EFFECTIVENESS OF ROADSIDE BEST MANAGEMENT PRACTICES …

University of Rhode Island University of Rhode Island

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Open Access Dissertations

2021

EFFECTIVENESS OF ROADSIDE BEST MANAGEMENT PRACTICES EFFECTIVENESS OF ROADSIDE BEST MANAGEMENT PRACTICES

(BMPS) ON MAINTAINING STORMWATER QUALITY THROUGH (BMPS) ON MAINTAINING STORMWATER QUALITY THROUGH

MONITORING AND MODELING MONITORING AND MODELING

Khurshid Jahan

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EFFECTIVENESS OF ROADSIDE BEST MANAGEMENT PRACTICES (BMPS)

ON MAINTAINING STORMWATER QUALITY THROUGH MONITORING AND

MODELING

BY

KHURSHID JAHAN

A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE

REQUIREMENTS FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

IN

BIOLOGICAL AND ENVIRONMENTAL SCIENCES

UNIVERSITY OF RHODE ISLAND

2021

DOCTOR OF PHILOSOPHY DISSERTATION

OF

KHURSHID JAHAN

APPROVED:

Dissertation Committee:

Major Professor Soni Mulmi Pradhanang

Thomas B. Boving

Arthur J. Gold

Brenton DeBoef

DEAN OF THE GRADUATE SCHOOL

UNIVERSITY OF RHODE ISLAND

2021

ABSTRACT

A comprehensive assessment of the stormwater BMPs’ in terms of flow volume

reduction, peak flow attenuation and runoff pollutant concentration reduction was made

to evaluate the effectiveness of existing stormwater best management practices (BMPs)

on the campus of the University of Rhode Island, RI, USA.

Urban stormwater runoff became a pressing issue in the later part of the 1980s

with the recognition that point source controls were not sufficient to protect and restore

water quality. In the last 15 years, the observed annual total rainfall in the study area

was usually greater than long-term average yearly rainfall, suggesting the climate is

towards wetter conditions. In addition to changing climate conditions, the areal extent

of urban and suburban land uses also increased in Rhode Island, i.e., there is a clear link

between increased stormwater runoff and the increasing extent of impervious surfaces

in build-over areas. Compared to predevelopment conditions, impervious surfaces can

lead to a rapid rise in peak flows in systems receiving stormwater runoff. About 10% to

15% of the total impervious areas in the USA are parking lots. This percentage is ex-

pected to increase in the future. Changing drainage patterns can cause floods, channel

erosion but also carries the potential for the decreased baseflow and streambed altera-

tions.

Roadside best management practices (BMPs) are techniques or methods that aim

to prevent or reduce the overall negative impacts of stormwater runoff flow and improve

the quality of stormwater runoff cost-effectively. These BMPs can be characterized into

three types based on their performance: (i) source control, (ii) flow control and (iii)

runoff treatment. Structural stormwater treatment practices are the most common type

of water quality control BMP in Rhode Island. They include 1) wet vegetated treatment

systems, 2) infiltration practices, 3) filtering systems, 4) green roofs, and 5) open chan-

nel practices. Therefore, it is essential to integrate monitoring and modeling of the treat-

ment of water stormwater pollutants by these BMPs and to evaluate water quality risks

of surface runoff along roadsides. Therefore, the proposed study was conducted to eval-

uate the effectiveness of the roadside BMPs under peak flow conditions as a function of

runoff depth and in terms of runoff pollutant reduction. In addition, a stormwater man-

agement model was applied, and the various types of Low Impact Development (LID)

control were analyzed.

This study conducted a four-pronged analysis based on 1) RS-GIS based SCS-

CN model developed; 2) EPA SWMM 5.1 for hydrologic and hydraulic simulation; 3)

parameter estimation using the Bayesian approach, and 4) predictive modeling through

Artificial Neural Network.

Specifically, this study focuses on:

(i) EPA SWMM5.1 model set up for runoff and stormwater pollutants,

(ii) Calibration and validation of the developed model

(iii) Estimation of parameter uncertainty of SWMM using Bayesian statistics

(iv) Application of Artificial Neural Network (ANN) is predicting on the of

important storm water pollutants

The developed model confirmed the significant role of LID in reducing runoff

depth and peak flow. The established LID structures (permeable pavement, bioretention,

and vegetative swale) are effective in runoff depth, peak flow, and pollutants reduction

(Total suspended sediments, Nitrate-N, and Orthophosphate-P) for the smaller rainfall

intensities (1-yr, 2-yr, 5-yr, and 10-yr) or rainfall design. The structures are not much

effective in terms of 50-yr and 100-yr rainfall design.

In conclusion, this study provides development and evaluation of the stormwater

management model in assessing the effectiveness of stormwater best management

practices and provides a decision-making tool for the stormwater managers and planners.

v

ACKNOWLEDGMENTS

I pay my humble gratitude to The Almighty, The Most Merciful and Beneficent,

who conferred his endless blessings in completing this Ph.D. journey.

This thesis arises in the part of “Philosophy of Doctoral in Environmental and

Life Sciences” by the Department of Geosciences at the University of Rhode Island,

USA. It is a delight to pass on my appreciation to all in my modest affirmation.

First and foremost, I am incredibly grateful to my supervisor Dr. Soni M

Pradhanang, Associate Professor, Department of Geosciences, URI, for her controlled

supervision, advice, and guidance from the beginning and for giving me extraordinary

experiences throughout the work. Above all, and most importantly, she offered me

unwavering encouragement and support in a variety of ways. Her truly scientist intuition

has made her a constant oasis of ideas and passions in science, which inspired me and

enriched my growth as a researcher and a scientist. I am indebted to her more than I can

express.

A deep feeling of gratitude to my committee member, Dr. Thomas B. Boving,

Professor, Department of Geosciences, for his pivotal role in the study design, fieldwork,

advice, and support that made this work accomplished. I gratefully acknowledge my

other committee member Dr. Arthur J. Gold, Professor, Department of Natural

Resources Sciences, for his timely advice and suggestions with kindness, enthusiasm,

and dynamism. His involvement with his originality has triggered and nourished my

intellectual maturity that I will benefit from for a long time to come.

Many thanks go particularly to Dr. Rebecca Brown, Professor, Department of

Botany, for contributing to the field preparation and her valuable advice in the biological

vi

discussion and provide necessary references. In addition, I am thankful to Dr. Vinca

Craver, Department of Civil Engineering, for her permission to use her lab for necessary

lab experiments. I am also grateful to Dr. Dawn Cardace, Professor, Department of

Geosciences, for her special encouragement and precious time reviewing my article.

I like to acknowledge the financial support for this study provided by Rhode

Island Department of Road and Transport (RIDOT) SPR-234-2362, and the University

of Rhode Island. I would like to thank URI College of the Environment and Life

Sciences (CELS) for its academic support throughout my graduate study.

I convey heartfelt gratitude to Dr. Anne Veeger, Vice Provost for Academic and

Faculty Initiatives, University of Rhode Island, and Dr. Jacqui Tisdale, Assistant Dean,

Students Office, for their constructive help and kind support for my family and me

throughout the challenging time in this Ph.D. journey.

I want to show my gratitude to the Department Chair, Dr. Brian Savage, Dr.

David Fastovsky, and all the professors of the Geoscience Department, University of

Rhode Island, for their suggestions, inspiration, and guidance. Special thanks to Dr.

Gavino Puggioni, Department of Computer Science and Statistics, for his outstanding

support and teaching during all the statistics courses I took.

I am also thankful to Julie Smallridge, the department's office staffs, and my

fantastic lab mates for their cordial help. I am grateful to my dearest friends Michaela,

May, Greta for being tremendously caring and providing me a breathing space. Thanks

to Tim Sharman for his help with the field work.

Where would I be without my family? A well-deserved special mention to my

parents Late Md. Khurshaduzzaman and Rasheda Zaman for their inseparable support

vii

and prayers. I could remember my deceased father, who was reading an article in a

newspaper about a Ph.D. graduate woman named after me back in the ’90s and wished

me to be a Ph.D. graduate one day.

Words come up short to specific my appreciation to my husband Tanjil, whose

dedication, love, encouragement, and unwavering confidence in me keep my dream

alive. I owe him for unselfishly letting his intelligence, passions, and ambitions collide

with mine. I am also thankful to my pretty girl Tanzeela Tasneem Tiyana and my

beloved son, Tahfeem Ahmed Tahan, for their extraordinary patience, endurance, and

unconditional love. I am a blessed mom having such wonderful and caring kids.

Thanks to my father- in-law, Mustafa Abu Bakar Siddique, and mother-in-law,

Shirin Begum, for supporting me.

Last but not the least, Dr. Nisa Khan, President of IEM LED Lighting

Technologies, I don’t know which word will fit to express my feelings to her. I had a

long-cherished dream of higher study. It is she with whom my dream, my effort, came

into reality. She makes me learn how to enhance concepts, ideas & thinking

academically, professionally, and personally. Her inspiration, motherly affection, and

support shape the pillar for my dream. Her unconditional love and protective care in

joys and sorrows make my journey smoother and prepare me to take new challenges. I

am grateful to Dr. Fred Heisman for his outstanding support and love towards my family

and me.

Finally, I would like to thank everyone important to the successful realization of

the thesis and express my apology that I could not mention personally one by one.

viii

Dedication

My parents and my family for whom I am here today

ix

PREFACE

This Ph.D. dissertation is written in a manuscript format adhering to the

guidelines of University of Rhode Island Graduate School. There are six main chapters

in this dissertation and one supplementary chapter documenting data.

The first chapter, entitled “ Surface Runoff Responses to Suburban Growth: An

Integration of Remote Sensing, GIS, and Curve Number” authored by Khurshid Jahan,

Soni M Pradhanang, and Mohamad Ehsan Bhuiyan, is a published article in the Land

Journal (Land 2021, 10(5), 452; https://doi.org/10.3390/land10050452). The second

chapter entitled “Modeling Runoff Quantity with different LID structures in the

Suburban Watershed using Stormwater Management Model (SWMM)” authored by

Khurshid Jahan, Soni M Pradhanang, and Thomas Boving in under review in the

Hydrological Sciences Journal-IAH. The third chapter entitled “Performance

Evaluation of LID Controls for Runoff Quality Using SWMM in Suburban Watershed”

is authored by Khurshid Jahan, Soni M. Pradhanang, and Rebecca Brown is in

preparation for the part II submission to the Hydrological Sciences Journal-IAH. The

fourth chapter is titled “Predictive Uncertainty Estimation of Stormwater Management

Model using Bayesian Markov Chain Monte Carlo approach” and is authored by

Khurshid Jahan, Soni M Pradhanang, and Russell D. Briggs and is prepared for

Hydrological Processes Journal. The fifth chapter entitled “Predicting Runoff Chloride

Concentrations in Suburban Watersheds Using an Artificial Neural Network (ANN)”

authored by Khurshid Jahan and Soni M Pradhanang is a published article in the

Hydrology Journal ((Hydrology 2020, 7(4),

80; https://doi.org/10.3390/hydrology7040080)

https://doi.org/10.3390/land10050452

https://doi.org/10.3390/hydrology7040080

x

The sixth chapter contains major conclusions, the significance of the study and

provides future research directions associated with this dissertation. The supplementary

chapter entitled “How effective are the bioswales in improving water quality? A case

study of a sub-urban catchment” provides an overview of field and laboratory analysis

data along with statistical analysis.

xi

TABLE OF CONTENTS

ABSTRACT ……………………………………………………………….………….ii

ACKNOWLEDGEMENT………………………………………………….………….v

DEDICATION………………………………………..……………………………...viii

PREFACE……………………………………………………………………………. X

LIST OF TABLES ..................................................................................................... xvii

LIST OF FIGURES ..................................................................................................... xx

CHAPTER 1 ................................................................................................................. 1

Manuscript І ................................................................................................................. 1

1. Introduction ................................................................................................................ 3

2. Study Area .................................................................................................................. 8

3. Methodology ............................................................................................................ 10

3.1. Surface Runoff Model ....................................................................................... 10

3.2. Integrated RS-GIS Approach to Surface Runoff Modeling .............................. 12

3.2.1. Land-Use and Land Cover Type Using Remote Sensing ........................... 12

3.2.2. Hydrogeological Parameter Determination Using GIS .............................. 15

Derivation of Soil Data ............................................................................................. 16

Derivation of Slope, Elevation, and Stream Order Data ...................................... 16

Rainfall Data......................................................................................................... 17

3.2.3. Hydrological Modeling within GIS ............................................................ 19

4. Results and Discussion: ........................................................................................... 20

4.1. Suburban Growth in the Study Area ................................................................. 20

4.2. Impact of Suburban Growth on Surface Runoff ............................................... 22

4.3. Impact of Suburban Growth on Rainfall-Runoff Relationship ......................... 26

5. Conclusions .............................................................................................................. 27

References .................................................................................................................... 29

xii

CHAPTER 2 ............................................................................................................... 36

Manuscript ІІ .............................................................................................................. 36

1. Introduction .............................................................................................................. 38

2. Study Area and Data Collection ............................................................................... 42

2.1 Study Area .......................................................................................................... 42

2.2 Local Precipitation ............................................................................................. 43

2.2.1. Observed Rainfall Data ............................................................................... 43

2.2.2. Rainfall Data Preparation............................................................................ 44

2.3 Drainage Pipeline Data ....................................................................................... 45

2.4 Elevation, Sub-catchment, Soil Infiltration, Land use and % Impervious Surface

.................................................................................................................................. 47

3. Methodology ............................................................................................................ 49

3.1. Hydrological Model Selection ........................................................................... 49

3.3 Model Calibration and Validation ...................................................................... 53

3.4.2 Permeable Pavement (PP) Scenario ............................................................. 55

3.4.3 Bio-Retention (BR) Cell Scenarios.............................................................. 56

3.4.4 Vegetative Swale (VS) Scenarios ................................................................ 58

3.5 Assessment of the LID envrionmental benefits ............................................. 58

3.5.1 Calculation of Runoff and Peak Reduction ............................................... 59

3.5.2 Calculation of LID environmental benefits ................................................. 59

4. Result and Discussion .............................................................................................. 60

4.1 SWMM Calibration and Validation ................................................................... 60

4.2 Results in Baseline Scenario .............................................................................. 63

4.3 Runoff Volume Reduction ................................................................................. 64

4.4 Peak Discharge Reduction .................................................................................. 66

4.5 Environmental Benefits .................................................................................... 68

5. Conclusions .............................................................................................................. 69

References .................................................................................................................... 71

CHAPTER 3 ............................................................................................................... 79

xiii

Manuscript III ............................................................................................................ 79

Abstract ........................................................................................................................ 80

1. Introduction ............................................................................................................ 81

2. Methodology .......................................................................................................... 86

2.1 Study Area .......................................................................................................... 87

2.3 Data collection, source, and preparation ........................................................ 93

2.3.1 GIS data ....................................................................................................... 93

