly of clay
soils with a high swelling potential, soils with a permanent
high water table. soils with a clay-pan or clay layer at or
near the surface, and shallow soils over nearly impervious
material.
Source: (James et al., 2010)
85
0.05 - 0.00
24.4 SCS Curve Numbers
Hydrologic Soil Group
Land Use Description
A
B
C
D
Cultivated land
Without conservation treatment
With conservation treatment
72
62
81
71
88
78
91
81
Pasture or range land
Poor condition
Good condition
68
39
79
61
86
74
89
Meadow
Good condition
30
58
71
78
45
25
66
55
77
70
83
77
39
61
74
80
49
69
79
84
Commercial and business areas (85% impervious)
89
92
94
95
Industrial districts (72/ impervious)
81
88
9i
77
61
57
54
51
85
75
72
70
68
90
83
81
80
79
92
87
86
85
84
98
98
98
98
98
76
72
98
85
82
98
89
87
98
80
Wood or forest land
Thin stand. poor cover, no mulch
Good cover2
Open spaces, lawns, parks, golf courses, cemeteries.
etc.
Good condition: grass cover on
75% or more of the area
Fair condition: grass cover on
50-75% of the area
Residential 3
Average lot size (% Impervious 4
1/8 ac or less (65)
1/4 ac (38)
1/3 ac (30)
1/2 ac (25)
1 ac (20)
Paved parking lots. roofs, driveways etc.5
Streets and roads
5
Paved with curbs and storm sewers
Gravel
Dirt
Source: (James et al., 2010)
86
91
89
24.5 Depression Storage
Impervious surfaces
Lawns
0.05 - 0.10 iches
Pasture
0-20 inches
Forest htter
0-30 inches
0-10 - 0.20 inches
Source: (James et al., 2010)
24.6 Manning's n - overland flow
Surface
n
Smooth asphalt
Smooth concrete
0.011
Ordinary concrete himng
0-013
Good wood
Bick with cement mortar
0-014
Vitrified clay
0.015
Cast iron
0.015
Corrugated metal pipes
Cement rubble surface
0.024
Fallow soils (no residue)
0.05
Cultivated soils
Residue cover < 20%
Residue cover> 20%
0.06
0.17
Range (natural)
0.13
Grass
Short, pranie
Dense
Bermuda grass
0.15
0.24
0.41
Woods
Light underbrush
Dense underbrush
0.40
0.80
0.012
0.014
0-024
Source: (James et al., 2010)
87
24.7 Manning's n - Closed Conduits
Conduit Material
Manning n
Asbestos-cement pipe
0.011 - 0.015
Brick
0-013 -0017
Cast iron pipe
- Cement-lined & seal coated
0-011 - 0.015
Concrete (monolithic)
- Smooth forms
- Rough forms
0.012 - 0.014
0.015 - 0.017
Concrete pipe
0-011 -0.015
Corrugated-metal pipe
(1/2-m. x 2-2/3-rn. corrugations)
- Plain
- Paved invert
- Spun asphalt lined
0.022 - 0.026
0.018 - 0.022
0.011 - 0.015
Plastic pipe (smooth)
0.011 - 0.015
Vitrified clay
0.011 - 0.015
0.013 - 0.017
- Pipes
- Liner plates
Source: (James et al., 2010)
88
24.8 Manning's n - Open Channels
Mannig a
Channel Type
Lined Channels
0.013 -0.017
Asphalt
0.012 - 0.018
-Bick
0.011 - 0.020
-Concrete
- Rubble or riprap
0.020 - 0.035
- Vegetal
0.030-0.40
Excavated or dredged
0.020 - 0.030
-Eartht straight and uniform
-
Earth winding, fairly unform
0.025 - 0.040
-
Rock
0.030 - 0.045
0.050 - 0.140
- Unmaintained
Natural channels (minor streams. top width at
flood stage
100 ft)
-
Fairly regular section
0.030 - 0.070
-
Irregular section with pools
0.040 - 0.100
Source: (James et al., 2010)
89