GIS APPLICATION FOR ASSESSMENT OF LOW-IMPACT
DEVELOPMENT EFFECTS ON STORMWATER RUNOFF
SEYYED MOHAMMAD REZA ESLAMI
A Project Report Submitted In Partial Fulfillment Of The
Requirements for the Award Of The
Degree of Master of Science (Geoinformatics)
FACULTY OF GEOINFORMATION SCIENCE AND ENGINEERING
UNIVERSITI TEKNOLOGI MALAYSIA
June 2010
iii
DEDICATION
To my dearest father and mother, for their love, compassion, understanding, and endless
support of my academic and professional career. I owe you everything.
To my sister, Elham, and my brother, Ahmad, thank you for cherishing my life.
To my uncles and aunts for their lovely encourage.
iv
ACKNOWLEDGEMENTS
I would like to express my deep and sincere gratitude to my supervisor,
Associate Professor Mohammad Nor Said, Head of Geoinformatics, Faculty
Geoinformation and Science Engineering of Universiti Teknologi Malaysia. His wide
knowledge has been of a great value for me.
I wish to acknowledge my friend Noradila Rusli for her valuable data and
friendly help. Without her software (XPSWWM), I couldn’t fulfill my project.
Last and but not least, thanks to all my friends, Hossein, Mohammad, Twins,
Ashkan, Fadi, Hassanein, Amir, Javad and Farhang in College 16, xb2. I had great time
with them during two years living in Malaysia. My appreciation also goes to my friends
Roozbeh Zarei and Ali Monemi. I never forget their great cuisine. There is no such
meaningful word than ….. Thank You So Much.
v
ABSTRACT
The growing urbanization is typically associated with increasing stormwater
runoff and non point source pollution. Low Impact Development (LID) is extended as a
new approach for stormwater management. LID components are utilized as
supplementary devices besides conventional drainage system in urban areas to decrease
the runoff and remove the non point source (NPS) pollution from the stormwater. In the
previous studies, various hydrologic models are driven to calculate the volume of
stormwater and NPS pollution. In addition, some prior literatures discussed the LID
effects on the quality and quantity of stormwater runoff but most of those researches are
restricted to LID site design and LID landscape. In this project, a fuzzy GIS model based
on the LID site design criteria and hydrology principles introduced to find the suitable
areas for LID components in the study area. To evaluate the accuracy of GIS fuzzy
technique, a dynamic rainfall-runoff simulation model (SWMM) is exploited to derive
the peak flows of conventional drainage system. The comparison between the results of
two models, illustrates the accuracy of fuzzy criteria and their weights. Also, analyses
are performed to calculate the effects of LID on amount of runoff and NPS pollution. A
simple GIS model is used to estimate the NPS pollution in the study Area. According to
experimental projects, the LID component effects on pollution and amount of runoff are
calculated by the removal fraction. The final results show LID is a very effective in
removing NPS pollution and runoff stormwater although its capability to absorb runoff
in flash flood isn’t very reliable.
vi
ABSTRAK
Urbanisasi tumbuh biasanya berkaitan dengan peningkatan limpasan stormwater dan
pencemaran titik sumber bukan. Kesan Pembangunan Rendah (LID) diperpanjang sebagai
pendekatan baru untuk pengurusan stormwater. bahagian LID dimanfaatkan sebagai tambahan
selain sistem drainase konvensional di daerah perkotaan untuk mengurangkan runoff dan
memadam sumber bukan point (NPS) pencemaran dari stormwater tersebut. Dalam kajian
sebelumnya, berbagai model hidrologi terdorong untuk menghitung volume stormwater dan
pencemaran NPS. Selain itu, beberapa literatur sebelumnya membahas kesan LID pada high dan
kuantiti limpasan stormwater tetapi sebahagian besar kajian tersebut dihadkan untuk desain
halaman LID LID dan landskap. Dalam projek ini, model GIS fuzzy berdasarkan kriteria desain
halaman LID dan prinsip-prinsip hidrologi diperkenalkan untuk mencari daerah yang cocok
untuk bahagian LID di daerah kajian. Untuk menilai ketepatan teknik fuzzy GIS, curah hujanlimpasan model simulasi dinamik (SWMM) dimanfaatkan untuk menurunkan puncak arus
sistem drainase konvensional. Perbandingan antara keputusan dua model, menggambarkan
ketepatan kriteria fuzzy dan berat mereka. Juga, analisis dilakukan untuk menghitung kesan LID
pada jumlah limpasan dan pencemaran NPS. Model GIS mudah digunakan untuk
menganggarkan pencemaran NPS di Daerah kajian. Menurut projek percubaan, kesan bahagian
LID mengenai pencemaran dan jumlah limpasan dikira oleh fraksi penghapusan. Keputusan
akhir menunjukkan LID adalah sangat berkesan dalam menghapuskan pencemaran NPS dan
stormwater limpasan walaupun kemampuan untuk menyerap limpasan banjir kilat adalah tidak
sangat bisa diandalkan.
vii
TABLE OF CONTENTS
CHAPTER
1
TITLE
PAGE
DECLARATION
ii
DEDICATION
iii
ACKNOWLEDGMENT
iv
ABSTRACT
v
ABSTRAK
vi
TABLE OF CONTENTS
vii
LIST OF TABLES
xi
LIST OF FIGURE
xii
LIST OF ABBREVIATION
xv
INTRODUCTION
1
1.1
Background
1.2
Problem Statement
1.3
Aim of Study
3
1.4
Objectives
4
1.5
Scopes of Study
4
3
viii
2
1.5.1
Study Area
5
1.5.2
Data
9
1.5.3
Software
9
1.6
Methodology
9
1.7
Significant of Study
11
1.8
Chapter Organization
11
1.9
Summary
12
LITERATURE REVIEW
2.1
Introduction
13
2.2
Stormwater Management
13
2.3
Stormwater Best Management Practices (BMP)
15
2.4
Low Impact Development (LID) Practices
16
2.4.1
2.5
Bioretention
17
None Point Source Pollution
20
2.5.1
Hydrological NPS Models
22
2.5.2
Hybrid Models
23
2.5.3
Geospatial Analysis
25
2.6
LID Effect on Amount of Runoff
25
2.7
Fuzzy GIS Based Model
28
2.8
Summary
30
ix
3
METHODOLOGY
3.1
Introduction
31
3.2
Phase1 (LID Site Suitability Criteria)
33
3.3
Phase 2 (Creating Fuzzy Membership Map)
34
3.3.1
Creating Slope Fuzzy Membership Map
34
3.3.2
Creating Distance From Drainage Network Fuzzy Map
37
3.3.3
Creating Distance From Building Structures Fuzzy Map
39
3.3.4
Creating Distance From Drainage Point Fuzzy Map
41
3.3.5
Convex Combination
43
3.4
3.5
3.6
Phase3 (Exploit XPSWWM To Evaluate The Fuzzy GIS Based
Model Accuracy)
47
3.4.1
Storm Selection for Event Modeling
47
3.4.2
XPSWMM Model
48
Phase 4 (Assess the LID Effects on Runoff)
52
3.5.1
LID Hydrologic Analysis Components
52
3.5.2
Determine Percentage of Each Land Use/Cover
55
3.5.3
Bioretention Design Guidelines
59
3.5.4
LID Effects on None Point Source Pollutant Loading
61
3.5.4.1 Estimated Mean Concentrations (EMC)
64
3.5.4.2 Calculation of EMC
65
Summary
66
x
4
5
RESULT AND ANALYSIS
4.1
Introduction
4.2
Analysis The LID Effects on Amount Of Runoff
4.3
Analysis The LID Effects on Non Point Source Pollution
4.4
Summary
67
67
70
81
DISCUSSION AND CONCLUSION
5.1
Introduction
82
5.2
Achievement of Study
82
5.3
Recommendation
84
REFERENCES
85
xi
LIST OF TABLES
TABLE NO
TITLE
PAGE
1.1
Land use of Taman Wangsa Melawati
6
3.1
Fuzzy Parameter Weight
44
3.2.