2.3.2 Sample collection and analyses ................................................................... 95

2.4 Scenarios of the developed model ...................................................................... 96

2.1.4 LID Selection and Developed Scenarios ..................................................... 96

2.5. Model performance evaluation .......................................................................... 98

2.5.1. Sensitivity analysis ...................................................................................... 98

2.5.2 Calibration and Verification of SWMM Models .......................................... 99

3. Result and Discussion ............................................................................................ 101

3.1 Model Performance Evaluation ........................................................................ 101

3.1.1 Parameters sensitivity analysis .................................................................. 102

3.1.2 Model Calibration and Validation ............................................................. 106

3.2 LID implementation on stormwater quality ..................................................... 111

3.2.1 Comparison of stormwater management scenarios .................................. 111

4. Conclusion ........................................................................................................... 117

CHAPTER 4 ............................................................................................................. 126

Manuscript IV .......................................................................................................... 126

Abstract ...................................................................................................................... 127

1. Introduction .......................................................................................................... 128

2. Study Area, Data & Materials .............................................................................. 133

3. Material & Methodology...................................................................................... 135

3.1 SWMM Model ................................................................................................. 135

xiv

3.2 Objective Function ........................................................................................... 136

3.3 Bayesian Approach and Error Modeling .......................................................... 138

3.4 Markov Chain Monte Carlo (MCMC) ............................................................. 140

3.5 Sensitivity Analysis (SA) ................................................................................. 141

4. Findings and Discussions ....................................................................................... 144

4.1 Sensitivity Analysis .......................................................................................... 144

4.2 Model Efficiency .............................................................................................. 148

4.3 Parameter Uncertainty ...................................................................................... 150

4.4 Predictive Uncertainty ...................................................................................... 151

5. Conclusion ............................................................................................................. 154

CHAPTER 5 ............................................................................................................. 161

Manuscript V ............................................................................................................ 161

1. Introduction ............................................................................................................ 163

2. Materials and Methods ........................................................................................... 166

2.1. Artificial Neural Network (ANN) ................................................................... 168

2.2. Back Propagation (BP) Algorithm .................................................................. 169

2.2. Curve Fitting Algorithm .................................................................................. 170

2.4. Density Distribution algorithm ........................................................................ 170

2.5. Model Structure ............................................................................................... 171

2.5.1. ANN Parameter Selection: Hidden Layers and Nodes ............................. 173

2.5.2. ANN Parameter Selection: Learning Rate and Momentum ..................... 173

2.5.3. ANN Parameter Selection: Initial Weights ............................................... 174

2.5.4. ANN Parameter Selection: Selection of Input Variables.......................... 174

2.5.5. ANN Parameter Selection: Data Partition ................................................ 175

2.5.6. ANN Parameter Selection: Model Performance Evaluation .................... 176

3. Results and Discussion ........................................................................................... 177

3.1. Model Output .................................................................................................. 177

3.2. Curve Fitting Analysis ..................................................................................... 179

3.3. Density Distribution of the Predicted Chloride Concentration ....................... 181

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3.4. Cross Validation Based on Snow and Precipitation Events ............................ 182

4. Conclusion ............................................................................................................. 186

References .................................................................................................................. 189

CHAPTER 6 ............................................................................................................. 193

CONCLUSION ...................................................................................................... 193

Scope of the work ..................................................................................................... 196

CHAPTER 7 ............................................................................................................. 197

Supplementary ......................................................................................................... 197

Abstract ...................................................................................................................... 198

1. Introduction .......................................................................................................... 199

2. Site Description .................................................................................................... 202

3. Materials and methods ........................................................................................... 204

3.1 Study Design .................................................................................................... 204

3.2 Rainfall pattern and size ................................................................................... 205

3.3 Monitoring Data Collection and Analysis ........................................................ 205

3.4 Turbidity ........................................................................................................... 207

3.5 Chloride ............................................................................................................ 209

3.5 Statistical Analysis ........................................................................................... 210

3.5.1 One-way ANOVA ..................................................................................... 211

3.5.2 Two-way ANOVA ..................................................................................... 211

3.6 Reduction Rate ................................................................................................. 214

5. Result and Discussion ............................................................................................ 214

5.1 Nitrate-N ........................................................................................................... 215

5.1.1 analysis on one-way and two-way ANOVA ................................................. 215

5.1.2 Reduction rate of the Nitrate-N ................................................................. 220

5.2 Orthophosphate-P (PO4-P) ............................................................................... 222

5.2.1 analysis on one-way and two-way ANOVA ................................................. 222

xvi

5.2.2 Reduction rate of the Orthophosphate-P (PO4-P) ...................................... 227

5.3 Total Suspended Sediments (TSS) ................................................................... 229

5.2.1 Analysis of One-way and Two-way ANOVA............................................... 229

5.2.2 Reduction rate of the Total Suspended Sediments (TSS) .......................... 233

6. Conclusion ............................................................................................................. 235

References .................................................................................................................. 236

xvii

LIST OF TABLES

TABLE PAGE

Chapter One

Table 1. General characteristics of four candidate image scenes…….…….…..….….10

Table 2. General characteristics of four candidate image scenes………………….….15

Table 3. Satellite-detected suburban expansion in South Kingstown, Rhode Island…21

Chapter Two

Table 1: Point Rainfall Frequency (pf) estimates with 90% confidence intervals and

supplementary information (NOAA Atlas 14, volume 10, version 3)……….....….…44

Table 2: Categories of the rainfall………………………….…………………………47

Table 3. Summary of Subbasins Characteristics …….……………………………….50

Table 4: Summary of LID Characteristics……………………………………………57

Table 5: SWMM calibration parameter………………………………………………61

Table 6: Comparison between three calibrations run………………...……………….63

Table 7. Percentage of runoff and peak reduction per unit area under six rainfall

reoccurrence scenarios…………………………………..……………………………65

Table 8: Environmental benefits for different LID structures………………………..68

xviii

Chapter Three

Table 1: Percentage of runoff depth and peak reduction per unit area under six rainfall

re-occurrences ………………………………………..……………………….….…..85

Table 2: Input parameters of the sub-catchment for the developed SWMM-LID……88

Table 3: Buildup and Wash off and parameters of pollutants on different land-use

types …………………………………………………………….…………………....92

Table 4: Data source of the required parameter of SWMM-LID……….….….…..…94

Table 5: Parameters of three LID controls used in the LID-SWMM Simulations.......97

Table 6: Model sensitive Parameter and their ranking based on the sensitivity

analysis…………………………………………………………………..…………..106

Table 7: Adjusted SWMM calibration surface layer and soil parameters………..…107

Table 8: Model Calibration and validation output………………...………………...108

Chapter Four

Table 1: Model Parameter and Ranges used for Calibration and Uncertainty

Analysis……………………………………………………………………………...138

Table 2: Model Parameter Settings………………………………………………….148

Table 3: Table 3: Model Calibration and Validation Output……………….……….149

Chapter Five

Table 1. ANN model output for all the sites…………….…………………………..178

Table 2. Summary of the curve fitting assessment…………………………….……181

xix

Chapter Seven [Supplementary]

Table 1: Monitoring Strategies……………………………...………………………203

Table 2: Types of rainfall based on rainfall intensity……………………………….205

Table 3: Summary of the parameters and analytical method used, including detection

limits (MDL)…………………………………………………………….…………..206

Table 4: Analysis of variance for linear regression………….………...……………218

Table 5: Categorical variables……………………………………………………….213

Table 6: Model selection based AIC for Nitrate-N, Orthophosphate-P, and

TSS…………………………………………………………………….………….....216

Table 7: Nitrate-N response on treatment type………………….…………………..218

Table 8: Orthophosphate-P response on treatment type……………………….……226

Table 9: TSS response on treatment type…………………………………...……….232

xx

LIST OF FIGURES

FIGURE PAGE

Chapter One

Figure 1. A glimpse of flood events from 2010 (a,b) and 2013 (c,d) in the conducted research area. Source: 2010 flood [56] and 2013 flood [60]……………………….….7

Figure 2. The geographical location for the study site: South Kingstown, RI, USA….9

Figure 3. The Process of Land-use and land cover Change Detection………...……..14

Figure 4. Hydrogeological parameters for the study area: (a) Stream order, (b) Hydro-logic soil group (HSG), (c) Slope, (d) Elevation…………………………………..…18

Figure 5. A schematic representation of the geographic information system (GIS) based soil conservation service curve number (SCS-CN) model in estimating the sur-face runoff…….……………………………………………….……………………...20

Figure 6. Land type change detection from 1994 to 2020…………………..………..22

Figure 7. Runoff depth change detection and area from 1994 to 2020…………….....23

Figure 8. Storm Runoff depth of 2020, 2014, and 1994……………………………...25

Figure 9. Rainfall-runoff relationship for the study period……………..…….....……27

Chapter Two

Figure 1: (a) SWMM, the stormwater Water Management Model, program

configuration (b) Overview of the SWMM structure indicating linkage among the

computational blocks. Receiving water simulation is by external programs (Adapted

from Huber and Dickinson, 1988)…………………..………….…………….……….40

Figure 2: Land-use changes based on 36 years of data in the study area adopted from

the recent research paper (Jahan et al. 2021)………………………….………….…..41

Figure 3: Location Map of the Study Area…………………………...………………43

Figure 4. Typical design rainfall processes of different return periods of 1-, 2-, 5-, 10-,

25-, 50-, and 100- yrs. (PDS: Precipitation Data Server) ……………………………45

Figure 5. Elevation and flow path, Soil infiltration rate, Land use, and the percentage of

impervious surface in the study area………………………………….…..…………..46

xxi

Figure 6: Sub-catchments, Nodes, and Streamflow Delineation of the watershed into

sub-basins of the study area with nodes, streamflow, and outlets…………..……..…48

Figure 7: Time series of the observed (green) and simulated (red) discharge [m3/s] for

Run 1, 2, and 3 during the calibration period. The precipitation rate [mm/sec] for the

corresponding time of discharge is included on the secondary axis (blue)……...……62

Figure 8: Simulated runoff processes in four LID structures with different return-

period events: (a) One year; (b) Two-year; (c) five-year (d) ten-year (e) 50-year, and

(f) 100 year…………………………………………………………………...….……64

Figure 9. Hydrological effectiveness of each LID per unit area……………...………65

Figure 10: Environmental benefits and its standard error for each LID structure in

different rainfall intensities. The standard error of the mean was applied to construct a

confidence interval (95%) for the environmental benefits for each of the LID

structures. ………………………………………………………………………….....69

Chapter Three

Figure 1: Subdivision of the conducted study area for developed SWMM-LID……..88

Figure 2: Flowchart of applied SWMM-LID framework………………………...…..93

Figure 3: a) Model runoff results of sub-catchments for hydrological parameters; b)

Model runoff results of the outfall of the catchment concerning hydraulic parameters.

[In SWMM, time patterns allow external dry weather flow to vary periodically. They

comprise of a set of adjustment factors applied as a multiplier (SWMM 5.1

manual]…………………………………………………………………………..…..104

Figure 4: Buildup and Wash-off parameter sensitivity analysis a) TSS; b) Nitrate-N; and

(c) Orthophosphate-P…………………………………………………......…………105

xxii

Figure 5: Calibration and Validation of the developed SWMM (a) Run 1 in Sub-

Catchment 3 and (b) Run 2 in Sub-catchment 6…………………………………….108

Figure 6: The observed and predicted TSS concentration (a) Site 1 (Agricultural Land)

(b) Site 5 (Parking Lot)…………………………………………………...…………109

Figure 7: The observed and predicted Nitrate-N concentration (a) Site 1 (Agricultural

Land) (b) Site 5 (Parking Lot)…………………………………………………….…110

Figure 8: The observed and predicted Orthophosphate-P concentration (a) Site 1

(Agricultural Land) (b) Site 5 (Parking Lot)……………………………..…………110

Figure 9: Performance of Bioretention for six different sites (a) TSS, (b) NITRATE-N,

and (c) Orthophosphate-P………………………………………..….………………113

Figure 10: Performance of Permeable Pavement for six different sites (a) TSS, (b)

NITRATE-N, and (c) Orthophosphate-P…………………………………….……..114

Figure 11: Performance of vegetative swale for TSS, NITRATE-N, and

Orthophosphate-P removal…………………………………………………………116

Chapter Four

Figure 1: (a)- Traditional Model, (b)- Uncertainty Model (adapted and modified from

[15]………………………………………………………………………….……….130

Figure 2: Location Map of the Study Area…………………………...……………..134

Figure 3: a) Model runoff sensitivity analysis of sub-catchments for a) hydrological

parameters; b) the outfall of the catchment concerning hydraulic parameters [In SWMM,

time patterns allow external dry weather flow to vary periodically. They comprise of a

xxiii

set of adjustment factors applied as a multiplier (SWMM5.1

Manual)]…………………………………………………………………….……….145

Figure 4: Sensitive Parameters Response of SWMM Quantity Module (a) Main

Response of the selected parameter (b) first-order SI indices represents the response of

each of the variables (c) Total Effects SI indices measure the portion of variability that

is due to total variation in each input or variable (d) Main effects for each input variables

which obtained as a by-product of the variance analysis with mean 90% error interval

(e) Uncertainty distribution of Sensitive parameters…………………………….….146

Figure 5: Sensitive Parameters Response of SWMM Quality Module (a) Main Response

of the selected parameter (b) first-order SI indices represents the response of each of the

variables (c) Total Effects SI indices measure the portion of variability that is due to

total variation in each input or variable (d) Main effects for each input variables which

obtained as a by-product of the variance analysis with mean 90% error interval (e)

Uncertainty distribution of Sensitive parameters……………………………………148

Figure 6: (a) Posterior distribution of Intercept (b) 10000 Iteration of Monte Carlo

Simulation with the random progression of the intercept of the model (c) Posterior

distribution of Slope (d) 10000 Iteration of Monte Carlo Simulation with the random

progression of the slope of the model……………………….…………………….…150

Figure 7: (a) Predictive Uncertainty Band (b) Monte Carlo simulation of the

data…………………………………………………………………………….…….152

Figure 8: Predictive Uncertainty Band Monte Carlo simulation of a) TSS, b) Nitrate-

N, and c) Ortho phosphate P…………………………….…………………………..154

xxiv

Chapter Five

Figure 1. Chloride concentration trends from 1992 to 2012 in the USA show regional

differences (modified from United States Geological Survey Report, 2014)…….…165

Figure 2. The map illustrates the location of the study area in Rhode Island, USA. Red

bounding box shows the exact location of the study site shown above (Google Earth

screenshot- on the right)………………………..…………….. …………………….166

Figure 3. Variability in observed chloride concentrations in runoff are shown, from

