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 61 220.127.116.11 Estimated Mean Concentrations (EMC) 64 18.104.22.168 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 22.214.171.124 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). 126.96.36.199 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. 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