Catchment's Imperviousness
56
3.3
Bioretention Cell's Volume
60
3.4
Runoff Estimated Concentration Values (mg/l)
64
4.1
Specification of Nitrogen Pollution Raster Map
75
4.2
Specification of Phosphorus Pollution Raster Map
75
xii
LIST OF FIGURES
FIGURE NO
TITLE
PAGE
1.1
Location of Study Area
5
1.2
Land Use Map
6
1.3
DEM of Study Area
7
1.4
3D Elevation Map of Study Area
8
1.5
The Flow Chart of Methodology
10
2.1
Relationship between Impervious Cover and Surface
Runoff
14
2.2
Bioretention
19
2.3
Non Point Source Pollution
21
2.4
Hydrologic Changes after Development
26
2.5
Comparison of the Hydrographs
27
2.6
Linear Membership Functions
29
2.7
Sinusoidal Membership Functions
30
3.1
Methodology Survey
32
3.2
Criteria Were Used For LID Site Suitability
34
3.3
Slope Map
35
xiii
3.4
Fuzzy Membership of Slop
36
3.5
Fuzzy Membership Slope Map
36
3.6
Drainage Network Map
37
3.7
Fuzzy Membership of Distance from Drainage Network
38
3.8
Fuzzy Membership Drainage Map
39
3.9
Fuzzy Membership of Distance from Building Structures
40
3.10
Fuzzy Membership Building Map
40
3.11
Drainage Points
41
3.12
Fuzzy Membership of Distance from Drainage Point
42
3.13
Fuzzy Membership Drainage Points Map
42
3.14
Fuzzy Site Suitability Maps
45
3.15
LID Subcatchments
46
3.16
Stormwater Design
47
3.17
Study Area’s Aspect
49
3.18
Conventional Links and Catchments in XPSWMM
49
3.19
Bioretention Cells and Conventional Drainage System
50
3.20
The Average Peak Flow
51
3.21
Bioretention Cell’s Catchments in XPSWMM
54
3.22
Intersected Catchment LID1 and Landuse Map
56
3.23
Catchment’s Imperviousness
57
3.24
Rainfall-Runoff Data
58
3.25
Rainfall Depth
60
4.1
Bioretention Cell’s Flow Work
68
xiv
4.2
Comparison Between Total and Absorbed Runoff
4.3
Vector and Raster Layer of Runoff Depth in
Catchment LID1
4.4
71
Vector and Raster Layer of Phosphorous EMC
in Catchment LID1
4.5
69
72
Vector and Raster Layer of Nitrogen EMC
in Catchment LID1
73
4.6
Phosphors Pollution Map
74
4.7
Nitrogen Pollution Map
74
4.8
Removal Phosphorous (g) in LID Catchment 1
76
4.9
Removal Nitrogen (g) in LID Catchment
76
4.10
Total Mass of Phosphorus in the Study Area
78
4.11
Total Mass of Nitrogen in the Study Area
79
4.12
Removal Nitrogen
80
4.13
Removal Phosphorus
80
xv
LIST OF ABBREVIATIONS
BMP
Best Management Practice
CN
Carve Number
EMC
Estimated Mean Concentrations
GIS
Geographical Information System
LID
Low Impact Development
NPS
None Point Source
SWWM
Storm Water Management Model
TC
Time of Consideration
1
CHAPTER 1
INTRODUCTION
1.1
Background
Wide ranges of water-related problems are associated with the urbanization
process: water runoff and flooding, pollution and sedimentation, etc. In the process of
population expansion, people need more land for housing and accommodations. The
growing housing demand in urban areas is typically associated with the increased storm
water runoff and flood (Cai, 2003).
Low-Impact Development (LID) is an approach to land development (or redevelopment) that works with nature to manage stormwater as close to its source as
possible. LID employs principles such as preserving and recreating natural landscape
features, minimizing effective imperviousness to create functional and appealing site
drainage that treat stormwater as a resource rather than a waste product. There are many
2
practices that have been used to adhere to these principles such as bioretention facilities,
rain gardens, vegetated rooftops, rain barrels, and permeable pavements (EPA, 2003).
Hence, LID is a comprehensive development approach aims to maintain the predevelopment hydrology system and water quality through small-scale distributed
stormwater controls, in both structural and non-structural ways (Jones, 2004). The
primary objective of LID is to mimic predevelopment site hydrology by using site
design techniques that store, infiltrate, evaporate, and detain runoff.
The use of LID techniques will help reduce off-site runoff and nonpoint resource
pollutant reduction. Pollution washed from the land surface by rainfall is called nonpoint
source pollution. Duda (1993) described nonpoint source pollution including agriculture
activities, urban and industrial runoff, joint sewer overflows and leaks, hazardous waste
dumpsites, septic tank systems, mining and forest harvesting activities, spills,
atmospheric deposition, and hydrologic modification. Pollution from non-point sources
heavily influences water quality in urban creeks and, thus, the urban aquatic
environment.
Nonpoint pollution comes from diverse and hard to identify sources, therefore it
is difficult to estimate. The use of Geographical Information Systems (GIS) provides an
extensive approach to evaluate land use and other mapping characteristics to explain the
spatial distribution of nonpoint source contamination.
3
1.2
Problem Statement
In recent years, the contribution that nonpoint sources make to pollution in the
earth’s surface waters has come under closer consideration. Nonpoint source, or diffuse,
pollution can be defined as pollution that is not associated with a specific location, pipe
effluent discharge, or “point”.
On the other hand, recent developing decreases the pervious surface in the cities
and developed area. The consequence of this new impervious area is increasing the
stormwater runoff and flood. Finding new approaches like LID to manage the
stormwater runoff in cities is still on progress.
GIS is widely used for a broad variety of landuse planning purpose. For nonpoint
source pollution control planning, it has been shown to be very effective in targeting and
prioritizing nonpoint source pollution control resources. In this study, GIS analysis is
discussed in order to find the suitability of locations for LID components (birotention
cells) and determine the amount of NPS pollution that can be reduced by LID practice.
Most of LID projects were done already; ignore site selection and location efficiency to
install LID. By using GIS and deriving a fuzzy GIS based model, the suitability of
places for LID projects was determined. On the other side, calculating run off and non
point source pollution loading to compare before and after LID practice are the other
challenging issues of this project.
4
1.3
Aim
The aim of this study is to assess the effects of LID on the amount of runoff and
its nonpoint source pollution in a specific area by using GIS. The reason of choosing
GIS lies in its ability to effectively represent spatial distribution of a variety of system
parameters such as hydrologic and geographic.
1.4
Objective
There are three primary objectives for this study:
i.
To identify criteria and design geospatial analysis for determining LID site
suitability.
ii.
To compare the result of GIS analysis with SWMM and evaluate the outcome
accuracy.
iii.
To assess the LID effects on quantity and quality of runoff.
1.5
Scope of Study
The scope of this study explains the flowing subsections: Study area, data and
software used for analysis.
5
1.5.1
Study Area
The study area is part of Taman Wangsa Melawati which is located in the state of
Selangor with an area of 7.14 hectares. It is located on the upstream catchment of Klang
River and surrounded by housing estates that are Taman Melawati and Taman Permata.
This area is chosen because of its location reflects the development of urban area which
can impact directly and indirectly to surface water runoff. Figure 1.1 shows the location
of study areas along rivers in the vicinity.
Figure 1.1: Location of Study Area
Source: Google Earth
This residential area consists of two storey terrace houses, shop houses, mosque,
parks and playgrounds, and childcare centre. Table 1.1 shows the details of landuse
while Figure 1.2 depicts landuse categories in the study area.
6
Table 1.1: Landuse of Taman Wangsa Melawati
Land Use
Number
Extent (m2)
Percentage (%)
Two-storey Terrace Houses
242
31,504
43.9
Two-storey Shophouse
10
1,505
2.1
Field
1
12,484
17.4
Mosque
1
1,414
1.97
Playgrounds
1
312
0.43
Road
1
23,515
32.7
Childcare Centre
1
1,509
1.48
71,793
100
Total
Figure 1.2: Landuse Map
7
Overall, the topographic conditions in the catchment area are within the
horizontal height of 67.8 meters in the upstream, up to 57.8 meters at the point of
expression (outlet downstream). The catchment area includes Persiaran Wangsa
Melawati in North, Jalan Melawati 4 in the south, Jalan Wangsa Melawati Wangsa Road
in the east and the west, Siaga 1.
The existing drainage system at Taman Wangsa Melawati is a conventional
system with water runoff from a roof will flow directly into the drain through the gutter
and perimeter drain. Water runoff from residential will be combined with water runoff
from roads and will eventually flow to the outlet. Type of drain consists of a fully
enclosed concrete drain. Figure 1.3 and 1.4 show the appearance of topography.
Figure1.3: DEM of Study Area
Figure1.4: 3D Elevation Map of Study Area
8
9
1.5.2 Data
A contour map of study area with 0.1 meter intervals was exploited to make
DEM in order to train editing and eliciting the drainage network and catchment points.
Landuse map was utilized to bring out the imperviousness. To calculate the runoff, a 120
minutes stormwater design data with 10 years return was used.
1.5.3 Software
Two software were used to accomplish the studies are ArcGIS 9.3 and
XPSWMM. ArcGIS is an integrated collection of GIS software produces that provides a
standard-based platform for spatial analysis, data management, and mapping.