2016 to 2019………………………………….……………………………………..167

Figure 4. This diagram illustrates the structure of a conventional feed-forward back-

propagation neural network model……………………………….…………………172

Figure 5. This diagram illustrates the structure of the artificial neural network (ANN)

model developed in this work……………………………………………..………..172

Figure 6. Curve fitting assessment between ANN output and target data of six differ-

ent sites. Site 1, 3, 4, and 5 showed a similar trend, not scattered data. On the other

hand, site 2 and 6 has scattered data point, but 80% of the dataset were in the confi-

dence interval…………………………………………………………..….……….180

Figure 7. Density distributions (ANN outputs) for chloride concentrations at all sites

reveal shifting means. Spatial distribution covered <50% for sites 1, 2, 3, and 4

whereas sites 5 and 6 covered >70% of predicted chloride……….…….…..…….183

xxv

Figure 8. Precipitation and snow events (a) 2017, (b) 2018, and (c) 2019 in South Kings-

town, RI. (a) total four significant snow events (>10 cm) and low to moderate precipi-

tation events appeared during winter in 2017; (b) three significant snow (>10 cm) events

(among them one was the worst >25 cm) and low to moderate with high-frequency pre-

cipitation occurred during winter in 2018; and (c) only one significant snow and low to

moderate precipitation happened during the winter in

2019……………………………………………………………………………….....184

Figure 9. Predicted concentrations for three sites reflect heavy Cl inventory in Winter

2018…………………………………………………………….……………………185

Chapter Seven [Supplementary]

Figure 1: Roadside Best Management Practices Sites within the URI Campus..…...203

Figure 2: An overview of Turbidity in the Six different monitoring sites…………..208

Figure 3: An overview of Chloride concentration in the Six different monitoring

sites………………………………………………………………………….…….…209

Figure 4: Statistical parameter of the different types of rainfall…………………….216

Figure 5: Statistical parameter of the different types of seasons…………...……….216

Figure 6: Residuals and Normal Q-Q plot of the developed model……….………...218

Figure 7: Nitrate-N response to treatment types in each of the sites………...…...…219

Figure 8: Comparison of Nitrate concentration reduction between control and treatment

site. Blue colored symbol represented the reduction (%), and the red color symbol

xxvi

represented the negative reduction for the treatment site. The dot lines separate Pre-

treatment and post-treatment periods………………………..……………………….222

Figure 9: Statistical parameter of the different types of rainfall…………………….223

Figure 10: Statistical parameter of the different types of season……………………224

Figure 11: Residuals and Normal Q-Q plot of the developed model………….…….225

Figure 12: Ortho-phosphate response to treatment types in each of the sites ……....226

Figure 13: Comparison of Ortho-phosphate concentration reduction between control

and treatment site. Blue colored symbol represented the reduction (%), and the red

color symbol represented the negative reduction for the treatment site……………..228

Figure 14: Statistical parameter of the different types of rainfall…………….……..230

Figure 15: Statistical parameter of the different types of season……………...…….231

Figure 16: Residuals and Normal Q-Q plot of the developed model………………..232

Figure 17: TSS response to treatment types in each of the sites………………...…..233

Figure 18: Comparison of TSS concentration reduction between control and treatment


represented the negative reduction for the treatment site………….....……………..234

1

CHAPTER 1

Manuscript І

Surface Runoff Responses to the Suburban Growth: An Integration of Remote

Sensing, GIS, and Curve Number

Published in Land 2021, 10(5), 452

Khurshid Jahan1, Soni M. Pradhanang1,*, Md Abul Ehsan Bhuiyan2

1Department of Geosciences, University of Rhode Island, Kingston, RI 02881

2Department of Civil and Environmental Engineering, University of Massachusetts,

Amherst, MA 02115, USA

*Correspondence: [email protected] (S.M.P.)

2

Abstract: Suburban growth and its impacts on surface runoff were investigated using

the soil conservation service curve number (SCS-CN) model, compared with the inte-

grated advanced remote sensing and geographic information system (GIS)-based inte-

grated approach, over South Kingstown, Rhode Island, USA. This study analyzed and

employed the supervised classification method on four Landsat images from 1994,

2004, 2014, and 2020 to detect land-use pattern changes through remote sensing appli-

cations. Results showed that 68.6% urban land expansion was reported from 1994 to

2020 in this suburban area. After land-use change detection, a GIS-based SCS-CN

model was developed to examine suburban growth and surface runoff estimation. The

developed model demonstrated the spatial distribution of runoff for each of the studied

years. The results showed an increasing spatial pattern of 2% to 10% of runoff from

1994 to 2020. The correlation between runoff co-efficient and rainfall indicated the sig-

nificant impact of suburban growth in surface runoff over the last 36 years in South

Kingstown, RI, USA, showing a slight change of forest (8.2% area of the total area) and

agricultural land (4.8% area of the total area). Suburban growth began after 2000, and

within 16 years this land-use change started to show its substantial impact on surface

runoff. We concluded that the proposed integrated approach could classify land-use and

land cover information to understand suburban growth and its potential impact on the

area.

Keywords: urbanization; suburban growth; land-use and land cover

3

1. Introduction

Urbanization and suburban growth increase challenges to the surface water bod-

ies, including flooding, channel erosion, water quality degradation, biodiversity, and

climate [1,2]. The current global environmental change pattern based on land use and

land cover is of concern [3]. Urbanization, particularly uncontrolled expansion associ-

ated with urban sprawl, is fueled by population growth and increasing demand for resi-

dential areas. Most notably in the environmental sector, rapid urbanization degrades

watershed functions and reduces agricultural and forest lands [3–5]. Eventually, the

quick changes in land-use and land cover impact the annual water balance locally and,

potentially, regionally [6–8], with severe consequences on the frequency, volume, and

peak rates of surface runoff. The proportion of rainfall that becomes surface runoff in-

creases along with increases in imperviousness of a watershed. Infrastructural develop-

ments such as building construction, residential development, lack of green areas, and

impervious surfaces such as parking lots accelerate runoff [9]. An increase in impervi-

ous surfaces and built-up land is more vulnerable to flooding than the surrounding en-

vironment. Higher runoff volumes lead to increased occurrences of flood and expansion

of floodplains. Therefore, land-use changes associated with urban development, vege-

tation, and hydrologic conditions are the major factors that affect urban flooding in

many ways [10], leading to environmental degradation. [8,11]

Recent investigations have focused on characterizing land-use changes and their

adverse effects on landscape characteristics that generate flood hazards, including inun-

dation and erosion [11]. Therefore, information on land-use and land cover (LULC),

4

which greatly influence hydrologic applications and water quality [12–14], aids in fully

understanding urbanization and its impact on local hydrology and water quality [15,16].

Urban growth has various stages, and each stage affects local hydrology in different

ways [8–14]. For example, during the first stage of urbanization, hydrology is changed

due to the removal of trees and vegetation [14,17], leading to decreased interception,

evapotranspiration, and sedimentation. During the second stage of urbanization, houses,

commercial buildings, streets, culverts, and parking lots increase imperviousness,

thereby, affecting and changing the water balance. Specifically, this stage increases

storm flows and runoff depth and degrades surface and groundwater quantity and qual-

ity in urban and suburban areas. The decrease in infiltration often leads to increased

runoff flashiness and peak discharge for even small-sized storms. As a result, flood be-

come a significant concern for highly and moderately urbanized and suburban areas.

The combination of a remote sensing (RS) and a geographic information system (GIS)

approach can viably assess such hydrologic changes [18–22] and produce a LULC de-

tection map for assessing the detailed changes in an area [23]. RS has made an immense

contribution to detecting change in the LULC which has helped researchers to think

about the impact of LULC modifications [23,24].

Remote sensing (RS) data products are cost-effective and readily available as

inputs to hydrologic and watershed models [25–27]. The RS technique gathers multi-

spectral, multiresolution, and multitemporal data or images and then transforms the im-

ages into information for urban land-cover datasets [7]. Multispectral bands play a vital

role in numerous urban growth studies, emphasizing the necessity for advanced land-

5

use and land cover change information for remote sensing optical satellite imagery ap-

plication [28–30]. Multispectral satellite images have been used as source data for

LULC change and water body detection applications since the 1960s [31]. Multispectral

remote sensing includes the acquisition of visible, near-infrared, and short-wave infra-

red images in many broad wavelength bands [32]. Different materials reflect and absorb

differently at different wavelengths, and this absorbance and emissivity characteristic

are used to detect LULC changes [33].

Multispectral images are primarily applied in the detection of urban land-use

changes rather than hyperspectral images [34,35]. Digital data in the form of satellite

images enable accurate computing of various LULC categories and help maintain the

spatial data infrastructure, essential for monitoring urban expansion and land-use studies

[36]. Consequently, RS and GIS data products are effectively used in the watershed,

town, urban, and regional planning [37]. Since most surface runoff modeling and land-

cover detection parameters are geographic data, integration of RS and GIS techniques

is expected to be more effective in evaluating the impacts of urban LULC. GIS has been

extensively applied in hydrologic models [27,38,39] because of its spatial analysis func-

tion, especially for model data preparation, model input parameters extraction, or model

output visualization [39].

The Soil Conservation Service Curve Number (SCS-CN) method, one of the

most widely used in rainfall-runoff modeling, was developed by the Natural Resources

6

Conservation Service (SCS), U.S. Department of Agriculture (USDA) [40–42]. Numer-

ous studies [43–48] showed that the SCS-CN method was developed beyond its original

scope and turned into an integral part of simulation models. This method was applied

for different landscape structures, soils, and climate conditions [49–53]. These research

outputs indicated that the SCS-CN runoff method could be used effectively for large

and small watershed areas. This study combined RS and GIS to measure stormwater

runoff and establish a relationship between rainfall and the stormwater runoff coeffi-

cient (i.e., the ratio of surface runoff to total rainfall). Stormwater runoff coefficient, a

crucial parameter in hydrology, is frequently used to examine the impacts of urban

LULC, which leads to urban runoff generation [54]. Stormwater runoff, pollutants man-

agement, and subsequent accumulation in soils, surface water, and groundwater pose

significant challenges to many Federal and State agencies. These challenges warrant a

better assessment of the impacts of suburban growth on runoff changes and local hy-

drology.

Due to rapid urbanization and its impact on the environment, researchers often

concentrate on highly urban areas to detect LULC changes and degradation rather than

focusing on suburban areas. Because of population demand and the interest in and need

for infrastructural development, suburban land-use is also gradually changing over time,

turning into urban areas. Only a limited number of studies have been done on suburban

LULC change detection, and no research has been conducted in the selected suburban

area. A recent study in this suburban area has revealed high chloride concentration in

the impervious zone due to road salt during the winter season [12]. The study area also

7

experienced two significant flood events within a short duration; one happened in March

2010, and another occurred in March 2013 (Figure 1) [55–62]. The 2010 flood event

resulted from the combination of the March Nor’easter storm and reservoir manage-

ment, while the 2013 flood was a precipitation event. The flood duration for 2010 and

2013 was three days and one day, respectively, but the damage amount was massive.

The magnitude of these flood events and damages has cost the urban and suburban in-

frastructure, which led us to investigate the land-use changes and assess the impact of

suburban growth on surface runoff.

Figure 1. A glimpse of flood events from 2010 (a,b) and 2013 (c,d) in the conducted

research area. Source: 2010 flood [56] and 2013 flood [60].

This study included an integrated approach to estimate the surface runoff and

examine suburban growth effects on LULC pattern change. The main objectives are: (a)

8

to detect suburban LULC changes using satellite RS and GIS and to study spatial pat-

terns of suburban growth; (b) to examine the effect of such suburban growth on surface

runoff generation, and (c) to detect the most affected area within the urban watershed.

2. Study Area

This study is focused on a suburban municipality (South Kingstown) in southern

Rhode Island, USA (Figure 2). According to the definition of ‘suburban’ [63], the pop-

ulation density is usually between 1000–1200 individuals per square kilometer. In our

study, the population is slightly lower, i.e., 206.2 per square kilometer according to the

2010 census, than the population for defined suburban areas. The study area is predom-

inantly flat, low relief, and the annual average rainfall is approximately1.34 m [62].

Based on bore log data, the groundwater level in this area is 3.12 m from the surface,

and the upper part of the aquifer is dominated by sand with some gravel [64,65]. The

geology of the aquifer, combined with the shallow groundwater table and the prevalence

of impervious surfaces, classifies this system’s water as a high-risk area for pollution-

induced by surface runoff [65].

This study used four multispectral (MS) satellite images at 30 m spatial resolution

from USGS EarthExplorer [63]. Specifically, to ensure the original image’s spectral

quality, we used MS imagery, which includes standard red-green-blue (RGB) channels

and narrow spectral channels from near- or middle-infrared regions’ reflectance spectra

[29,30]. Four candidate image scenes were chosen from four different years, and their

respective features are presented in Table 1. It is noted that each image scene was

9

radiometrically corrected using a relative radiometric correction method [66] with Erdas

Imagine. The scanline error correction was also conducted for the Landsat 7 ETM im-

age. Scanline error correction is required for LANDSAT 7 ETM images except for

LANDSAT 5 TM. Striping is one of the limitations for LANDSAT 7 ETM images. The

striping arises due to the scanline corrector (SLC) failure in 2003 [33,35]. Finally,

Figure 2. The geographical location for the study site: South Kingstown, RI, USA.

10

Surface runoff estimates were obtained from these satellite image scenes applying GIS

application.

Table 1. General characteristics of four candidate image scenes.

S.No.

Date of

Images

Satellite Sensors

Resolution

(m)

Bands

Thermal

Band

1. 09-19-1994 LANDSAT-5 TM 30 7 6

2. 09–14-2004 LANDSAT-5 TM 30 7 6

3. 09-18-2014 LANDSAT-7 ETM 30 8 6

4. 08-09-2020 LANDSAT-8 OLI 30 11 10 and 11

3. Methodology

3.1. Surface Runoff Model

In this research, we applied the Soil Conservation Service (SCS) method in rainfall–

runoff modeling. The SCS method utilizes several significant factors such as soils, wa-

tershed characteristics, i.e., slope, elevation, shape, and land-use over the study area

[67,68]. The other two noteworthy factors that affect runoff are rainfall duration and

intensity. The SCS-CN model was extensively used to determine the CN, ranging from

0–100 [67]. The estimates of surface runoff depend on the potential retention in the

catchment. Surface runoff is largely impacted by three factors, i.e., interception, surface

retention, and infiltration, which vary for different soil types. The SCS-CN equation is

mathematically represented as Equation (1):

11

Q =(P − 0.2S)2

(P + 0.8S) (1)

Here, Q indicates storm runoff which is estimated from rainfall (P), and S specifies

maximum potential storage and is defined as,

S = (1000

CN) − 10 (2)

Here, CN indicates the runoff curve number of a hydrologic soil group–landcover com-

plex. Two parameters are required to solve the equation: rainfall (p) and CN. The rain-

fall data is collected from the National Oceanic and Atmospheric Administration

(NOAA). CN is used to estimate the runoff from rainfall, ranging between 30 to 100,

based on soil properties of land types (Table 2). In this study, rainfall-runoff depth is

estimated for nine different kinds of land type: (1) Agricultural land, (2) Commercial

land, (3) Forest, (4) Grass and pasture, (5) Residential, (6) Industrial, (7) Open Space,

(8) Parking lot and street, and (9) Water. The permeability characterizes the hydrologi-

cal soil group (HSG) (A, B, C, D). The infiltration rate is higher in group A (>0.30 in/h)

or >7.62 mm/h) even when the soil is thoroughly wetted, while group D has the lowest

permeability and infiltration rate (0–0.05 in/h or 01.27 mm/h) for the runoff. Group B

(0.15–0.30 in/h or 3.81–7.62 mm/h) and C (0.05–0.15 in/h or 1.27–7.62 mm/h) soils are

intermediate between groups A and D. Group A and D consist of sand and clay, respec-

tively. Rango et al. (1983) [68] claimed only a 5% error in land-cover estimates from

Landsat data at the basin level and a much higher error at the cell level. A composite

CN can be computed for the different land-uses for a watershed using the following

equation:

12

CNc = CN1A1+ CN2A2+ ………+CNiAi+ ….+ CNnAn

∑ Aini=1

(3)

where CNi is the curve number of area i, Ai is the area of each LULC for area i and n is

the number of land uses. In this study, CN is calculated for each land class using ArcGIS

10.6 from the vector soil dataset.