XPSWMM is a comprehensive software package for modeling stormwater, sanitary and
river systems. XPSWMM is used by scientists, engineers and managers to develop linknode (1D) and spatially distributed hydraulic models (2D). It simulates natural rainfallrunoff processes and the performance of engineered systems that manage our water
resources.
1.6
Methodology
Generally, there are 4 phases to complete this study. Figure 1.5 depicts these
phases.
10
IDENTIFY THE CRITERIA
1.
2.
3.
4.
Slope
Distance From Building
Distance From Drainage Network
Distance From Catchment Points
CONVERT MAPS TO FUZZY MEMBERSHIP MAPS
COMBINE THE FUZZY MEMBERSHIP MAPS TO
DETERMINE THE SUITIBILITY LOCATION FOR LID
EXPLOIT THE XPSWMM TO EVALUATE THE FUZZY GIS
BASED MODEL ACCURACY
NO
CRITERIA AND WEIGHTS ARE
ACCEPTABLE
YES
AS
ASSESS THE LID EFFECTS OF RUNOFF
Figure1.5: The Flow Chart of Methodology
11
1.7
Significance of Study
This study carried out to find suitable location for LID components in a small
developed area and assess the LID effects on Runoff. Previous LID works mostly
focused on LID site design and LID construction materials. In this study, a fuzzy GIS
based model was exploited to determine the suitability of areas for LID to increase their
efficiency in removing pollution and runoff. Besides LID site suitability model,
additional analyses were developed to calculate the volume of LID components
(bioretention cells) according to their corresponding catchment in order to increase cost
effective. The study was extended to find LID (which was located in suitable area with
different volumes) effects on runoff.
1.8
Chapter Organization
This research covers 5 chapters which are introduction, literature review,
methodology, analysis and conclusion.
Chapter1: This chapter includes the introduction of research, the problem statement, the
aim of study, the objectives, scope of study, general methodology and
significant of study.
Chapter2: In this chapter, literature related to study were reviewed. These literature
covers stormwater management, LID Component and Fuzzy GIS based
model. The early findings in this chapter are important to provide a useful
guideline to plan and accomplish the flowing chapter.
12
Chapter3: This chapter describes the details methodology to achieve the aims and
objectives which were shown in chapter1. All the explained phases are
including LID site suitability, LID hydrologic analysis components and LID
effects on none point source pollutant loading.
Chapter4: All the analysis carried out to find the LID effects on runoff in the study area
is completely depicted in this chapter. The result of the analyses is display
and discuss.
Chapter5: In this chapter, it concluded the entire research with the achievement. In
addition, some recommendation is proposed to the reader.
1.9
Summary
This chapter highlights the purpose of the proposed study. Aims, objectives and
metrologies are identified and explained. This provides a general framework of the
entire execution of the study area.
13
CHAPTER 2
THE LITERATURE REVIEW
2.1
Introduction
This chapter is to provide a background reading about subjects related to this study.
This includes some background of stormwater management, LID Practices, none point
source pollution and fuzzy logic. This extends to review the previous studies about LID
effects on Runoff as well.
2.2
Stormwater Management
Urban stormwater is rainfall and snowmelt that seeps into the ground or runs off
the land into storm sewers, streams and lakes. It may also include runoff from activities
such as watering lawns, washing cars and draining pools. Historically, flood protection
14
through the controlled release of water is the most commonly used method for
stormwater management. The technology has been applied for that purpose is detention
basin. Moving stormwater away as fast as possible from high area and collecting the
runoff by engineered storage devices are primary steps of detention basin method. This
method controls the rate of discharge in order to protect cities from floods, but it
couldn’t manage the total amount of runoff received from the basin. The effects of
urbanization on the amount of runoff are shown in Figure 2.1.
Figure 2.1: Relationship between Impervious Cover and Surface Runoff
Source: Journal of the American Planning Association
Stormwater runoff in urban areas is also a significant environmental concern for
a multitude of reasons, including sedimentation in waterways, erosion of streambeds and
higher pollutant loads. The ultimate goal of stormwater management is to maintain the
health of streams, lakes and aquatic life as well as provide opportunities for human uses
15
of water by mitigating the effects of urban development. To achieve this goal
stormwater management strives to maintain the natural hydrologic cycle, prevent an
increased risk of flooding, prevent undesirable stream erosion, and protect water quality.
2.3
Stormwater Best Management Practices (BMP)
Impervious surfaces considerably impact the water cycle by both blocking water
from infiltrating into groundwater and by increasing the water volume in surface
waterways. In the past two decades, stormwater management has focused primarily on
the development of best management practices (BMPs). Stormwater BMPs are designed
to delay, store, infiltrate, and treat rain water in an effort to lessen the impacts of urban
development on water quality and quantity. Stormwater should be managed to meet the
following goals :
•
Maintain groundwater quality and recharge
•
Reduce stormwater pollutant loads
•
Protect stream channels
•
Prevent increased overbank flooding
•
Safely convey extreme floods
LID is a new stormwater management practice. It is largely used to control
nonpoint source pollution.
16
2.4
Low Impact Development(LID) Practices
In 1997, the Department of Environmental Resources in Prince George’s County
(PGC), Maryland, introduced Low Impact Development (LID), a comprehensive
approach to stormwater management (PGC, 2000). LID measures provide a means to
address both pollutant removal and the protection of predevelopment hydrological
functions.
Some basic LID principles include conservation of natural features, minimization
of
impervious surfaces,
hydraulic disconnects,
disbursement
of
runoff
and
phytoremediation. The concept of applying LID rests on four key activities: reducing the
impact of development by minimizing impervious areas; implementing on-site
stormwater management systems to collect and treat stormwater; routing stormwater
through the watershed to match post-development times of concentration to predevelopment times of concentration; and educating the public on pollution prevention
and on the maintenance of stormwater management systems (Coffman et al. 1999).
EPA(2000) categorized LID practices as bioretention facilities or rain gardens,
grass swales and channels, vegetated rooftops, rain barrels, cisterns, vegetated filter
strips and permeable pavements perform both runoff volume reduction and pollutant
filtering functions. Bioretention is a low Impact development best management practice
that has the potential to improve stormwater quality from developed areas (Davis et al.
2003). Bioretention is also one of the most extensively used BMPs in LID stormwater
designs (Natural Resources Defense Council (NRDC, 1999).Therefore bioretention
component was selected as the only LID practice in the study area.
17
2.4.1 Bioretention
Bioretention systems are designed based on soil types, site conditions and land
uses. A bioretention area can be composed of a mix of functional components, each
performing different functions in the removal of pollutants and attenuation of
stormwater runoff. They have been shown to considerably reduce influent runoff
volumes and peak flow rates (Davis, 2008). High pollutant removal levels have also
been observed from laboratory and field monitoring of these systems (Li and Davis
2009).
The bioretention concept was originally developed by the Prince George.s
County, Maryland, Department of Environmental Resources in the early 1990s as an
alternative to traditional BMP structures. The principle of utilizing biological properties
for the retention and transformation of nutrients and pollutants is not new – it forms the
basis of agricultural and wastewater treatment practices. However, the concept of
bioretention described here has the advantage of being on site, minimizing the distance
between the source of runoff (e.g. parking lots, roof tops) and the site of control (e.g.
rain garden), unlike end-of-pipe storm water management practices. In effect, the
strategic integration of bioretention facilities into the landscape can result in smaller,
more manageable sub-watersheds (Boyd, 2000).
The development and early adoption of bioretention systems were motivated by a
number of beneficial characteristics of the systems. Aside from their expected efficiency
in reducing storm flows and retaining pollutants, bioretention units can be integrated in
urban development’s and can provide at-source treatment. The installation of these
18
systems is inexpensive and little system maintenance is required after installation (RoyPoirier, 2009).
The main benefits of bioretention are:
• Applicable to small drainage areas
• Good for highly impervious areas, particularly parking lots
• Good retrofit capability
• Relatively low maintenance requirements
• Can be planned as an aesthetic feature
• More efficient pollutant removals
• Increases groundwater recharge
• Minimal installation and maintenance costs
• Reduces strain on stormwater infrastructure
bioretention practice is categorized as bioretention cells (Figure 2.2 a) and
bioretention swales (Figure 2.2 b). Bioretention cells may or may not have an underdrain and are not designed as a conveyance system in comparison bioretention swales
that are designed as part of a conveyance system.