3.2. Integrated RS-GIS Approach to Surface Runoff Modeling

Integrated RS-GIS approaches are used for surface runoff modeling. The analy-

sis comprises three main parts: (a) derivation of the land class of the study area using

RS, (b) hydrological parameter determination applying GIS, and (c) runoff modeling

using GIS. For the land class derivation, four MS image scenes were selected from four

different years. The land class type and soil information provided the hydrological curve

number, one of the key parameters needed for the hydrologic models. Hydrological pa-

rameters (directly related to runoff calculation), such as maximum storage, were deter-

mined using the curve number. The land-use types were used as independent variables

for the proposed methodology. Lastly, the runoff was determined using precipitation

and the maximum storage dataset. In the succeeding sub-sections, all three processes

are described in detail.

3.2.1. Land-Use and Land Cover Type Using Remote Sensing

We used land-use and land cover patterns for four different years from 1994 to

2020 at the same and almost the same time (Table 2). For all the images, we considered

less than 10% cloud cover. Initially, all images were corrected using a common Univer-

sal Transverse Mercator (UTM) coordinate system [69]. Every image was then radio-

metrically corrected according to the Jensen method [67]. Scanline error correction was

13

done for the 2014 images. For the land-use and landcover derivation, supervised classi-

fication with a maximum likelihood algorithm was applied [70,71]. The MS image for

2020 was collected from Landsat 8 OLI (Operational Land Imager), and scanline error

correction was not required for this image.

A supervised classification method was used for image classification. This

method usually requires a priori knowledge of each of the land types. A group of training

data sets was collected to identify each of the land types. Four different classification

algorithms were available to proceed with the supervised classification. In this study,

we applied the maximum likelihood approach for land-use change analysis. The algo-

rithm uses the spectral signature of the pixels from the training dataset to classify the

whole image. The spectral signature file used the training information to define the sta-

tistics, such as the mean and variance of each land type. Every training dataset consists

of at least 5 to 10 data points [71]. The more data points represented, the greater the

accuracy of the classification. In this study, we collected 12 to 15 data points for each

of the land classes. The process was subsequently applied to 1994, 2004, 2014, and 2020

(Figure 3).

Every classified image was superimposed by land-use and land cover shapefiles

from Anderson land classes to determine the accuracy of the classification [70], which

were used to categorize the land use. Land use and land cover vector files are available

for 1995, 2004, and 2011 in the Rhode Island Geographic Information System (RIGIS).

Therefore, the classified images are evaluated with the publicly available land-use and

14

land cover vector datasets. 1995, 2004, and 2011 data were used to evaluate 1994, 2004,

and 2014 land classification. Since the current land-use and land cover shapefile for

2020 is not available, no image accuracy evaluation was done for 2020 land classes.

In this study, residential, commercial, industrial, and parking lot and street areas were

considered urban class types as they are also directly related to population growth. A

total of nine land-use classes (Table 2) were derived from the images. In terms of using

multispectral perspective, three-band combinations: Green (band 2), Red (band 3), and

near-infrared (band 4) were used to derive the land classification. A flow chart is pre-

sented in Figure 3 to describe the detailed process.

Figure 3. The Process of Land-use and land cover Change Detection.

15

The classified suburban growth image was overlaid with the vector files to calibrate the

suburban expansion area. All the raster files were created using 30 m cell size, and this

cell size area was calculated for every land class. The town boundary vector file was

utilized to detect the suburban expansion for the particular year.

To detect urban expansion in the suburban area, curve number (CN) values were

used (Table 2). Table 2 represents the CN value for different land types and hydrologic

soil groups.

Table 2. General characteristics of four candidate image scenes.

Land Type A B C D

Agricultural Land 64 75 82 85

Commercial 89 92 94 95

Forest 30 55 70 77

Grass/Pasture 39 61 74 80

Residential 60 74 83 87

Industrial 81 88 91 93

Open Space 49 69 79 84

Parking and paved spaces 98 98 98 98

Water/Wetlands 0 0 0 0

Adapted from 210-VI-TR-55, Second Ed., June 1986 [64.]

3.2.2. Hydrogeological Parameter Determination Using GIS

In this step, we prepared soil and precipitation images to generate hydrogeolog-

ical parameters, and the overall processes are discussed below:

16

Derivation of Soil Data

The soil data is prepared from a digital soil survey with a detailed soil geographic

data level, jointly developed by the Rhode Island Soil Survey and National Cooperative

Soil Survey, and was downloaded from the Rhode Island Geographic Information Sys-

tem (RIGIS) [72]. The map data contains detailed information such as hydrological

group, shape area, shape length, and soil name. For this study, we applied hydrologic

soil group (HSG) (A, B, C, and D) (Figure 4b) information (Table 2) for each land type.

All four types of HSG were found in the study area. As shown in Figure 4b, the HSG in

group B occupied about 29% (61 sq. km) of the total area. About 22% (48 sq. km) of

the total area belonged to group D. However, groups A and C occupied approximately

17.7% (37 sq. km) and 9.1% (19 sq. km), respectively. Considering the HSG proportion,

the study mainly consisted of a moderate infiltration rate (group B) of 3.81 to 7.62

mm/h. A raster file is then generated for the study area based on the hydrologic group

using the runoff curve number (CN) values. The maximum storage of the area was then

calculated using the raster calculator.

Derivation of Slope, Elevation, and Stream Order Data

A slope is an essential parameter of watershed characteristics representing an

angle between the inclined ground surface and the horizontal plane. A slope map was

produced (Figure 4c) using a DEM with a 30 m resolution for the study area applying

the ‘surface’ option from the spatial analyst tool of ArcGIS. The slope map showed the

slope variation in degree. About 75% of the area had a slope between (0–5) degrees,

and 18% had a slope of (5.1–15) degrees, mainly in the southern part of the study area.

17

Consequently, elevation (Figure 4d) and stream order (Figure 4a) maps were also de-

veloped to represent the overall scenario of the hydrogeological parameter of the study

area. This area’s elevation ranges from 0 to 323 m and has predominantly first and sec-

ond-order streams.

Rainfall Data

Rain data was derived from the Parameter-elevation Regressions on Independent

Slopes Model (PRISM) [73–75] climate mapping system. Since only one USGS

weather station was available in the study area, there were insufficient neighboring sta-

tions to create the spatial distribution of rainfall in a given area. Therefore, the PRISM-

derived high-resolution precipitation data were considered for this study. PRISM pre-

cipitation products are spatially gridded at 4 km resolution. Elevation is the primary

variable that controls the precipitation pattern [76–78]. PRISM data accounts for eleva-

tion impacts and, therefore, could be used reliably for this study.

18

Figure 4. Hydrogeological parameters for the study area: (a) Stream order, (b) Hydro-

logic soil group (HSG), (c) Slope, (d) Elevation.

19

For this study area, 10 PRISM stations are projected and used for the analysis.

The kriging application [79] was used to generate the spatial distribution image by as-

signing average yearly rainfall in the coverage with a 30 m cell size for the raster.

Kriging is expressed as:

ZK = ∑ ƛiZini=1 (4)

where Zk is an estimate by kriging, ƛ is a weight for Zi and Zi is a variable. The weight

is determined to ensure unbiasedness [79].

PRISM data helped to calculate the storage for the entire study area for each of the land

classes.

3.2.3. Hydrological Modeling within GIS

We applied GIS for the runoff modeling of this suburban watershed (Figure 5).

Multiple images (land cover and soil images) were used to construct the CN image.

Each area’s CN value was estimated using USDA 1972 [67] standard SCS values (Table

2). Potential maximum storage, S, was derived using map algebra application of GIS

for the entire time series. Then, storm runoff depth images were prepared from rainfall

and potential maximum storage images using Equation (1). Four images for 1994, 2004,

2014, and 2020 were created for four different years applying the same methodology.

The resulting runoff images were reclassified into runoff ranks. Suburban growth and

its development were most prominent in the potential maximum storage images.

We then developed the relationship between rainfall-runoff coefficients for this

study area. The runoff coefficient was calculated for each of the selected years’ ten

largest storm events based on the rainfall amount. A runoff coefficient curve was then

20

constructed as a function of the flood size. A significant change of the suburban effect

based on the runoff coefficient pattern was observed over time.

Figure 5. A schematic representation of the geographic information system (GIS)

based soil conservation service curve number (SCS-CN) model in estimating the sur-

face runoff.

4. Results and Discussion:

4.1. Suburban Growth in the Study Area

Suburban growth represents the expansion of residential, commercial, industrial,

and roads and parking lots, and results indicate an increasing trend, as shown in Table

3. Land type change detected both areal expansion and reduction. The 36 years analysis

results (Table 3) showed that the suburban area expanded by about 68.6% (6564 acres)

within this administrative region.

Simultaneously, the agricultural area increased considerably (by approximately

5%), and the forest area decreased by nearly 8.2%. The proportion of urban area to the

21

total land area was 18%, 20.4%, 29%, and 31% in 1994, 2004, 2014, and 2020, respec-

tively. According to land-use change detection, from 1994 to 2004, suburban areas did

not expand. Differences in LULC change from 1994 to 2020 from satellite image anal-

ysis are presented in Table 3.

Table 3. Satellite-detected suburban expansion in South Kingstown, Rhode Island.

Land Type

Calculated Area (Acre) Change Detection of the Area

1994 2020 Area (Acre) %

Urban 9569 16,133 6564 68.6

Agriculture 2684 2814 130 4.8

Forest 18,440 16,934 −1506 -8.2

Grass/Pasture 6662 710 −5952 -89.3

In this study, suburban land-use expansion (Figure 6) is the main predictor in

analyzing the surface runoff pattern. Suburban growth is directly related to percentage

imperviousness. Increasing imperviousness leads to an increase the suburban runoff.

22

Figure 6. Land type change detection from 1994 to 2020.

In the study area, suburban expansion started from 2000 to 2020 along with con-

siderable population growth [75]. Suburban institutional development also exhibited the

overall expansion of the suburban area. For suburban growth, the main driving factor is

population growth and residential zone demand [4–6]. The suburban area expanded by

68.6% during this time; if the trend continues, the suburban area will quickly convert to

an urban area.

4.2. Impact of Suburban Growth on Surface Runoff

The impact of suburban expansion on surface runoff was examined by compar-

ing the measured runoff volume from 1994 to 2020. With the growth in suburban ex-

23

pansion, runoff is also expanded over time. This study found that suburban growth in-

creased surface runoff. The GIS-based SCS-CN model was used to evaluate the surface

runoff for the years 1994, 2004, 2014, and 2020. For each of the years, runoff areas for

different runoff depths were calculated. The resulting images of runoff depth showed

the runoff depth changes in this area (Figure 7).

The spatial distribution of modeled runoff (Figure 8) showed the runoff change in

2014 and 2020 compared to 1994. A moderate runoff depth (Figure 7) and runoff area

increased over time in the study area. The runoff increase was relatively low from 1994

to 2004 and higher in 2014; even in 2020, the runoff depth increased significantly (by

about 15% on average) compared to 2014 (Figure 7).

Figure 7. Runoff depth change detection and area from 1994 to 2020.

24

We then ranked runoff from 1 to 9 based on the runoff depth, indicating the lowest

runoff depth as rank 1 (i.e., a decrease or no change in runoff depth) and the highest

rank as 9 (i.e., zones having the highest runoff depth of 9 mm in 2020). Each of these

ranks is a continuous and discontinuous extension expressed with a different color in

the raster file for 1994, 2014, and 2020. The yellow color represents the runoff depth of

3 to 4 mm in 1994 and 2014. Noticeably, yellow holds a major portion of the study area

in 1994 (17.2% of total area) and 2014 (16.6% of the total area). However, in 2020, the

noticeable portion of the site has a runoff depth of 4 to 5 mm, indicating that the area

had a higher runoff depth 2020. In 2020, the maximum runoff depth was reported as 9

mm, which was not observed in the previously investigated years (1994, 2004, and

2014). A visual and detailed interpretation of the study area’s areal extent and spatial

occurrence was created by aggregating categories 6 to 8 for 2014 (Figure 8). Due to a

small amount of suburban growth between 1994 and 2004, the spatial distribution of

runoff depth for 2004 did not show much change relative to 1994. The modeled runoff

spatial distribution of each year (1994, 2014, and 2020) reflected a suburban expansion

in this area. The land-use classification showed that urban land type for 2020 increased

by approximately 8.3% from 2014, and these land changes led to an increase of runoff

depth from 2014.

The total area of runoff depth under the ranks increased by around 21.7% from

1994 to 2020. Despite the decrease in rainfall, we still found an increasing trend in run-

off depth. This was higher in the northwestern part of the study site. This northwestern

area was also becoming more vulnerable due to expansion in suburban growth and land

25

use. A correlation between the distributed runoff area and the suburban expansion area

was examined using the ordinary least square (OLS) tool from the spatial statistical op-

tion in ArcGIS. The result showed a strong positive correlation between two mapped

patterns with multiple r-values of 0.63 (average) (p < 0.05), where the correlation value

indicated an increasing trend along with the increasing suburban growth in the study

area.

Figure 8. Storm Runoff depth of 2020, 2014 and 1994.

26

4.3. Impact of Suburban Growth on Rainfall-Runoff Relationship

The runoff coefficient was measured according to the ten highest rainfall events

for each of the years. The runoff coefficient range varied based on imperviousness, rain-

fall depth, duration, and intensity [15]. Figure 9 showed the relationship between rainfall

and runoff coefficient for the study area. A higher runoff coefficient was expected when

there was more rainfall volume over the suburban area. According to the SCS model,

the rainfall and runoff coefficient relationship is governed by maximum potential stor-

age. The impervious zone has low or no potential storage, and the runoff coefficient

value showed a strong relationship in these areas. This strong relationship illustrates the

effects of suburban growth in this study area. The two dynamic variables are the subur-

ban growth rate and the maximum potential storage. A correlation between the suburban

growth rate and the maximum potential storage variables generated r = 0.45 in 1994, r

= 0.68 in 2014 and r = 0.71 in 2020. During the entire period from 1994 to 2020, the

runoff coefficient increased by about 0.28. This increasing trend indicated that urbani-

zation played a vital role in the rainfall-runoff relationship. Thus, urbanized areas are

more prone to increased runoff and flooding events because lower potential storage of-

ten implies that the same amount of rainfall may generate more runoff depth depending

on imperviousness [13,14]. Furthermore, the land-use change detection showed about

68.6% increased suburban growth from 1994 to 2020 (Table 3). The standard deviation

of the runoff coefficient, ranging from 0.16 to 0.021 for these 36 years, indicates subur-

ban growth.