19
a: Bioretention Cells
b: Bioretention Swales
Figure 2.2: Bioretention
Source: Low-Impact Development Technical guidance and manual for Puget Sound
20
2.5
None Point Source Pollution
Non-point source pollution models are basically a description of the hydrologic
rainfall-runoff transformation processes with attached quality components (Notovny, V.
and Chesters, G. 1981). EPA (2003) estimated different sources for NPS pollution.
Figure 2.3 depicts various none point source pollution in an urban area.
•
Excess fertilizers, herbicides, and insecticides from agricultural and residential
areas
•
Oil, grease, and toxic chemicals from urban runoff and energy production
•
Sediment from improperly managed construction sites, cropland and eroding
stream banks
•
Salt from irrigation practices and acid drainage from abandoned mines
•
Bacteria and nutrients from livestock, pet wastes, and faulty septic systems
•
Atmospheric deposition and hydro modification
Over the years, many models have been developed to estimate and control water
bodies. GIS are becoming more useful in modeling water quality because they can
incorporate spatially varying data. There are many instances where GIS have been
incorporated into modeling efforts, and two basic ways that they have been used are: (1)
as a method for deriving input for external models, and (2) as a stand-alone model
(Melancon, 1999). In this study, hydrological NPS models, hybrid models and GIS
Stand-Alone Models were discussed.
k
Figure 2.3: Non point source pollution, source: Long Island Sound
21
22
2.5.1 Hydrological NPS Models
A hydrological model is a simplified simulation of the complex hydrological
system. The major problem in modeling the hydrological processes based on their
physical governing laws is the variability in space and time of the parameters that
control these processes. In the first generation of hydrological models this has been dealt
with by assuming homogeneous properties for the hydrological processes over the whole
catchment area or, in the best cases, for subdivisions of the catchment area (Moore et al,
1993). These models are defined as a mathematical representation of the flow of water
and its constituents on some part of the land surface or subsurface environment
•
SWMM (Stormwater Management Model) – created by EPA – as first
urban runoff quality model. The EPA Storm Water Management Model
(SWMM) is a dynamic rainfall-runoff simulation model used for single
event or long-term (continuous) simulation of runoff quantity and quality
from primarily urban areas. The runoff component of SWMM operates
on a collection of subcatchment areas that receive precipitation and
generate runoff and pollutant loads (EPA, 2010).
•
HSPF (Hydrological Simulation Program-FORTRAN) – created by EPA
Athens Laboratory – simulates both watershed hydrology and water
quality for conventional & toxic organic pollution. HSPF is a one
dimensional stream network model representing the contribution of
sediment, pesticides, and nutrients from agricultural areas and the
resulting water quality conditions at the watershed scale (Novotny, 1995).
•
CREAMS is the Chemicals, Runoff, and Erosion from Agricultural
Management Systems model developed by the U.S. Department of
Agriculture – Agriculture Research Service (USDA-ARS). This model is
23
a field scale model that has sub models for the hydrology, erosion, and
chemistry components (Novotny, 1995)
•
AGNPS (The Agricultural Nonpoint Source) model is the result of efforts
to modify CREAMS to simulate complex watersheds with varying soils,
land use, and management and is supported by the USDA-ARS (Texas
A&M Blackland Research Center, 1999). The emphasis of the model is
on nutrients and sediment, and it allows for comparison of various control
practices implemented in the watershed. As with ANSWERS, AGNPS
uses a distributed approach dividing the watershed into grid cells with
computations done at the cell level (Novotny, 1995).
2.5.2
Hybrid Models
Over the last twenty years the need for hydrological models has shifted from
generating stream flow hydrographs into predicting the effects on water resources of the
actual land use practices (Abbott et al, 1986) or the need to estimate the distributed
surface and subsurface flows (Moore et al, 1993). These needs require better description
of the catchment topography and the distributed properties of the hydrological processes
acting on it.
With the emergence of remote sensing techniques as potential sources of data of
the hydrological processes and the improved capabilities of generating and processing
DEM data, GIS techniques have gained a prominent role in hydrological modeling. This
role has developed from the traditional use of GIS as an interface to the hydrological
models for pre-processing and post processing of data into "rethinking hydrological
24
models in spatial terms so that better GIS-based hydrological models can be created"
(Maidment, 1993).
GIS-hydrologic model linkage offers the potential to address regional or
continental-scale processes- whose hydrology has not been modeled previously to any
significant extent. Some of these models contain:
•
BASINS (Better Assessment Science Integrating Point and Nonpoint
Sources) model was originally released by the US EPA in September
1996 (US EPA, 1999). GIS with national watershed data and several
environmental assessment and modeling tools were blended into one
program.
•
HEC-HMS was developed by the research group at the Center for
Research in Water Resources at the University of Texas by improving an
ArcView project to pre-process various digital spatial data sets to derive
an input file for the Corps of Engineers Hydrologic Modeling System
(HMS) developed by the Hydrologic Engineering Center. The HMS
model provides several options for simulating precipitation-runoff
processes. The HMS model recently added capabilities for continuous
hydrograph simulation over long periods of time, and spatially distributed
runoff computation using a grid-cell depiction of the watershed (US
Army Corps of Engineers, Hydrologic Engineering Center, 1998)
•
SWAT (Soil and Water Assessment Tool) is another product of the
USDAARS (Texas A&M Blackland Research Center, 1999).
25
2.5.3 Geospatial Analysis
GIS nonpoint source assessment methods provide relatively accurate estimates of
pollutant loads and concentrations throughout the stream network of a hydrologic unit.
These methods also provide an efficient way to identify specific locations or regions
where elevated levels of pollutant concentrations may be expected. Use of the GIS
nonpoint source pollution assessment methods also has some logistical advantages that
allow for adaptation to other study areas.
These methods make use of all recorded stream flow and pollutant concentration
data available in the basin and synthesize the data in a consistent and logical way across
the basin. In this study, a stand-alone GIS model was exploited to determine the mass of
removal NPS pollution by Low-Impact development practice in a specific area.
2.6
LID Effects on Amount of Runoff
Impervious surfaces such as roads, parking lots, rooftops and sidewalk reduce
infiltration, filtration, and groundwater recharge. On the other hand, impervious area
increase time of consideration and amount of run off as well. In Figure 2.4
Q represents Amount of Runoff and T depicts time of consideration.
26
Figure 2.4: Hydrologic Changes after Development
Source: PG County, 2000
Typical alterations to the hydrologic regime as a result of development include,
but are not limited to, the following (PG county, 2000);
•
Increased runoff volume
•
Increased imperviousness
•
Increased flow frequency, duration, and peak runoff rate
•
Reduced infiltration (groundwater recharge)
•
Modification of the flow pattern
•
Faster time to peak
•
Loss of storage
LID is usually applied for development area and recognized as post development
movement. Figure 2.5 shows the different effects of development in three conditions,
predevelopment, developments and post development (development area that
incorporated with LID).
27
Although there are conventional stormwater in development area but as
illustrated in Figure 2.5 hydrograph 2 (represent a post development condition) shows
prominent significant increases in runoff volume and duration of runoff in comparison
with hydrograph 1(predevelopment condition). On the other hand, hydrograph 3 depicts
the effects of pro-development condition (LID). Reducing the run off volume and
duration are shown clearly in hydrograth3 in bellow. In Figure 2.5 Q represents Amount
of Runoff and T depicts time of consideration.
Figure 2.5: Comparison of the Hydrographs
Source: PG County, 2000
28
2.7
Fuzzy GIS Based Model
Fuzzy logic theory and fuzzy set theory provide an excellent means for
representing imprecision and uncertainty in the decision-making process and for
defining the reasoning in such process (Zadeh, 1983). Fuzzy logic is basically a multi
valued logic that allows intermediate values to be defined between conventional
evaluations, such as yes/no, true/false, black/white, and so on; it provides a remarkably
simple approach to draw definite conclusions from vague, ambiguous, or imprecise
information (Klir andFoger, 1988). Multi criteria decision making analysis (MCDM) is a
set of alternatives that are evaluated on the basis of conflicting and incommensurate
criteria. At the most rudimentary level, a spatial multi criteria decision problem involves
a set of geographically defined alternatives (events) from which a choice of one or more
alternatives is made with respect to a given set of evaluation criteria. The alternatives are
defined geographically in the sense that result of the analysis depend on their spatial
arrangement. In GIS terminology, the alternatives are a collection of point, line, and
areal objects, attached to which are criterion values (Fazeli, 2010).
The decision maker’s preferences with respect to the evaluation criteria are
incorporated into the decision model. The preferences are typically expressed in terms of
weights of relative of relative importance assigned to the evaluation criteria under
consideration. Broadly speaking, the purpose of criterion weights is to express the
importance of each criterion relative to other criteria. The derivation of weights is a
central step in eliciting the decision maker’s preferences. Given the set of alternatives,
attributes, and associated weights, the input data can be organized in the form of
decision matrix or tables (Malczewski, 1999).
The membership function of a fuzzy set is a generalization of the indicator
function in classical sets. In fuzzy logic, it represents the degree of truth as an extension
29
of valuation. The first step in doing the analysis with fuzzy logic is to build fuzzy
membership map and combine these map in Raster Calculator of Arc GIS. The selection
of a suitable membership function for a fuzzy set is one of the most important activities
in fuzzy logic. It is the responsibility of the user to select a function that is a best
representation for the fuzzy concept to be modeled. Wolfgang Kainz (2006) described
two types of membership functions:
1- Linear membership functions (Fig 2.6)
2- Sinusoidal membership functions. This function has four parameters that
determine the shape of the function. By choosing proper values for a, b, c, and d, Sshaped, trapezoidal, triangular, and L-shaped membership functions can be created
(Figure 2.7).
Figure 2.6: Linear Membership Functions
30
Figure 2.7: Sinusoidal Membership Functions
2.8
Summary
This chapter discusses some important principles such as stormwater
management, BMP, LID and Fuzzy GIS based model which are going to used and
explored in the methodology. Furthermore, this chapter also has covered some literature
reviews related to LID effects on runoff and different models to calculate the runoff and
none point source pollution.
31
CHAPTER 3
METHODOLOGY
3.1
Introduction
The methodology in this study is partitioned into four major tasks:
(1) A GIS-based fuzzy site selection to find suitable places for LID practice
(bioretention cell) according the bioretention site design criteria.
(2) Calculation of current (development condition) volume of runoff and the
capacity of bioretention cell.
(3) Exploit the SWMM model to evaluate the accuracy of fuzzy GIS based
model’s outcome
(4) Finally, compute amount of runoff and NPS pollutant loading under the
assumption that the existing conventional drainage system had been
retrofitted with the LID techniques of stormwater management.
32
The General flow of the whole methodology is shown in Figure 3.1.
IDENTIFY THE CRITERIA
Slope
Distance from Building
Distance from Drainage Network
Distance from Catchment Points
Phase1
CONVERT MAPS TO FUZZY MEMBERSHIP MAPS
Phase2
COMBINE THE FUZZY MEMBERSHIP MAPS TO
DETERMINE THE SUITIBILITY LOCATION FOR LID
EXPLOIT XPSWMM TO EVALUATE THE FUZZY GIS
BASED MODEL ACCURACY
Phase3
NO
CRITERIA AND WEIGHTS ARE
ACCEPTABLE
YES
AS
ASSESS THE LID EFFECTS ON RUNOFF
Figure3.1: Methodology Survey
Phase4
33
3.2
Phase1 (LID Site Suitability Criteria)
Site selection analysis decides the best location for various land uses. The
success of a site selection analysis can be directly attributed to comprehensive project
preparation, along with an objective, methodical, and detailed process. Site selection
analysis can be improved by using GIS. GIS is a suitable tool for site selection since it
has the capability to manage large amount of spatial data that comes from various
sources (Kao et al., 1996).
Because there are no special criteria or limitations for low-Impact development
site selection, GIS-based fuzzy model was used in order to rank suitability of potential
areas and locations for LID practices specially bioretention cell.
According the Bioretention cell Design’s Specifications and Criteria manual,
published by Prince George’s County (2000), potential bioretention facilities should be
applied where:
•
Sloped areas immediately adjacent to proposed bioretention areas should
be less than 20% but greater than 2% to ensure positive flow at reduced
velocities.
•
The bioretention facilities shouldn’t be less than 3 meters to the building
structure.
•
Sub-drainage areas are limited to less than 1-2 acres, and preferably less
than 1 acre (4, 046 square meters).
•
Facilities can be placed close to the source of run-off generation.
•
Wooded areas should not be cleared to make room for a bioretention
facility.
34
Based on the above design specifications, and increasing efficiency of LID in
reducing runoff and pollution, four flowing criteria are selected (Figure 3.2).
Criteria
Slope
Distance From Building Structure
Distance From Drainage Point
Distance From Network Drainage
Figure 3.2: Criteria Used For LID Site Suitability
3.3
Phase 2(Creating Fuzzy Membership Map)
In this section, criteria maps are converted to fuzzy membership maps. The
conversation comprises two parts. In the first place, criteria vector maps are converted to
raster maps. Second, by using raster calculator in ArcGIS, the fuzzy membership maps
are derived from their raster maps.
3.3.1 Creating Slope Fuzzy Membership Map
35
According the study area’s slope map (Figure 3.3), although majority of the
slopes are less than 20 degrees, to have precise suitability site location, it is necessary to
elicit slope fuzzy map. This Slope map was derived by study area’s DEM.
Figure 3.3: Slope Map
The following function describes the membership function for slope; it describes
that if slope(x) is less than 10, membership value is 1 which is most suitable, if the slope
is between 10 to 20 then the membership value gradually decreases to zero and for the
slope more than 20 the membership value is 0 which shows the worse area for LID
(Figure 3.4).The membership function can be written in raster calculator as follow:
36
con([ExtraSlop] >= 0 & [ExtraSlop] <= 20,1,[ExtraSlop] > 20 & [ExtraSlop] <= 30,(30 - [ExtraSlop])
/10,0)
con([ExtraSlop] >= 0 & [ExtraSlop] <= 20,1,[ExtraSlop] > 20 & [ExtraSlop] <= 30,(30 - [ExtraSlop])
/10,0)
0 ≤ x < 10
1
µ (slope) =
0
10≤ x <20
x ≥20
Figure 3.4: Fuzzy Membership of Slope
Figure 3.5 as shown below is the fuzzy membership slope map. In fuzzy slope
map in comparison with the conventional slope map; all cells have
ve values between zero
and one. The value of one represents highest suitability and zero stands
stan for the worst
suitability base on given criteria.
Figure 3.5: Fuzzy Membership Slope Map
37
3.3.2
Creating Distance from Drainage Network Fuzzy Map
ArcHydro tool in ArcGIS was utilized to extract drainage network map
(Figure3.6) from study area’s DEM. Installing LID components near drainage network
increases the efficiency of LID in reducing runoff and pollution. Therefore, distance
from drainage network was considered as LID site selection criteria. Figure 3.7 is
depicts the membership of distance from drainage network.
Figure 3.6: Drainage Network Map
38
0≤x≤3
1
µ (Distance) =
0
3 < x ≤10
x >10
Figure 3.7 Fuzzy Membership of Distance from Drainage Network
The following function gives an account of the membership function for distance
from drainage network. There is no specific or standard information to elicit this
membership function thus it was assumed according the scale of study area and
prospective result. It describes that if distance is less than 3 meters, membership value is
1 which is most suitable, and if the distance is between 3 to 10 meters then the
membership value
ue gradually decreases to zero and for the distance more than 10 meters
the membership value is 0 which has less suitability for LID practice (Figure 3.8).
39
Figure 3.8: Fuzzy Membership Drainage Map
3.3.3
Creating Distance from Building Fuzzy Map
The LID distance from building structure is one of the important criterions of
LID site design. This criterion should be regarded to prevent the eventual damages to
foundations. Therefore, this site design is considered as a site selection as well. The
following function provides the membership function for distance from building (Figure
3.9). It shows that if distance is less than 3 meters, membership value is 0 which is
worse suitable location for LID, if the distance is between 3 to 10 then the membership
value gradually increases to 1 and for the distance more than 10 the membership value is
1 which it means these area are most suitable according the site design criterion. Figure
3.10 shows the fuzzy membership building map.
40
µ (Distance) =
0
x≤3
3 < x ≤ 10
1
10 ≤ x
Figure 3.9:: Fuzzy Membership of Distance from Building Structure
Figure 3.10: Fuzzy Membership Building Map
ap
41
3.3.4
Creating Distance from Drainage Point Fuzzy Map
By using ArcHydro tool in ArcGIS, drainage Point map (Figure 3.11) is elicited
from study area’s DEM map. Drainage points are the most downstream points in their
associated catchments. Installing LID components near these Drainage Point raises the
amount of entering runoff and pollution to LID. This assumption decreases number of
LID practices for a specific area and enlarges the efficiency of their performance.
Figure 3.11: Drainage Points
The subsequent function (Figure 3.12) illustrates the membership function for
distance from drainage point. It depicts that if distance is less than 5 meters, membership
value is 1 which is most suitable location for LID, if the distance is within 5 to 30 meters
then the membership value gradually decreases to 0 and for the distance more than 30
42
meters, the membership value is 0 which has worse condition. Figure 3.13 depicts the
fuzzy membership drainage point
poin map.