27

Figure 9. Rainfall-runoff relationship for the study period.

5. Conclusions

We developed an integrated approach using an RS and GIS-based SCS-CN

model to assess suburban growth influences in surface runoff. The combined effort of

RS-GIS confirms it to be an efficient tool for suburban growth analysis. By applying

this methodology, we developed a linkage between suburban growth and surface runoff

through spatial analysis, which showed a positive and significant correlation.

Land-use and land cover changes are examined through remote sensing using

four different Landsat images applying supervised classification. We emphasized the

change detection in the urban land type. The analyzed output showed that urban areas

expanded by about 68.6% from 1994 to 2020, whereas forest (decreased by only 8.2%)

28

and agricultural (decreased by only 4.8%) land types showed a little change in this area.

Despite this slight change in forest and agriculture, significant urban land change im-

pacted the surface runoff, and this change confirmed suburban growth in suburban ar-

eas. The output raster file of land use changes from remote sensing was used as an input

parameter along with soil and precipitation in the GIS-based SCS-CN model.

The GIS-based SCS-CN approach was applied to develop the model for estimat-

ing runoff for four different years. The significant advantage of employing the GIS ap-

plication in rainfall–runoff modeling is that more accurate sizing and calculation can be

achieved compared to traditional methods. We relied on GIS analysis to detect the sub-

urban growth effect on surface runoff in the study areas. Surface runoff and its areal

extension were also examined from the analysis. The modeled output indicated that run-

off depth had increased from 2%–10% from 1994 to 2020. The spatial distribution of

the surface runoff also signifies a moderate to significant effect of suburban growth.

Furthermore, the increasing trend of runoff coefficient with time and rainfall events in-

dicates the positive impact of suburban growth.

The integrated approach worked successfully in suburban areas and could arrive

at the stormwater quality impacts in this study. Stormwater quality degradation could

be the scope of work for further analysis. The two primary factors that were predictors

of the surface runoff are rainfall and the hydro group soils (HSG) group. From 1994 to

2020, both runoff depth and spatial extension of runoff areas enlarged. Due to the in-

crease of suburban expansion in the northwestern part of the study area, the runoff depth

29

primarily increased. This integrated approach is a valuable tool to analyze suburban

growth and its impact on surface runoff by developing rainfall–runoff modeling, espe-

cially for suburban areas. This research’s final output strongly supports the impact of

urbanization impact in this suburban region on surface runoff. Further research should

be focused on stormwater management in terms of quality and quantity to minimize the

future environmental impact in suburban areas.

Author Contributions: For this research, K.J. developed the research framework, designed and

carried out the analysis of results, and wrote the original paper. S.M.P. contributed to the inter-

pretation of results, resources, supervision, project administration. M.A.E.B. conceptualization,

figures preparation, interpretation of results, writing, review & editing and contributed to the

development of the paper. All authors have read and agreed to the published version of the

manuscript.

Funding: The research study is funded by the Rhode Island Department of Transportation, SPR-

234-2362, and the University of Rhode Island.

Acknowledgments: Authors are thankful to the Rhode Island Department of Transportation

(RIDOT) for the project fund. We also thank Arthur Gold and Yeqiao Wang, Professors of the

University of Rhode Island, for their valuable comments and recommendations.

Conflicts of Interest: The authors declare no conflict of interest.

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36

CHAPTER 2

Manuscript ІІ

Modeling Runoff Quantity with different LID structures in the Suburban

Watershed using Stormwater Management Model (SWMM)

Under review in Hydrological Sciences Journal-IAH, June 2021

Khurshid Jahan1, Soni M Pradhanang1,*, and Thomas Boving1, 2

1Department of Geosciences, University of Rhode Island, RI, Kingston, USA 2Department of Civil and Environmental Engineering, University of Rhode Island, RI,

Kingston, USA

*Correspondence: [email protected]

37

Abstract

This study represents the application of the stormwater management model

(SWMM) in simulating stormwater runoff quantity (both peak flow and runoff

depth) in a suburban watershed area. Further, it investigates the comparative per-

formance of different low impact development (LID) measures. The Chipuxet wa-

tershed in Rhode Island (USA) was selected to develop an optimized LID struc-

ture for the suburban watershed using SWMM (Ver.5.1). The focus is on enhanc-

ing the treatment for both peak flow and stormwater runoff depth by LID struc-

tures during 1, 2, 5-, 10-, 50-, and 100-year rainfall return periods. The compre-

hensive effectiveness of different LID structures was measured for 4 different sce-

narios without any LID, permeable pavement (PP), bioretention (BR), or vegeta-

tive swale (VS). A detailed analysis was then performed for runoff peak reduction

and the runoff volume or depth reduction for PP, BR, and VS. The results show

that the LID structures effectively control the peak flow and runoff depth of storm

events for all six different return periods. The study revealed that both BR and PP

could control the peak and runoff volume in the suburban watershed. Finally, en-

vironmental benefits for each of the LID structures were calculated for the differ-

ent return periods of rainfall.

Keywords: stormwater management model; suburban watershed; low impact de-

velopment, analytical hierarchy process; bioretention; permeable pavement, bios-

wales.

38

1. Introduction

Rapid urbanization occurred in the United States in the early twentieth century [1]

that changed the land utilization from forest or agricultural uses to suburban or urban

lifestyles. Since then, large-scale urbanization has led to rapid infrastructure

development resulting in increased impervious surface areas. Most of the urban and

suburban land surfaces with high impervious surfaces do not sufficiently allow

stormwater and snowmelt to infiltrate into the ground. Further, the expansion of

impervious areas accelerates rainwater convergence that causes a higher volume of

runoff and peak flows [2-4]. The consequences of the increased runoff and the peak

flows have changed the natural hydrologic cycle [5,6] and increased urban flooding,

runoff pollution, water environment deterioration while damaging local ecology [7-12].

The US Environmental Protection Agency (EPA) specified in a 1988 report to Congress

that urban stormwater runoff is the third and fourth most extensive cause of water

quality impairment of the nation's lakes and rivers, respectively [13, 14]. In another

report in 1992, the EPA stated that the urban runoff is the second and third-largest source

of the impairment of the lakes, estuaries, and rivers, respectively [15,16]. Therefore,

assessing the impact of urbanization is essential for developing plans to reduce

stormwater runoff impact on hydrologic processes.

To reduce stormwater runoff impacts on the hydrologic process, many researchers

have broadly applied stormwater management models, especially in urban areas [17-

31]. The most widely used urban water quantity and quality model is the Storm Water

Management Model (SWMM) developed by the EPA in 1971 [32]. Since then, it has

39

undergone several significant updates for improved outputs [33]. The most recent

version, SWMM5.0, was released in early 2014 [34,35]. It can simulate hydrology,

hydraulics, and surface runoff quality of watersheds [36]. The model consists of

modules that usually apply to simulating different components of the hydrologic cycle

[37]. The governing principle for simulating runoff is the conservation of mass and

momentum equations for gradually varied and unsteady flow, known as the Saint

Venant flow equation. SWMM is a dynamic, comprehensive hydrological rainfall-

runoff simulation model that is usually used for a single event or transient simulations

of all aspects of the urban and suburban hydrologic and quality cycles [32,38,39]. This

model comprises four computational or building blocks, i.e., 1) runoff, 2) storage/

treatment, 3) transport, and 4) extran (Extended transport), as shown in Figure 1. The

RUNOFF block generates hydrographs and pollutographs. The primary input

parameters required to simulate hydrographs are rainfall hyetographs and the sub-

catchment physical characteristics. SWMM5.0 has modernized both the model’s

structure and its graphical user interface (GUI), making SWMM easier to use and more

accessible to a new generation of engineers and water resources specialists. However,

it is complex for the general public or planners with no modeling experience [19]. The

modified and the most recent version of SWMM (5.1.010) has many subroutines added

to analyze impacts of green infrastructure (GI) through low-impact development (LID)

components [37,38,40-48].

40

Figure 1: (a) SWMM, the stormwater Water Management Model, program

configuration (b) Overview of the SWMM structure indicating linkage among the

computational blocks. Receiving water simulation is by external programs (Adapted

from Huber and Dickinson, 1988 [49])

SWMM with different LID structures has been used widely for different regions

and countries, such as the USA and China. It must be noted that this study focuses on

the predominantly suburban watershed with mixed land use where the effectiveness of

SWMM coupled with LID has not been considered.

LID systems are characterized by a sequence of micro-scale stormwater devices or

structures with varying design parameters [50]. The LID techniques infiltrate and trap

pollutants in stormwater at the source and minimize imperviousness of both urban and

suburban areas [51,52]. Examples of LID structures include green roofs, permeable

pavements, bioretention cells, concave greenbelts; vegetative swales, and rain gardens

[32]. Several studies have reported the effectiveness of LID systems for maintaining

water quantity and quality in urban areas. However, there are no studies that primarily

41

focus on suburban areas. An investigation has been done on the growth in suburban

watersheds showing significantly increased surface runoff in terms of quantity and

quality [53,54]. This study analyzed suburban growth based on 36 years of land use and

land cover changes using the soil conservation service curve number (SCS-CN) model

[54]. In addition, the investigated area experienced two significant flood events in

March 2010 and March 2013 [55-62] (Figure 2), which resulted in significant

infrastructural damages. Both suburban growth (Figure 2) and flood events warrant us

to investigate the stormwater management techniques in this suburban area.

Figure 2: Landuse changes based on 36 years of data in the study area adopted from

the recent research paper [54]

42

Suburban areas are becoming equally important as urban areas to investigate

stormwater management. The research herein was conducted in a suburban

environment, and its purpose is to develop a stormwater management model (SWMM)

for controlling stormwater runoff depth and peak flow with LID structures. This

research tests the effectiveness of 4 different scenarios, which are reducing the peak

runoff and runoff depth by developing the stormwater model for the suburban watershed

in the absence of LID, permeable pavement (PP), bioretention (BR), and vegetative

swale (VS). A comprehensive evaluation of the different LID scenarios is assessed in

this study based on an analytic hierarchy process (AHP) [63, 64]. The AHP is a multi-

criteria programming technique for decision-making in complex environments. The

AHP works by developing sets of priorities for alternatives managements and the

criteria used to judge those alternative managements [65]. Environmental benefits such

as reduction in runoff volume and peak are considered in the prioritization and

determination of construction and infrastructural development projects [19,50,64].

2. Study Area and Data Collection

2.1 Study Area

The Chipuxet watershed is in southern Rhode Island, USA, and covers 36.93 sq.

mi (95.64 sq. km) (Figure 3). The watershed provides most of the public water supply

(used in agriculture, recreational use, and drinking water) of two towns. The Chipuxet

River that drains it is mainly recharged by groundwater [66]. The watershed

characteristics are typical for many similar suburban watersheds in the eastern United

States.

43

Figure 3: Location Map of the Study Area

Agricultural, drinking water, and recreational and current water demands affect water

quantity and quality and impact the recharge and runoff characteristics [67].

2.2 Local Precipitation

2.2.1. Observed Rainfall Data

Rainfall-runoff modeling with EPA-SWMM requires rainfall data as input to

the model. There are two National Oceanic and Atmospheric Administration (NOAA)

rain gauge stations (WBANNO: 54796 and 54797) in and around the Chipuxet

watershed (Figure 4). Both stations record data at daily 5-mins intervals. The collected

time-series data for 2006 to 2020 are continuous, having no missing data. Among this

time series data set, the 2017 and 2018 records were chosen to be the SWMM-5 model

calibration and validation, respectively.

44

2.2.2. Rainfall Data Preparation

Precipitation data server (PDS) based typical precipitation processes for six

rainfalls return occurrence intervals (1, 2, 5, 10, 25, 50, and 100 years) with five-minute

precipitation resolution and a minimum of 24h duration was investigated. All data,

including point precipitation frequency estimates with 90% confidence intervals and

supplementary information, were obtained from the NOAA Atlas (Table 1).

The rainfall depth ranges are from 3 inches (80 mm) to 8.59 inches (195 mm) of

24-hr precipitation for 1-yr to 100-yr (Figure 4). This study considered only one, two,

five, ten, fifty, and hundred years return periods to analyze the LID structures scenario.

Table 1: Point Rainfall Frequency (pf) estimates with 90% confidence intervals and

supplementary information (NOAA Atlas 14, volume 10, version 3).

Average

Recurrence

Interval,

Year

5-

min

10-

min

15-

min

30-

min

60-

min

2-

hr

3-hr 6-hr 12-

hr

24-

hr

1 8 12 14 19 24 32 37 48 61 74

2 10 14 17 23 30 39 45 58 73 88

5 13 18 21 30 38 50 59 74 92 111

10 15 22 25 36 45 60 70 88 108 129

25 19 26 31 43 56 73 85 107 130 155

50 21 30 35 49 63 83 96 121 147 175

100 24 34 40 55 71 93 108 135 164 195

45

PDS-based depth-duration-frequency (DDF) curves

Latitude: 41.74800, Longitude: -71.56180

Figure 4. Typical design rainfall processes of different return periods of 1-, 2-, 5-, 10-,

25-, 50-, and 100- yrs. (PDS: Precipitation Data Server)

2.3 Drainage Pipeline Data

Sewer mains and interceptors for public sewer systems from the State’s Utility data

[67] generally are only for pipes with 10 inches (0.25meter) or greater diameter.

Polyvinyl Chloride (PVC) sewer pipe and fittings are primarily used in the drainage

having 20 feet in length and 0.83 feet diameter. Due to the low to moderate topographic

slope (Figure 5), no overland flow occurs in this area unless heavy rainfall (> 32 mm/day;

Table 2), runoff water moves along the roads or pavements toward the low-lying areas

and flows into rivers. Consequently, the roads as the drainage channels are the central

part of the drainage system in this study (Figure 5).

46

Figure 5. Elevation and flow path, Soil infiltration rate, Land use, and the percentage

of impervious surface in the study area

47

Table 2: Categories of the rainfall

Rainfall Categories Rate of the precipitation

Light rain 0 - 4 mm/day

Light-Moderate 4 – 16 mm/day

Moderate - Heavy rain 16 – 32 mm/day

Heavy rain 32 - 64 mm/day

Heavy- Torrential rain 64 – 128 mm/day

Extreme or Torrential rain >128 mm/day

Source: Alpart et al., 2002

2.4 Elevation, Sub-catchment, Soil Infiltration, Land use and % Impervious Surface

Elevation data, including Digital Topographic Map (DTM) 1:2000 and Remote

Sensing Images (RSI) 1:2000, are required inputs for the SWMM model. Watershed

delineation (Figure 6) for the Chipuxet Watershed extracted from Digital Elevation

Model (DEM) file data and worked on in ArcGIS 10.6.1. Discharge outlet points can be

either nodes of the drainage system or a sub-catchment. Sub-basins are divided into

pervious and impervious subareas (Figure 6). Surface runoff can infiltrate into the upper

soil zone of the pervious subarea but not through the impervious subarea. Impervious

areas are divided into two subareas - one that contains depression storage and another

that does not. Runoff flow from one subarea in a sub-catchment can be routed to the

other subarea or both subareas.

Percent imperviousness is calculated from the land-use 2011 data, and perviousness

is also validated with the impervious data from RIGIS. The percent of imperviousness

48

is relatively high in the western part of the watershed, ranging from 9% to 46.5%. The

2001 Land use and land cover was updated here to assess the land types.

Figure 6: Sub-catchments, Nodes, and Streamflow Delineation of the watershed into

sub-basins of the study area with nodes, streamflow, and outlets.

49

A total of 49 sub-catchments were delineated. Each of the sub-catchment was

divided into two subareas: an impervious area, a pervious area. The areas and slopes of

each sub-catchment were calculated using ArcGIS. Then ArcGIS files were converted

into .inp files through inp.PINS application.

3. Methodology

3.1. Hydrological Model Selection

Researchers developed many methods for evaluating the effects of LID to study

highly urbanized watersheds; among them, hydrological models are considered the most

accurate method [23,26, 69-76]. Hydrological models, such as the Long-Term

Hydrologic Impact Assessment-Low Impact Development (L-THIA-LID) model [Liu

et al., 2015]; the improved SCS-CN model [77,78]; the System for Urban Storm-water

Treatment and Analysis Integration (SUSTAIN) model [79]; and the SWMM [78, 80-

82] all are well-established models to evaluate LID structures. In this study, the most

updated SWMM version 5.1 was applied for surface runoff simulation because it

provides realistic peak discharge and surface runoff simulation [82], and it is

supplemented with a LID module [22, 29,32]. SWMM with different LID structures

has been used widely for different regions and countries, such as the USA and China. It

must be noted that this study focuses on suburban and semi-urban-mixed watersheds

where the effectiveness of SWMM coupled with LID has not been well understood.

SWMM is open access and a flexible numerical model that simulates the stormwater

runoff quantity and quality based on the physical processes of surface runoff, infiltration,

surface ponding, and flow routing.

50

3.2 Input Parameterization

For any model simulation, proper selection of the initial parameter values is most

crucial. Previous studies related to SWMM [16,18] indicated that the width (W) of the

region, impervious coefficient, Manning coefficient (n), and depression storage (D) for

both the impervious and pervious areas are sensitive parameters for runoff simulation.

Width (W) depends on the shape and size of the watershed area and its topography and

underlying surface and can be calculated according to Equation (1) [18],

W = K * (0.2 <K<5) …………………………………………………………..(1)

where K is the shape correction coefficient, and A is the area of the watershed. K is

crucial for calculating W and was calibrated based on the measured geometric

characteristics. According to the shape of the study area, the initial value of K was

assigned as 1.1 in this study.

The volume of the surface runoff is directly related to the impervious coefficient.

In this study, all the initial k values were used based on the Rhode Island Stormwater

Design and Installation Standards Manual, 2010, and then the corresponding impervious

coefficients were obtained (Table 3).

Table 3. Summary of Sub-catchments Characteristics

Sub-basin

ID

Area

Sq.km

%

Imp

Slope N-Imp N-Per S-Imp S-Per Manning's

Roughness

SC_1 0.35 7.0 15 0.01 0.1 0.02 0.25 0.055

SC_2 0.40 15.9 8.5 0.01 0.1 0.02 0.25 0.055

SC_3 0.40 4.0 10 0.01 0.1 0.02 0.25 0.12

SC_4 1.20 0.0 8.5 0.01 0.1 0.02 0.25 0.12

51

SC_5 4.27 128 6 0.01 0.1 0.02 0.25 0.065

SC_6 1.23 61.5 12 0.01 0.1 0.02 0.25 0.03

SC_7 2.04 40.8 8 0.01 0.1 0.02 0.25 0.12

SC_8 1.16 23.1 6 0.01 0.1 0.02 0.25 0.12

SC_9 0.07 0.0 7.5 0.01 0.1 0.02 0.25 0.12

SC_10 1.31 26.2 15 0.01 0.1 0.02 0.25 0.12

SC_11 1.01 10.1 8 0.01 0.1 0.02 0.25 0.12

SC_12 0.30 9.1 7 0.01 0.1 0.02 0.25 0.12

SC_13 1.41 49.3 7 0.01 0.1 0.02 0.25 0.12

SC_14 0.72 28.9 12 0.01 0.1 0.02 0.25 0.055

SC_15 1.08 0.0 12 0.01 0.1 0.02 0.25 0.12

SC_16 0.09 3.8 12 0.01 0.1 0.02 0.25 0.023

SC_17 0.94 56.3 9 0.01 0.1 0.02 0.25 0.023

SC_18 0.36 1.8 8 0.01 0.1 0.02 0.25 0.12

SC_19 0.82 77.8 7.5 0.01 0.1 0.02 0.25 0.011

SC_20 1.37 0.0 7 0.01 0.1 0.02 0.25 0.12

SC_21 0.47 2.4 10 0.01 0.1 0.02 0.25 0.12

SC_22 0.67 0.0 7 0.01 0.1 0.02 0.25 0.12

SC_23 1.14 28.6 14 0.01 0.1 0.02 0.25 0.055

SC_24 1.72 8.6 13 0.01 0.1 0.02 0.25 0.12

SC_25 1.73 0.0 4 0.01 0.1 0.02 0.25 0.12

SC_26 0.34 0.0 11 0.01 0.1 0.02 0.25 0.055

SC_27 1.23 12.3 10 0.01 0.1 0.02 0.25 0.055

SC_28 0.98 4.9 3 0.01 0.1 0.02 0.25 0.12

SC_29 7.55 113 12 0.01 0.1 0.02 0.25 0.065

SC_30 0.69 27.4 12 0.01 0.1 0.02 0.25 0.12

SC_31 3.65 0.0 3 0.01 0.1 0.02 0.25 0.12

SC_32 2.31 46.3 7 0.01 0.1 0.02 0.25 0.055

52

SC_33 1.44 0.5 8 0.01 0.1 0.02 0.25 0.035

SC_34 0.81 8.1 12 0.01 0.1 0.02 0.25 0.012

SC_35 0.63 15.9 15 0.01 0.1 0.02 0.25 0.055

SC_36 0.20 0.0 4 0.01 0.1 0.02 0.25 0.12

SC_37 0.99 0.0 6.5 0.01 0.1 0.02 0.25 0.12

SC_38 0.02 0.0 9 0.01 0.1 0.02 0.25 0.055

SC_39 0.72 28.8 8 0.01 0.1 0.02 0.25 0.023

SC_40 1.86 27.9 14 0.01 0.1 0.02 0.25 0.065

SC_41 0.46 0.2 8 0.01 0.1 0.02 0.25 0.035

SC_42 3.45 41.4 12 0.01 0.1 0.02 0.25 0.055

SC_43 1.79 17.9 11 0.01 0.1 0.02 0.25 0.012

SC_44 0.61 18.4 9 0.01 0.1 0.02 0.25 0.055

SC_45 2.14 21.4 7 0.01 0.1 0.02 0.25 0.1

SC_46 0.69 13.7 8 0.01 0.1 0.02 0.25 0.055

SC_47 2.15 43.1 9 0.01 0.1 0.02 0.25 0.055

SC_48 1.21 0.0 6 0.01 0.1 0.02 0.25 0.12

SC_49 0.41 18.3 9 0.01 0.1 0.02 0.25 0.023

The impervious and pervious areas influence the sub-surface properties, and initial

values are all empirical. The study area is moderately low in elevation does not vary

much; the initial n of impervious areas (channels) was set as 0.25, and the initial n of

pervious areas were set as 0.9, according to the manual of SWMM 5.0 [32]. For the

model initialization, D of the impervious and pervious areas was 1.7 mm and 6 mm,

respectively, based on the point frequency estimate presented in Table 2.

53

For the model calibration and validation, the initial sensitive parameters were

considered the optimization parameters, and the Horton model was applied here for the

infiltration processes. The infiltration (f) was calculated as Equation (2) [18,22],

f = fc + (f0 − fc) e−kt ………………………………………………………………(2)

where f0 and fc are the minimum and maximum infiltration rates (mm/sec), respectively,

and k is the attenuation coefficient [-]. Although fc and f0 are less sensitive than the k

and W parameters, they are critical in infiltration calculation. The other SWMM

parameters, such as cross-section size, the characteristics of the drainage pipeline

(channel), and slope, were calculated based on the DEM, and investigation data (Table

3). The drainage plan was collected from the stormwater management plan report on

the South Kingstown area [84].

3.3 Model Calibration and Validation

SWMM was calibrated with discharge data from 2017. Three individual storm

events were considered for model calibration and validation with the discharge data

from USGS 01117350 Chipuxet river at West Kingston, RI. Calibration was conducted

for three different model sets up (Run 1, Run 2 and Run 3) in three different sub-

catchments. The adjusted validation parameter is presented in Table 5.

Figures 7 shows the observed, and simulated discharge hydrograph for the three

different models. The optimal parameter values for the study area were selected (Table

5) according to the Nash-Sutcliffe Efficiency (NSE) [85].

NSE = 1 − ∑ (Qsim,t−Qobs,t)2n

t

∑ (Qobs,t−Q͞obs)2nt

…………………………..…………….…………(3)

54

where Qsim,t [m3/s] is the predicted discharge, Qobs,t [m

3/s] is the observed discharge, Q¯

obs [m3/s] is the mean of the observed discharge and n is the number of time steps and t

is the time. NSE ranges between −∞ and 1, where 1 is considered a perfect fit for any

model where NSE is sensitive to extremes [86]. Dongquan et al. 2009 [87] suggests that

an NSE value of 0.5 or greater is acceptable for SWMM simulation. The coefficient of

determination, R2, an established measure in statistics quantifying the model’s fit, is

described as:

R2 = 1 −∑ (Pj−P^j)2m

j=1

∑ (Pj−P͞j)2mj=1

……………………………………………………..………..(4)

where Pj and Pjˆ are the observed and estimated values, Pj¯ is the observed mean, and

m is the number of discharge peaks. The denominator is the total variance in the

observed data, and the numerator is the variance between the simulated and observed

data. The closer R2 is to 1, the larger proportions of the observed variability can be

explained with the simulations of estimated values [88]. As predicting runoff peaks is

one of the main purposes of this suburban hydrological models, R2 of observed and

predicted runoff peaks provide the measure of performance measures.

3.4 LID Selection and Design Scenarios

SWMM 5.1 includes modules for LID components including permeable pavement

(PP), bio-retention cells (BR), and vegetative swales (VS). These are the most common

LID in the study according to the Stormwater Management Plan [84] and the Rhode

Island Stormwater Design and Installation Standards Manual [89]. The SWMM-LID

implementation considers the landscaping schemes and how the local constructions are

planned to mean that the Land use criteria, local drainage, and construction plans are

55

taken into consideration. SWMM-LID preserves the original terrain, limits the ratio of

impervious surface areas, and avoids the direct connection of impervious areas. Also,

suitable LID structure selection is made according to local conditions in terms of both

technical and social/economic factors, and the specific stormwater problem of the

selected area. In this study, two large storm events in 2010 and 2013 and their impacts

on this area and the suburban growth [54] problems are considered before implementing

the LID structures for the developed SWMM model. These two aforementioned storms

resulted in large flood and infrastructural damages.

Scenarios for PP, BR, and VS were developed, considering storm design events (one,

two, five, ten, fifty, and hundred year). The storm, soil, and LID design parameters of

each LID (Table 4) measurement was assigned according to the soil infiltration rate,

land use, and elevation, and local technical regulation [88]. The peak infiltration rate in

determining runoff capture is computed based on the input parameters of the selected

LID structures.

3.4.1 Baseline Scenario

The calibrated SWMM having no LID at the outfall served as the baseline

scenario and was compared to the model that included PP, BR, and VS.

3.4.2 Permeable Pavement (PP) Scenario

Properly maintained permeable pavements are highly effective for parking lots or

where the percentage of impervious surfaces is elevated. In this model, permeable

pavements were set in the University of Rhode Island parking lot and the municipal

56

sidewalks. The PP ratio of the sidewalks was set as 90% and that of the residential

community roads was 70%. The surface layer thickness was approximately 2 cm with

0.65 porosity; the entire layer thickness was 50.8 cm, and 1.3 cm/hr was considered as

the infiltration rate according to the Rhode Island stormwater management technical

regulation [89]. Slopes (<9%), percentage of underlying soil (Clay content <20%,

Silt<40%) were assigned based on [84]. Table 4 represents all input parameters of the

selected LID structures.

3.4.3 Bio-Retention (BR) Cell Scenarios

Bioretention (BR) reduces runoff volume, and peak discharge rate and it is also

capable of reducing the pollutant levels [33, 34]. BR is primarily effective in low slope

areas (5% to 15%), such as this study area.

BR cells were in the northwest part of the watershed. The required parameter values

(Table 4) used in the model were based on the Rhode Island stormwater management

structure manual. The depth of the surface storage was 21 cm with 90% vegetation

coverage; the soil thickness was 12 inches with 0.15 porosity. According to the duration

depth and frequency curve, the precipitation depth is 8 cm. The field capacity was 0.2,

and the wilting point was 0.2. The conductivity coefficient of the soil layer was 6, with

1.1 cm/hr hydraulic conductivity. The gravel/stone layer height was 25 cm with a 20.32

suction head of media layer. The other required parameter i.e., surface roughness 0.5

(manning’s, n), ponding layer (6 – 9) cm, was taken based on [84].

57

Table 4: Summary of LID Characteristics

Parameter Permeable

Pavement

Bioretention

Cell

Vegetative

Swale

Surface Layer

Storage Depth (cm) 2 21 18

Vegetation (%) 0 90 100

Media Layer

Depth (cm) 50.8 31 12.7

Porosity (%) 0.65 0.15 0.25

Field Capacity (%) n/a 35 12

Hyd.cond (cm/hr) 2 3.6 0.85

Cond.slope n/a 6 15

Suction head n/a 20.32 2.5

Stone Layer

Depth (cm) 18-25 25 86

Void ratio (v/s) 0.65 67 67

Soil hyd.cond.

(cm/hr)

2.3 1.3 1.1

Underdrain-free

Coefficient 3.8 4 5

Exponent 0.5 0.5 0.5

Offset (cm) 3.1 0 10.1

Underdrain-controlled

58

Coefficient 2.7 0.55 0.25

Offset (cm) 21-39 2.9 4.7

3.4.4 Vegetative Swale (VS) Scenarios

VS are considered an important type of LID due to their design and effectiveness.