0≤x≤5
1
µ (Distance) =
0
5 < x < 30
x ≥30
Figure 3.12: Fuzzy Membership
embership of Distance from Drainage Point
Figure 3.13: Fuzzy Membership Drainage Points map
43
3.3.5
Convex Combination
A convex combination is a linear combination of points (which can be vectors,
scalars) where all coefficients are non-negative and sum up to 1(Equation3.1). All
possible convex combinations will be within the convex hull of the given points. In fact,
the collection of all such convex combinations of points in the set constitutes the set's
convex hull. More formally, given a finite number of points , , … , in a real vector
space, a convex combination of these points is a point of the form
, , … , Where the real numbers satisfy ≥ 0 and
+ + ⋯ + = 1
(Equation 3.1)
As a particular example, every convex combination of two points lies on the line
segment between the points. The maximum value of any convex combination of Fuzzy
membership function is 1. The following Equation (3.2 and 3.3) and the land parameter
weights w in Table 3.1 have to be used in the calculation of the convex combination of
the raster layers that contains the fuzzy values:
µ = Where
=1
µ = 1
xєX
(Equation3.2)
> 0
(Equation 3.3)
44
There is no exact method to weight fuzzy parameters especially in this project.
These weights based of their importance to gain the aim of this study are taken to
account (Table 3.1).
Table 3.1: Fuzzy Parameter Weight
Parameter
Weight
Slope
10%
Distance from Building structure
50%
Distance from Drainage network
20%
Distance from Drainage Points
20%
Finally, a fuzzy site selection map was elicited from fuzzy maps (slope, distance
from building, point and drainage network). The best locations for bioretention cell were
shown in black blue which fuzzy membership is 1 (Figure 3.14).
45
Figure 3.14: Fuzzy Site Selection Maps
According to bioretention site design, sub-drainage areas should be limited to
less than 1-2 acres, and preferably less than 1 acre (4,046 " ). By considering the extent
of study area, 8 bioretention cells are selected in this study. LID bioretention cells are
considered as outlet points for their correspondent catchments. “Batch Point Generation”
46
tool in ArcHydro is used to generate LID cell’s catchment. In Arc Hydro, batch function
delineates watershed for its given batch point (Figure 3.15).
Figure 3.15: LID Subcatchments
47
3.4
Phase 3 (Exploit
Exploit XPSWWM
XP
To Evaluate The Fuzzy GIS
IS Based Model
Accuracy)
In the previous section, a GIS-based
based fuzzy model was designed to find the
suitability of area for installing bioretention cell. To appraise the fuzzy suitability
method, Peak Flow for conventional drainag
drainage system was calculated in a specific
rainfall.
3.4.1
Storm Selection for Event Modeling
Design storms have been conventionally used in the design of storwater
management devices. The principal reason is ease of use. Regulations usually specify
the design storm or storms to be used to analyze and size the device (Friedlich, 2004).
Nasir (2005) calculated stormwater design for the study area (Taman Wangsa Melawati)
in 10, 50 and 100 years return with 15, 30, 60 and 120 minutes duration. In this project a
24-hour
hour storm with a 10
10-year
year return period (the rainfall that occurs in a 24
24-hr period on
average once every 10 year) is selected (Figure 3.16).
Figure 3.16: Stormwater Design
Source: Nasir (2005)
48
3.4.2
XPSWMM Model
XPSWMM uses a method that is contains nodes, links and catchments to model
the stormwater and calculating the runoff in a specific area. Runoff is gathered from
each catchments and spilled to its correspond nodes. The first step in modeling the
runoff is drawing the catchments in XPSWMM. 30 catchments were drawn manually
base on the tin’s aspect map (Figure 3.17). Aspect elicited from tin of study area’s
surface. Aspect can be thought of as the slope direction. The values of the output raster
will be the compass direction of the aspect. Figure 3.18 shows the interface of
XPSWMM. Aspect map was utilized to determine the extent of catchments and draw the
directions of links.
Figure 3.17: Study Area’s Aspect
49
L
Figure 3.18: Conventional Links and Catchments in XPSWMM
Lastly, peak flow (cube meter per sec) for all nodes and links were determined in
XPSWMM. By categorizing the peak flow in three categories the below map was
derived.
50
According to Figure 3.19 peak flows in conventional drainage system categorize
to 3 groups which have less then 0.1cm, between 0.1cm and 0.3cm and beiger than
0.3cm. All bioretention cells are located near the conduits with more than 0.1 cubic
meters.
Figure 3.19: Bioretention Cells and Conventional Drainage System
51
According to Figure 3.20 the links beside LID components (blue columns) have
peak flow more than the average peak flow in conventional drainage system (red
columns).. It demonstrates the efficiency of simple fuzzy GIS-based
based model to find the
location off bioretention cell in a small catchment.
Figure 3.20: The Average Peak Flow
AS
52
3.5
Phase4 (Assessment of LID Effects on Runoff)
3.5.1
LID Hydrologic Analysis Components
The
low-impact
development
“functional
landscape”
emulates
the
predevelopment temporary storage (detention) and infiltration (retention) functions of
the site. This functional landscape is designed to mimic the predevelopment hydrologic
conditions through
runoff
volume control, peak
runoff rate
control,
flow
frequency/duration control, and water quality control (PG County, 2000). The lowimpact analysis and design approach focuses on Runoff Curve Number (CN), Time of
Concentration (Tc), Retention and Detention. The runoff curve number (also called a
curve number or simply CN) is an empirical parameter used in hydrology for predicting
direct runoff or infiltration from rainfall excess. The curve number method was
developed by the USDA Natural Resources Conservation Service, which was formerly
called the Soil Conservation Service or SCS — the number is still popularly known as a
"SCS runoff curve number" in the literature. The runoff curve number was developed
from an empirical analysis of runoff from small catchments. It is widely used and is an
efficient method for determining the approximate amount of direct runoff from a rainfall
event in a particular area.CN has a range from 30 to 100; lower numbers indicate low
runoff potential while larger numbers are for increasing runoff potential.
In this study, hydrologic evaluation is utilized to determine the effects of LID on
stormwater (amount of volume runoff). The evaluation method is used to measure the
amount of infiltration and/or storage to control the runoff volume and peak discharge
rate. To design a GIS model in order to estimate the LID affects, LID bioretention cell
component is selected.
53
The fundamental information to utilize the LID in an urban area and determine
the run off Curve Number (CN) and Time of Concentration (Tc) is the same as
conventional site plan and stormwater management approaches.
In order to figure out the amount of runoff in bioretention cell’s catchments, the
Storm Water Management Model (SWMM) is selected. SWMM is a dynamic rainfallrunoff simulation model used for single event or long-term (continuous) simulation of
runoff quantity and quality from primarily urban areas (EPA, 2010). It is widely used
throughout the world for planning, analysis and design related to stormwater runoff, and
other drainage systems in urban areas, with many applications in non-urban areas as
well.
Dividing the study area into a collection of smaller, homogeneous subcatchment
areas is done in previous section. Each catchment has its fraction of pervious and
impervious. Overland flow can be routed between sub-areas, between subcatchments, or
between entry points of a drainage system. The runoff component of SWMM operates
on a collection of subcatchment areas that receive precipitation and generate runoff and
pollutant loads.
The routing portion of SWMM transports this runoff through a system of pipes,
channels, storage/treatment devices, pumps, and regulators. SWMM tracks the quantity
and quality of runoff generated within each subcatchment, and the flow rate, flow depth,
and quality of water in each pipe and channel during a simulation period comprised of
multiple time steps (EPA, 2010). Since imperviousness decreases the infiltration and has
a straight affect on runoff, determining the imperviousness of catchment is the first step
in analyzing and calculating the amount of runoff and pollution.
54
Figure 3.21 shows the 8 LIDs and their correspond catchments in XPSWMM.
Each catchment attaches to its LID by dash line. These catchments are going to be
utilized in calculating the amount of runoff which spill to LIDs.
Figure 3.21: Bioretention Cell’s Catchments in XPSWMM
55
3.5.2
Determination Percentage of Each Land Use/Cover
The determination of the LID volume requires a detailed evaluation of each land
cover within the development site. This will allow the designer to take full advantage of
the storage and infiltration characteristics of LID site planning. This approach
encourages the conservation of more woodlands and the reduction of impervious area to
minimize the needs of LID (PG County, 2000). It is also important to obtain the
percentage of pervious and impervious surface for a more accurate hydrologic modeling.
There are various methods to calculate the percentage of these two parameters.
The simplest way is by manipulating land use data. Residential, commercials, and roads
are classified as impervious, while pervious surface is calculated based on the amount of
open spaces and land use information related on pervious surfaces.