Because of its dense grass cover, the roughness of the swales is significantly higher than

that of the streambed conduits; simultaneously, VS also provides good infiltration. VS

is also applicable to water quality treatment through sedimentation and biological

uptake. VS was set in six Sub-catchments (SC 7,13,17,21,39, and 49) where the slope

is in between 7% to 14% and the area of %impervious was greater than 10 sq.km of the

study area (Table 3). The total swale length was 500 m, and the total storage depth of

the surface layer was approximately 18 cm. The Manning’s n for the surface bed was

taken as 0.5 while a value of 0.24 was used for the swales. The hydraulic conductivity

of saturated soil was 0.85 cm/hr, and the soil porosity was 0.25. All the input parameters

are represented in Table 4.

3.5 Assessment of the LID envrionmental benefits

LID structures are usually cost-effective and robust approaches for stormwater

management bring social, economic, public health, and environmental benefits to

communities. It typically influences the physical environment by reducing impervious

surfaces and constructing natural habitat and permeable surfaces. In this study,

comprehensive environmental benefits are assessed based on two sub-indicator runoff

reduction and peak reduction. It was simulated to compare the reduction rates before

and after the addition of LID scenarios.

59

3.5.1 Calculation of Runoff and Peak Reduction

Runoff volume and peak reduction is calculated for each of the LID structures

to assess its effectiveness using equation 4 [89],

R (%) = Q0−QLID

Q0 x 100(%) ……………………………………………………. (5)

Where R is the reduction rate in %, Q0 (cm/s) is the simulated runoff without LID

structure, and QLID (m3/s) is the simulated runoff with LID.

3.5.2 Calculation of LID environmental benefits

The benefits of each LID were measured in this study, applying the analytical

hierarchy process (AHP). The individual benefits were calculated using a weighted

summation of indicator values applying equation 6:

Bim= ∑ Wkik Ikm …………………………………………………………………… (6)

where Bim is the benefit i in the scenario m, Wki refers to the calculated weight of

indicator k in benefit i, and lkm is the normalized value of the indicator k in scenario m.

Linear normalization makes the indicator values dimensionless, equation 7:

lkm = Xkm

∑ XkmMm=1

…………………………………………….……………………… (7)

where Xkm is the original value of the indicator k in scenario m, and M is the total

number of the LID scenario; in this study M is 4.

The performance in the AHP analysis is a product of relative weight of the

category and estimated the sub-indicator of runoff reduction and peak reduction for each

60

of the LID scenarios. The comprehenssive environmental benefit was determined for

four different LID scenarios applying four different weight indicator (20%, 40%, 60%,

and 80%). Indicators represents the area increased for each of the LID controls. For

example, if the permeable pavement area is increased 20% then how much runoff

volume and peak could be reduced compared with the original value. Each of the LID

structures area is increased under the same hydrological (rainfall, duration of rainfall,

and intensity), landuse, soil infiltration rate, and the topographical (slope, elevation).

Runoff volume and peak discharge reduction were assessed then for each of the LID

structures (PP, BR, and VS) under different weight indicator.

4. Result and Discussion

4.1 SWMM Calibration and Validation

The obtained NSE in run 1 is 0.73, in run 2 is 0.58, and for run 3 it is 0.64 (Table

5). The R2 is 0.88, 0.68, and 0.73 for run1, 2 and 4 respectively (Table 5).

61

Table 5: SWMM Calibration Parameter

Calibration Run 1 (Sub-catchment 24) :NSE = 0.73

Parameter Adjustment Measured Run 1 Run 2 Run 3

% Impervious ….... x 1.0 X 0.23 X 0.25

Depression Storage (mm) ….... 2.032 1.524 2.54

fo & fc ….... 1 1 1

Sub-catchment Width ….... 1 1 1

Impervious "n" ….... 0.02 0.02 0.02

Pipe "n" ….... 0.013 0.013 0.013

Depth of Runoff (mm) 1.15 5 1.8 1.15

Maximum Flow (cms) 5.3 4.43 6.9 7.1

Time of Max.Flow 9:45 9:40 9:45 9:40

62



Figure 7: Time series of the observed (green) and simulated (red) discharge [m3/s] for

Run 1, 2, and 3 during the calibration period. The precipitation rate [mm/sec] for the

corresponding time of discharge is included on the secondary axis (blue).

63

Table 6: Comparison between three calibrations run

Calibration of SWMM Sub-catchment NSE R2

Run 1 Sub 24 0.73 0.88

Run 2 Sub 18 0.59 0.68

Run 3 Sub 33 0.64 0.73

4.2 Results in Baseline Scenario

The runoff processes under different LID scenarios (Figure 8) were simulated in

the SWMM model and their corresponding runoff depths (Tab.7) were found to be

proportional to the return period, i.e., with the increasing return period, the runoff depth

is increased. Among the four different LID scenarios simulated, BR and PP showed the

highest impact in terms of runoff volume and peak reduction in the study area.

(a) (b)

(c)

(d)

64

Figure 8: Simulated runoff processes in four LID structures with different return-

period events: (a) One year; (b) Two-year; (c) five-year (d) ten-year (e) 50-year, and

(f) 100-year

4.3 Runoff Volume Reduction

The runoff reduction effectiveness (Figure 9) was evaluated through the

percentages of runoff reduction calculation and analysis (Table 8), considering the four

LID structures. Among all, PP is the most effective in runoff reduction, especially when

the return period of precipitation is short i.e., 1-year and 2-years. For PP, the runoff

reduction percentages ranged from 31% to 78% in all types of rainfall events, and the

peak reduction percentages range was 28 %to 70%. The reduction rate is much higher

in the one-year and two-year return period rainfall event than rest of the rainfall return

periods. It means, over the time the performances are gradually decreasing, and this

output is the same for all applied LID structures in this study.

(f) (e)

65

Table 7. Percentage of runoff and peak reduction per unit area under six rainfall

reoccurrence scenarios.

LID Measures Runoff Reduction/ha (%) Peak Reduction/ha (%)

1

yr

2

yr

5

yr

10

yr

50

yr

100

yr

1

yr

2

yr

5

yr

10

yr

50

yr

100

yr

Permeable Pavement (PP) 78 65 58 58 45 31 70 61 45 45 32 28

Bio-retention cell (BR) 70 70 55 52 48 35 72 71 50 41 33 24

Vegetative Swale (VS) 60 65 45 45 36 28 55 52 40 40 23 19

Figure 9. Hydrological effectiveness of each LID per unit area.

BRs were effective for the study area, especially where the slope is relatively low.

Hydraulic gradient and the gravel pack design are the most impactful BR design

parameters. Besides, 90% vegetation cover has a significant impact in reducing the

runoff volume. BR showed the same characteristics that PP exhibited on reducing runoff

66

percentages, with the longer return period (runoff reduction rate ranged from 35% to

70%). This result suggests that BR cells are effective in various rainfall intensities.

Applying various measures or structures changes the state and process of the runoff in

part of the study area, which likely balances some of the overall effectiveness.

In terms of runoff reduction, VS was the least effective among all three LIDs

evaluated herein, with the runoff reduction ranging from 28% to 60%. Location and the

percentages of the impervious area are likely the most important factors determining the

relative performance of VS. Usually, VS are effective in flat land where the slope is <

10% [91-93], but the selection of the location and the proper design is essential for

getting the most significant outcomes.

Overall, all three LID structures can reduce the runoff volume between 28% to 78%

during rainfall events with different return periods. It is noticeable that the volume

reduction range is higher for shorter return periods and that LIDs are less effective if the

rainfall events in the return periods is higher than five years. The effectiveness of runoff

reduction in all scenarios decreased as the rainfall intensity increased. Proper

maintenance of the LID structures could increase their performance for long period of

time.

4.4 Peak Discharge Reduction

The peak discharge reduction was calculated to evaluate the effectiveness of the

LID structures (permeable pavement, bioretention cells, and vegetative swales) (Table

8). All LID structures showed significant outputs in terms of runoff volume and peak

67

reduction for the study area (Figure 9). The calculated percentage peak flow reduction

of the LID scenarios (Table 8) was based on the rainfall return periods assessing and

comparing their suitability. The analysis showed that storage volumes within BR cells

could be depleted during significant storm events. In 2013, Qin et al. [94] suggested that

BR performance significantly varies from storm to storm in urban watersheds, and in

the suburban study, it showed the same performance for BR. In this study, LID

structures were designed based on 1, 2, 5, 10, 50, and 100 years return period of rainfall

intensity. The performance of each LID structure decreased with increased storm

intensities. VS were less effective for peak reduction (19% - 55%) compared to BR and

PP, although VS had an effective storage capacity (18 cm) in the study area. The reason

for the lower VS effectiveness could be related to their size and frequency which

influences their total water storage capacity. For peak reduction, permeable pavement

(28% - 70%) and bioretention cells (24% - 72%) are most effective (Table 8 and Figure

9) for the selected location.

In summary, the proper implementation of LID structures was far from ideal for

solving the interval problems of flood in the suburban study areas. Previous research

supports these analyses [95]. A regional flood risk map should be prepared based on the

simulation results of the ponding depth. Public awareness needs to be created for the

inhabitants in the low-lying areas which are also the high-risk areas for the regions

studied.

68

4.5 Environmental Benefits

Environmental benefits were represented by two corresponding sub-indicator of

runoff volume and peak flow reduction, where it measured their benefits after

implementing the LID structures. The comprehensive environmental benefits of PP, BR,

and VS for six different rainfall return periods are represented in Table 8.

Table 8: Environmental benefits for different LID stuctures

Types of LID Facilities

Rainfall Return Period (%)

1 yr 2 yr 5 yr 10 yr 50 yr 100 yr

Permeable Pavement (PP) 75 64 54 54 41 30

Bio-retention Cell (BR) 71 70 56 48 43 31

Vegetative Swale (VS) 58 54 43 43 32 25

The reduction rates of runoff volume and peak increased with the increase of the

size and numbers of the LID structures (Figure 10). Again, the effectiveness of the LID

structures decreased with the increase of rainfall intensities; it is also applicable for the

environmental benefits (Figure 10).

0 20 40 60 80 100

1 yr

2 yr

5 yr

10 yr

50 yr

100 yr

Environmental Benefit (%)

Vegetative Swale (VS) Bio-retention Cell (BR) Porous Pavement (PP)

69

Figure 10: Environmental benefits and its standard error for each of the LID structures

in different rainfall intensities. The standard error of the mean was applied to construct

a confidence interval (95%) for the environmental benefits for each of the LID structures.

Finally, the following equation are applied to determine the environmental benefits for

three selected LID structures in terms of six different rainfall return period:

Environmental Benefits (%) = 0.667 * runoff volume reduction + 0.333* peak reduction.

The reduction rates of LID facilities for different rainfall intensities per unit area

(sq. km) were ranked as follows: BR > PP > VS. Bioretention cell showed a consistent

effectiveness in every rainfall intensity even for the 100 years return period (31% ± 5%).

5. Conclusions

Unlike previous studies which applied SWMM to mostly urbanized watersheds,

this study extended the SWMM model to a data-sparse suburban area. Because of the

importance of suburban sprawl in most countries, combined with an increasing

likelihood of flooding events in the future, more studies of stormwater management in

suburban watershed are needed. Input data covered rainfall data for different return

periods and the digital input of the drainage pipe networks was the basis for delineating

49 sub-catchments. Three different LID structures were considered, i.e., permeable

pavement, bioretention cell, and vegetative swales. The model calibration and validation

were conducted for three set ups i.e., run 1, run 2, and run 3, in three different sub-

catchments. The respective NSE values were 0.73, 0.59, and 0.64, with an average

value of 0.65. The average R2 value was 0.76. Both NSE and R2 indicated a good fit of

the SWMM model in this study area. Further, four additional scenarios were modeled

70

with the ‘no LID’ serving as the baseline to which PP, BR, and VS LIDs were compared

to by evaluating their response to runoff volume reduction and the peak discharge

reduction. The simulations indicate that all three LID structures were maximum

efficient during one-year to five-year rainfall events only. During more severe storms,

PP and BR exhibited a significant reduction in their effectiveness controlling runoff

volume and peak discharge. This study suggests that the LID performance is location

specific with design and input parameters (Table 4) being are the controlling factors.

Environmental benefit analysis also suggest that LID structures are effective for the

suburban areas. Most importantly, permeable pavement and bioretention showed the

significant impact in terms of runoff and peak discharge reduction for this suburban area.

Moreover, through the proper and regular management and monitoring of various

LID structures, the effectiveness in various aspects could make additional improve

under all scenarios. It is noted that the effectiveness of the LID structures is inversely

correlated to the rainfall intensity in terms of all the selected aspects. This study has also

revealed that for the suburban area bioretention cell, and the permeable pavement are

the applicable LID structure for managing the peak and runoff volume.

Acknowledgments:

The authors acknowledge the funding obtained from the Rhode Island Department of

Transportation (RIDOT)- SPR-234-2362, USDA Multistate Hatch Grants S1063 and S1089,

and the College of Environment and Life Sciences, University of Rhode Island. This research

and associated analysis are done through Hydro-Systems and Water Quality Lab (Pradhanang

Lab) assistance. The authors like to special thank William Brown, Meteorologist from NOAA's

National Centers for Environmental Information, for providing data support, especially for the

suburban area. The authors are also thankful to the CHI for providing a PCSWMM fellowship.

71

Conflicts of Interest: There is no conflict of interest.

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79

CHAPTER 3

Manuscript III

Performance Evaluation of LID Controls for Runoff Quality Using SWMM

in Suburban Watershed

prepared for the part II submission to the Hydrological Sciences Journal-IAH,

July 2021

Khurshid Jahan1,*, Soni M Pradhanang1,*, and Rebecca Brown2

1Department of Geosciences, University of Rhode Island, RI, Kingston, USA 2Department of Plant Science and Entomology, University of Rhode Island, Kingston,

RI, USA

*Correspondence: [email protected] (K.J.), [email protected]

(S.M.P)

mailto:[email protected]


80

Abstract

Suburban growth impacts the surface runoff; therefore, stormwater runoff management

becomes a prime concern for suburban areas due to its impact on quantity and quality.

The study conducted in the suburban watershed to assess the effectiveness of the Low

Impact Development (LID) controls in terms of pollutant reduction. LID practices have

been proposed as a promising suburban management technology to control the runoff

pollutant or mitigate the environmental issues arising from increasing the impervious

cover. The water quality module of LID in the stormwater management model (SWMM)

was used through the RUNOFF block to develop the SWMM-LID model in this study.

A total of 42 individual rainfall events were used in the model simulation. Among them,

30 rainfall events were used for model calibration, and 12 events were used for model

validation. We evaluated the model performance for simulating total suspended

sediments (TSS), Nitrate-N, and Orthophosphate-P of the LID facilities for three

different scenarios i.e., Bioretention, Permeable Pavement, and the vegetative swale.