To get imperviousness value for each catchment, landuse map and catchments
layer were intersected in ArcGIS (Figure 3.22). Table 3.2 and Figure 3.23 depict the
imperviousness of LID catchments in the study area.
The runoff volume from the site is a key in a stormwater management design
included conventional system and best management practices. For preliminary event
modeling using SWMM, rainfall-runoff data was desired with the widest possible range
of intensity, volume, and duration among storms. By using the CN method and
importing imperviousness and stormwater design data for each catchment in XPSWMM,
the amount of runoff is calculated (Figure 3.24).
56
Figure 3.22: Intersected Catchment LID1 and Landuse Map
Table 3.2: Catchment's Imperviousness
Catchment
Imperviousness (%)
Catchment LID1
64.3
Catchment LID2
62.7
Catchment LID3
34.6
Catchment LID4
56.5
Catchment LID5
62.5
Catchment LID6
55.7
Catchment LID7
75.7
Catchment LID8
82.1
Figure 3.23: Catchment’s Imperviousness
57
Figure 3.24: Rainfall-Runoff Data
58
59
3.5.3
Bioretention Design Guidelines
According to Poirier (2009), five sizing methods are using vastly for bioretention
system. The states of Georgia, Maryland, New York and Vermont require that
bioretention facilities be sized based on a volume of runoff to be treated to meet water
quality objectives, where filter bed sizing is based on Darcy’s law (Maryland
Department of the Environment 2000; Atlanta Regional Commission 2001; Vermont
Agency of Natural Resources 2002; New YorkDepartment of Environmental
Conservation 2008). North Carolina adopted the initial sizing guidelines proposed by
Prince George’s County in 1993, which are based on the Rational Method for peak
runoff (North Carolina Department of Environment and Natural Resources, 2007).
Bioretention design guidelines for the states of Virginia and Idaho require that
bioretention areas cover a specific percentage (5 to 7) of the total impervious drainage
area (Virginia Department of Conservation and Recreation 1999; Idaho Department of
Environmental Quality 2005). The state of Delaware requires bioretention system
geometry to meet necessary loading rates (Delaware Department of Natural Resources
and Environmental Control 2005). Finally, Wisconsin bioretention design guidelines
recommend the use of the RECARGA model to size bioretention facilities (Wisconsin
Department of Natural Resources (WDNR) 2006). This model was developed at the
University of Wisconsin to determine the hydrologic impact of bioretention systems
(Atchison and Severson 2004).
In this project, the volume of the bioretention cell is considered 7 percent of the
drainage areas depending upon the percentage of impervious surface in the contributing
watershed multiply by runoff in impervious area (Equation 3.4). Figure 3.25 shows the
amount of runoff in impervious and pervious area. Table 3.3 illustrates the volume of
each bioretention cell.
60
Bioretention Cell’s Volume = Impervious area in each catchment* 0.07 * Runoff
depth in impervious area
Equation3.4
Figure 3.25: Runoff Depth
Table 3.3: Bioretention Cell's Volume
Catchment
Area(Ha)
Imperviousness (%)
Bioretention Volume(Cubic meter)
LID1
0.89
64.35
40.15
LID2
0.77
62.7
33.84
LID3
1.33
34.6
32.26
LID4
1.02
56.55
40.43
LID5
0.57
62.51
24.97
LID6
0.78
55.72
30.46
LID7
LID8
0.44
0.38
75.73
82.16
23.35
21.88
61
3.5.4
LID Effects on None Point Source Pollutant Loading
Stormwater runoff contains various pollutants that are produced through the
activities in different residential, commercial, industrial and other land use types within
a catchment. Runoff pollution occurs every time water flows across the ground and
picks up contaminants (Krpo, 2004)
The stormwater pollution problem has two main components: the increased
volume and speed of surface runoff and concentration of pollutants in the runoff. Both
components are directly related to development in urban and urbanizing areas. Together,
these components cause changes in hydrology and water quality that result in a variety
of problems including habitat loss, increased flooding, decreased aquatic biological
variety, and increased sedimentation and erosion, as well as affects on the health,
economy, and social well-being (EPA, 1997).
Although all land uses can affect water quality, in undeveloped areas natural
processes can lessen the impact of contaminant or even remove contaminants from
runoff through infiltration and evaporation (Minnesota Pollution Control Agency, 2000).
Impervious areas reduce the opportunity for natural processes to treat stormwater and
that is why stormwater runoff must be adequately controlled and treated to reduce
pollutants before it is discharged into surface water, groundwater or wetlands (Schueler,
1994).
The water quality model developed for this project is based on a fundamental
concept of water quality modeling and control, specifically, that pollutant load is equal
to the product of concentration and flow (Thomann and Mueller, 1987). Four water
62
quality parameters have been selected for quantification of nonpoint pollution (Rifai et.
Al., 1993).
-
Total nitrogen (TN) = all the various forms of inorganic and organic nitrogen
-
Total phosphorus (TP) = orthophosphate and organic phosphorus
-
Biochemical Oxygen Demand (BOD) = measure of biodegradable organics in the
water
-
Fecal coliforms = bacteria present in the intestines or feces of humans and warm
blooded animals. E.Coli is primarily originated from domestic pollution
Different approaches can be used to study nonpoint pollution. A GIS is a
computer system capable of spatially representing data on the land surface and linking
additional data related to this spatial depiction, through tables and charts. Furthermore,
GIS is used in the area of environmental modeling, by providing ease and accuracy in
surface terrain representation, watershed delineation, precipitation, data compilation,
non-point source pollutant loading calculation and other concepts related to
environmental processes. Consequently GIS has emerged as a powerful modeling tool
which can help provide the knowledge necessary to make management decisions.
The effects of nonpoint source pollutants on particular waters are different and
may not always be fully or easily calculated and assessed. This study provides a GIS
model to calculate annual mass contaminant loadings. The following four steps, define
the procedure for calculating pollution loads generated by non-point sources:
63
1. Estimate typical concentrations of each water quality constituent in runoff
2. These water quality data, defined as estimated mean concentrations
(EMC), must be derived from each pre-defined land use type.
3. Load from a given geographic location must be calculated by multiplying
the calculated runoff volume from that area with the appropriate EMC
value.
4. Total loads from a watershed can be calculated by summing the loads
from all the contributing areas in the watershed.
In this study, for running the model and estimating the pollution, three main
parameters which are landuse, estimated mean concentration and runoff are tackled.
landuse defines the type of surface cover and present chemicals, and population density.
64
3.5.4.1 Estimated Mean Concentrations (EMC)
Estimated Mean Concentrations (EMCs) are typical pollutant values found in the
runoff. There are three typical ways of assigning EMC values:
Based on land use/land cover information(Table 3.4)
Based on percent impervious cover
Based on watershed type
Although the definition of EMC relates to a single rainfall event, the assumption
is often made that the EMC is directly related to land uses in the drainage areas and
constant independently of the duration and intensity of the rainfall events (DID 2000)
Table 3.4: Runoff Estimated Concentration Values (mg/l)
Source: Babich and Lewis, 2001
Landuse Type
Median
Concentration(g/m3)
Total Suspended
Solids
Residential Commercial
Open
Industry Rural Road Quarry
Space
178
55.6
48
138
95
114
28.6
8
8.7
4.35
12.9
5.4
8.9
12.9
Total Phosphor
0.39
0.28
0.28
0.509
0.255
0.26
0.1
Total Copper
0.04
0.05
0.006
0.053
0.003
0.04
0.053
Total Nitrogen
1.96
2.45
0.895
1.75
1.9
1.67
3.9
Biochemical
Oxygen Demand
65
Because Nutrients, nitrogen and phosphorus in particular, are pollutants of
primary concern for the protection of aquatic ecosystems, they are considered in this
study. In coastal and oceanic waters, nitrogen is generally found to be a limiting nutrient
for algal growth, while phosphorus tends to become limiting in freshwater systems
(Howarth and Marino 2006).
3.5.4.2 Calculation of EMC
Loads for total nitrogen and total phosphorus can be estimated by applying
pollutant concentrations to runoff volumes. There are different runoff depths for each
land use category. The model then applies the runoff depth based on the land use in each
catchment and applies it to the EMC, resulting in loads for each pollutant (Equation3.5).
#$
Load (g) = Runoff (" * EMC %
Equation 3.5
Calculating loading is useful as an input into water quality modeling or
comparing with other inputs into the system or with other systems. Spatial patterns of
loadings offer differentiation between sites or sub catchments in pollutant loadings.
They are useful for identification of specific sources and land uses of concern to water
quality (Marsalek, 1990).
66
3.6
Summary
Overall, this chapter has discussed about the methodology of this study. The
methodology flows chart begin with identify the criteria, convert maps to fuzzy
membership maps and exploit XPSWMM to evaluate the fuzzy GIS based model
outcome accuracy. According the result of XPSWMM, accuracy of fuzzy GIS based
model validated. The methodology extended to determine the parameters such as
bioretention volume and EMC which effects on amount of runoff and pollution.
AS
67
CHAPTER 4
Result and Analysis
4.1
Introduction
This chapter elaborates the performed spatial analysis in previous chapter to
achieve the final output of LID effects of runoff. In the first place, the LID effects on
amount of runoff are calculated. Second, LID removal pollution ability is discussed.
This includes raster calculating EMC pollution maps with runoff maps.
4.2
Analysis the LID Effects on Amount of Runoff
In previous chapter, the volume of each bioretention cell according to the
percentage of imperviousness was calculated. Figure 4.1 illustrates the flow chart of
bioretention cell. Each cell absorbs amount of water and reduces the volume of runoff.
68
Q1 (" : volume routed from LID correspond catchment to the LID component
Q2" : overflow from LID (that calculated previous section)
Figure 4.1: Bioretention Cell’s Flow Work
The volume of each bioretention cell as its capacity to absorb runoff is
considered to calculate the amount of removal runoff in each LID. Figure 4.2 shows the
comparison between amount total, absorbed and overflow runoff.
69
Figure 4.2: Comparison Between Total and Absorbed Runoff
According to Figure 4. 2, in a flash stormwater (120 minutes consistent rainfall
with 10 years return), bioretention cell can just absorb 7% of total runoff in a specific
area. In other words, the capability of reducing runoff by bioretention cell in a heavy
rainfall isn’t reliable. It demonstrates that although LID components can alleviate
alleviat runoff
in urban area with high imperviousness such as this study area, the conventional
drainage system still has the main role in controlling runoff. However, biroetention cells
would be useful components in uncritical rainfall.
70
4.3
Analysis the LID Effects on Non Point Pollution
In previous chapter the “simple methode” was described. By multiplying the
runoff depth and area in a specific land use, the amount of runoff is calculated. 3 new
fields of N_EMC, P_EMC and Rainfall_D are built in LID catchment layer’s attribute
table in order to convert vector map to three raster maps. N_EMC represents the EMC of
nitrogen in different land use, P_EMC stands for EMC of phosphorus and Rainfall_D
represents rainfall depth. Figure 4.3 shows Vector and Raster Layers of Runoff Depth in
Catchment LID1. Figure 4.4 depicts Vector and Raster Layers of Phosphorous EMC in
Catchment LID1. Figure4.5 represents Vector and Raster layers of Nitrogen EMC in
Catchment LID1. Figures 4.6 and 4.7 illustrate the result of multiplying raster layers
which as done with raster calculator.
Figure 4.3: Vector and Raster Layers of Runoff Depth in Catchment LID1
71
Figure 4.4: Vector and Raster Layers of Phosphorous EMC in Catchment LID1
72
Figure 4.5: Vector and Raster layers of Nitrogen EMC in Catchment LID1
73
Figure 4.6: Phosphors Pollution Map
Figure 4.7: Nitrogen Pollution Map
74
75
Table 4.1 and 4.2 show the specification of Nitrogen and Phosphorus Pollution
Raster maps in catchment LID1. Both raster maps have 3 different cells’ value and each
cell has 0.25 m² areas (the cell size is 0.5 meter). The amount of total nitrogen and
Phosphorus are determined by multiplying the cell’s value by its count and cell size.
Table 4.1: Specification of Nitrogen Pollution Raster Map
Cell
ID
Value (
'
()
Count
Cell Size( m²)
Mass of Total
Nitrogen (g)
1
0.1666
16470
0.25
685.9
2
0.14195
6465
0.25
229.4
3
0.0208535
12710
0.25
66.3
981.6
Table 4.2: Specification of Phosphorus Pollution Raster Map
Cell
ID
'
()
Value
Count
Cell Size( m²)
Mass of Total
Phosphorus(g)
1
0.03315
1647
0.25
13.6
2
0.0221
16465
0.25
90.9
3
0.00652
12716
0.25
20.7
125.2
There isn’t any exact model to determine the amount of removal pollution by
LID component. It is assumed that each bioretention cell can provide coefficient
removal for the runoff that passes over it. Bioretention box experiments were performed
by Davis et al. (2006) to investigate the nutrient removal potential of bioretention
76
systems. Total phosphorus removals ranging from 70 to 85% were noted
note (corresponding
to an average mass removal of 82%), while 55 to 65% of total Kjeldahl nitrogen (TKN)
was removed (Figure 4.8 and 4.9).
Figure 4.8: Removal Phosphorous (g) in LID Catchment
atchment 1
Figure 4.9: Removal Nitrogen (g) in LID Catchment
atchment 1
77
Eventual mass contaminant loading in this study will be calculated by first
assessing the physical characteristics of case study, then describing the nature of
stormwater quality based on various land use types and finally calculating mass
contaminant loading in stormwater runoff using EMC typical for specific land use.
The analyses are extended to calculate the non point source pollution and amount
of removal for the rest of catchments. Figure 4.10 and 4.11 depicte the amount of two
significant pullotions in the study area. Figure 4.12 and 4.13 illastrate the amount of
nitorgen and phosphorous which are removed by LIDs in the rainfall stormwater event.
Figure4.10: Total Mass of Phosphorus in Study Area
Figure
78
Figure 4.11: Total Mass of Nitrogen in Study Area
79
80
Figure4.12: Removal of Nitrogen
Figure 4.13: Removal of Phosphorus
81
4.4
Summary
In summary, this chapter discussed how the spatial analyses are
performed. The results show the LID effects on amount of runoff and its
pollution. They demonstrate bioretention cells have significant impacts on
removing pollution although their ability to absorb runoff in flash stormwater is
doubtable.
82
CHAPTER 5
Conclusion and Discussion
5.4
Introduction
This chapter discusses the achievement, and the recommendation to improve
further. The aims objectives and methodology are revised to conclude the
achievement. The recommendations are made for further research.
5.5
Achievement of Study
Flash flood is becoming more prevalent nowadays in big cities in Malaysia.
Rapid and uncontrolled development projects aggravate the problem. Lack of space
for the construction of flood quantity and quality mitigation facilities has prompted
authorities to look for other solutions for runoff control. One of the approaches is to
83
regulate flow at the upstream area. That is why this study area (Taman Wangsa
Melawati), which is located at the upstream of Klang river basin, is selected for the
study. Taman Wangsa Melawati catchment, which drains to Sg. Gisir is used in this
study to evaluate the impacts of LID for storm water management at a small scale.
The catchment is fully developed where 83 percent of the area is covered by
impervious surfaces.
Traditionally, stormwater detention facilities have been used to control peak
flows, but little attention has been given to actually decreasing the total runoff
volume from developed land. Non-point source pollution has become an additional
concern for Runoff control (Friedlich, 2005).
LID provides solutions to these
problems. LID is used to reduce the amount and enhance the quantity of runoff.
Bioretention cells are particulate LID devices which have the capacity to store
stormwater and ability to filter pollution as the runoff infiltrates in them.
Installing LID component in suitable location increases the efficiency of LID
operation. A GIS-based fuzzy model is employed to find the suitability of areas in
case study. The method compromises site design criteria, hydrology principal and
criteria’s weights. In addition, a dynamic simulation program (SWMM) is utilized to
evaluate the accuracy of GIS-based fuzzy outcome and compare two different
models. First of all, bioretein cell were located near the links with high peak flows
which illiterate the accuracy of GIS-based fuzzy model and its criteria. In the second
place, GIS-based fuzzy model is quite easier and faster than SWWM model and it
can be extended to use in different areas. Furthermore, SWWM is a time consuming
model in compression the GIS model.
In order to investigate the effectiveness of LID at improving the non point
source pollution (especially phosphorus and nitrogen) concentrations in stormwater
84
runoff the simple GIS method was utilized. The results of the caste study indicate
that bioretention cell can considerably reduce runoff pollution. However, installing
bioretion cells in the study area couldn’t reduce considerable amount of runoff in
flash flood.
5.6
Recommendation
Based on the study, several recommendations are suggested for the future
research. These are:
i.
The LID effects on runoff in the study were limited to its impact on amount
of runoff and removal pollution. LID effects can be extended to calculate its
influence on peak flow and time of consideration in conventional drainage system .
ii.
The fuzzy GIS based model criteria for determining LID site suitability
restricted to 4 criteria. They can be increased by further study according to LID site
design and LID hydrologic specifications.
85
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