The selected LID controls occupied 11% of each sub-catchment, and the LID pollutant

removal efficiency was up to 27% to 83% for the TSS, 11% to 28% for Nitrate-N, and

14% to 27% for Orthophosphate-P. Rhode Island stormwater management group

already started their work also in the suburban areas. So, these studies will the associated

authority to implement the LID controls with a proper design and maintenance plan.

Keywords: Suburban growth, Low Impact Development, RUNOFF block, pollutant

removal efficiency, environmental issue, water quality module.

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1. Introduction

Urban growth disturbs water resources and water quality [1]. Change of natural

hydrological systems due to urbanization can be seen in the form of increased runoff

rate and volume, decreased infiltration, groundwater recharge, baseflow, and water

quality deterioration in streams and rivers [2-7]. Hence, the urban land management to

address and mitigate the water-associated challenges, including the pollutants

generation and urban runoff in the environmental cycle, is a big challenge for urban and

suburban areas [4,7]. The U.S Environmental Protection Agency [8] considers nonpoint

sources as the leading threat to water quality in the Nation through the stormwater runoff.

Therefore, urban management practices such as Low Impact Development (LID)

emerged to control runoff and its pollutants near their source areas [9]. LID refers to

decentralized systems of small-scale treatment units located at or near the source area

[10]. The source areas consist of various urban land-use and land cover types, such as

streets, parking lots, construction, commercial, residential, recreational, and agricultural

landscapes. The impervious surfaces are largely responsible for the contribution of

runoff and pollutants [11]. On the other hand, pervious surfaces such as small sized

lawns, garden may also become essential contributors to pollutant sources during

significant storm events, although many stormwater management models ignore their

contribution [12], for example, SUDS, LTHIA-LID etc.

Stormwater runoff usually contains many different pollutants. Stormwater

monitoring and management programs in the urban catchment are crucial for

characterizing stormwater runoff quality and formulating an effective pollution control

82

strategy. However, stormwater quality monitoring is often too costly, time-consuming,

and laborious. As a result, computer models have become useful tools for simulating

pollutant transport and predicting stormwater pollutants based in available data. The

ultimate goals of urban water quality modeling are to characterize urban runoff, provide

input to the analysis of receiving water, determine the appropriate size of control

structures, and perform frequency analysis of water quality parameters [13]. A range of

climate mitigation measures is available for urban stormwater management. One is to

implement low-impact development (LID) practices [14]. Low impact development

(LID) is a stormwater management approach that mimics a site's natural hydrology as

the landscape is developed. The calculation of predevelopment hydrology is based on

native soil and vegetation. In the past decade, low impact development (LID) practices

have been proposed to deliver better hydrologic and environmental functions [10, 15].

LID technique provides various tools to reduce or control the hydrologic and water

quality effects of urbanization [10,16-18]. Many communities across the United States

are now implementing LID practices to meet stormwater management goals [1, 19, 20

Research groups formulated guidelines for the efficient implementation in different

locations of LID through modeling approaches, which help evaluate the LID

performance with various urban management scenarios [10, 21, 22].

Several research groups have developed numerical models to simulate the LID

process, e.g., Stormwater Management Model (SWMM) [23], System for Urban

Stormwater Treatment and Analysis (SUSTAIN) [24], Western Washington Hydrology

Model (WWHM) [25], and L-THIA Low Impact Development (L-THIA_LID) [26].

83

These models can simulate the effects of LID on the hydrological processes and have

also been widely applied to evaluate pollution reduction in urban areas [10, 27 – 30].

According to USGS 2019 [31] study, LID infrastructure can reduce the effects of

suburban development on streams by storing and infiltrating runoff on the landscape

before it reaches streams. The study found that LID controls in Maryland were

particularly effective at mitigating runoff for less than 20.32 mm rainfall events (1-year,

24-hour event equivalent to 66.04 mm of rainfall) but similar to the 1-inch (25.4 mm)

design criteria for all other stormwater practices.

LID performances differ from one rainfall return period to another, and therefore,

the runoff pollutant that each precipitation event contribute could vary. The main goal

of this paper is to develop a stormwater management model for the suburban catchment

to predict urban stormwater quality based on pollutant buildup and washoff concept. It

demonstrates that physically based models can be developed with various degrees of

complexity in different rainfall-runoff transformations depending on the desired levels

of model performance.

This research study aims to evaluate the effects of LID controls on hydrology

and water quality at a suburban watershed scale using EPA SWMM 5.1. The field data

were collected from 2016 to 2019. In this study, bioretention, permeable pavement, and

vegetated swale, among all types of LID controls, were selected to be employed to

impervious areas to reduce the effect of imperviousness on hydrology, and water quality

at the selected study area. The selected LIDs have the advantage of controlling the

84

stormwater quantity and quality at the source, reducing the flow volume, and thus

delaying the hydrologic response and reducing the pollutant load washed-off from urban

surfaces [31]. In this study, the focus is given in evaluating runoff quality in suburban

landscapes. However, few studies already proved that suburban growth also

significantly impacted quantity and quality in the suburban watershed [33,34]. There

are many benefits of LID controls in the urban and well as suburban catchments.

Previous studies have investigated the impact of different types of LID controls on urban

runoff flooding and water quality. For example, Hunt et al. 2008 [35] found the

bioretention cells can reduce the average peak flows by at least 45% in North Carolina

and Maryland during a series of rainfall events with high intensity. It has also been

proved in Dietz 2007 [36] study that bioretention can reduce sediment and nutrient up

to 99%. Another study carried out on vegetative swales by Ahiablame et al. 2012a [27]

that vegetative swale has an average retention range from 14% to 98% for nutrients and

TSS, and up to 93% for metals. Jackisch and Weiler [37] conducted a study on the

hydrological performance of permeable pavement vegetative swale, and bioretention

that revealed that the selected LIDs could capture 73% of surface runoff and the runoff

volume reduction range from 77 – 87%. Wilson et al. [38] compared the LID

performance in terms of water quality with the conventional development and the

outcome depicted that LID control water quality performance was way better than the

conventional systems. All the results confirmed that LID could replace the conventional

stormwater management systems. Many more similar research investigate the

hydrological and water quality execution of LIDs [39–44].

85

In this study, six different rainfall return periods and three individual scenarios

are analyzed. Six different rainfall return periods includes 1-yr, 2-yr, 5-yr, 10-yr, 50-yr

and 100-yr. The three developed scenarios are a) Bioretention (BR), b) Permeable

Pavement (PP), and c) Vegetative Swale (VS). The runoff quantity study conducted by

Jahan et al (unpublished) reported a better performance of PP and BR than VS in terms

of runoff depth and peak reduction [45], Jahan et al., 2021, unpublished]. Environmental

benefits were also measured for all three LIDs.

Table 1: Percentage of runoff depth and peak reduction per unit area under six rainfall

re-occurrences

LID Measures Runoff Reduction/ha (%) Peak Reduction/ha (%)

1

yr

2

yr

5

yr

10

yr

50

yr

100

yr

1

yr

2

yr

5

yr

10

yr

50

yr

100

yr

Permeable Pavement (PP) 78 65 58 58 45 31 70 61 45 45 32 28

Bio-retention cell (BR) 70 70 55 52 48 35 72 75 50 41 33 24

Vegetative Swale (VS) 60 65 45 45 36 28 55 52 40 40 23 19

Since this study aims at understanding how various rainfall intensities impact

runoff qualities, the various LIDs scenarios will allow us to evaluate how they perform

for higher rainfall intensities. Water quality parameters considered are Nitrate-N,

Orthophosphate-P, and Total suspended Sediments (TSS). The surface runoff quality

data were collected from the installed v-notch weir point at the study area; V-notch weir

mainly installed in the low flow zone to collect the stormwater runoff sample. For our

study site, we installed 300 V-notch weirs having 0.2 feet minimum head in all the six

86

monitoring sites. After collecting samples laboratory analysis was done in the

University of Rhode Island, Hydro-Systems and Water Quality Lab (Pradhanang Lab).

The stormwater data was collected for three years (from August 2016 to July 2019). To

develop the SWMM-LID model, the historical rainfall-flow data were also retrieved

from the on-site automated rain gauge station for model validation. Other necessary data

(drainage plan, conduit length, conduit roughness, installed LID design parameter

especially the BR, PP, and the VS) were collected via site inspection, literature review,

site-specific stormwater management report, and vector and raster maps. Sensitivity

analysis and model calibration were also carried out to identify the model's most

sensitive parameters. The significance of the study was to endorse the idea that LID

application in suburban areas can reduce stormwater pollutants and provide decision

support tool for retrofitting conventional stormwater management systems.

2. Methodology

This methodology explains the detailed process of SWMM-LID model

development for suburban stormwater runoff quality based on the buildup and washoff

concept [46, 47]. It also describes the model accuracy and performance with various

degrees of complexity due to heterogeneities in landscape shown by different water

quantity and quality parameter sensitivities Finally, the performance of the LID controls

are also demonstrated in terms of stormwater pollutant removal.

87

2.1 Study Area

This research was carried out in the Chipuxet watershed located in South

Kingstown, Rhode Island. The selected study area is split into 12 different sub-

catchments (Figure 1). The catchment has one rainfall gauge station (NOAA Station ID

54796), one automated stream gauge flow (USGS 01117350) run by USGS. Stormwater

quality monitoring sites are established near the parking lot, agricultural practice area

within the University of Rhode Island complex (Figure 1). Among Six monitoring sites,

four sites are adjacent to the agricultural land and two other sites are close to URI

parking lot. The conducted area is characterized with 10 to 20% of clay and 50-90% of

loam, silt loam, silt, or sandy clay loam. Soils consisting of clay, silt and sandy loam

have moderately low runoff potential when thoroughly wet and the soil infiltration range

is 0.15 – 0.30 in/hr [33]. The monthly average potential evapotranspiration data is

obtained from Northeast Regional Climate Center [48]. The average monthly potential

evapotranspiration estimated based on the NRCC’s adaptation of the MORECS model.

Stormwater runoff quantity and quality was measured with the samples collected

from the forty-two individual storm events from six different sites with a 600 ml bottle.

The samples were collected from 2016 to 2019. The area is a low elevated region having

a low to moderate slope. The slope of the study site ranged from 7 % to 10 %. The land-

use is considered suburban. The recent study has revealed suburban growth of about

68.7% from 1994 to 2020. Detailed characteristics of the selected research sites are

represented in Table 2.

88

Figure 1: Subdivision of the conducted study area for developed SWMM-LID

Table 2: Input parameters of the sub-catchment for the developed SWMM-LID

Sub_

ID

Area

(sq.k

m)

Width

(km)

P_Im

p

%Slo

pe

N_Imp N_Per D_Store

Imp

D_Store

_Per

Manning

’s N

Sub_1 0.08 0.03 0 7 0.01 0.1 0.02 0.25 0.023

Sub_2 0.03 0.01 0 7 0.01 0.1 0.02 0.25 0.023

Sub_3 0.10 0.05 0.05 8 0.01 0.1 0.02 0.25 0.023

Sub_4 0.03 0.01 0 9 0.01 0.1 0.02 0.25 0.02

Sub_5 0.09 0.04 0 7 0.01 0.1 0.02 0.25 0.12

Sub_6 0.04 0.02 0.038 10 0.01 0.1 0.02 0.25 0.12

Sub_7 0.17 0.07 0.07 10 0.01 0.1 0.02 0.25 0.12

89

Sub_8 0.13 0.06 0.035 8 0.01 0.1 0.02 0.25 0.12

Sub_9 0.08 0.03 0.014 7 0.01 0.1 0.02 0.25 0.12

Sub_10 0.09 0.04 0.01 7 0.01 0.1 0.02 0.25 0.023

Sub_11 0.15 0.07 0.04 9 0.01 0.1 0.02 0.25 0.023

Sub_12 0.16 0.07 0.05 10 0.01 0.1 0.02 0.25 0.12

P_Imp: % imperviousness, N-Imp: Mannings N of impervious area, N_Per: Mannings

N of pervious area, D_Store Imp: Depression storage on impervious area, D_Store_Per:

Depression storage on pervious area, Manning’s N: Manning’s coefficient

2.2 Model DescriptionThe EPA’s SWMM is a single event or continuous

hydrological and water quality simulation model developed principally for urban areas

[49]. The flowchart of the SWMM development for the study site is presented Figure 2.

SWMM consists of four different computational modules or blocks (runoff,

storage/treatment, transport, and extran) that can simulate various hydrological cycle

components such as rainfall, snow, interception, depression storage, infiltration, runoff

etc. The RUNOFF block was applied in this study to predict stormwater runoff at the

single rainfall events time scale.

Hydrological computations in the RUNOFF block are based on the theory of

nonlinear reservoirs. Time of concentration is computed based on the kinematic wave

theory. Two pervious area infiltration loss equations are available, the Horton and

Green-Ampt. The Green-Ampt equation (Equation 1) was used here. It has the

advantage over the Horton equation of using physically based parameters that can be

determined a priori [49]. These are the average capillary suction head, Su (mm), at the

90

wetting front, the initial moisture deficit (IMD) in mm/mm, and the saturated hydraulic

conductivity, Ks of the soil (mm/h). Therefore, this semi-theoretical equation was used

instead of the Horton equation because of the availability of relevant soil data from

pervious USGS studies and the local Natural Resources Conservation Service (U.S.

Department of Agriculture). The modified Green-Ampt equation proposed by Mein and

Larson (1973) [50] was used to estimate f1 (Rossman and Huber, 2016) [51]:

f1 = Ks(1+(𝜑− Ɵ1)(𝑑1+𝜓)

𝐹) ………………………………………………………….(1)

where f1 is the infiltration flux [cm/min], Ks is the saturated hydraulic conductivity of

the soil in LID (mm/min), φ is the soil porosity [-], Ɵ1 is the moisture content of the soil

in LID [-], 𝜓 is the suction head at the infiltration wetting front [mm], d1 is the ponding

depth on the surface [mm], and F is the cumulative infiltration volume per unit area

[mm]. Mein and Larson’s modified Green-Ampt equation suggests that the infiltration

rate equals rainfall intensity when the soil has not reached its saturation point [52].

The model simulates the rate of soil percolation (f2) in the soil layer through the

soil into the storage layer. The soil percolation can be a calculation based on Darcy’s

law which followed the same method employed in the groundwater module of the

SWMM [53]:

f2 = Ksexp (-HCO(φ-Ɵ2)), Ɵ2 > ƟFC ………………………………………………………………………….(2)

f2 = 0, Ɵ2 ≤ ƟFC

where, f2 is the soil percolation rate [cm/min], HCO is the decay constant derived from

the moisture retention curve [-], and θFC is the field capacity moisture content of the soil

[-].

91

The exfiltration rate (f3) from the storage zone into the native soil was estimated

for the storage layer. The following equation was used to calculate f3 [53]:

f3 = f2 – φ2𝜕𝑑2

𝜕𝑡, f3<K3 ……………………………………………………………………………………………………(3)

f3 = K3, f3 ≥ K3 …………………………………….………………………………………………… …………………..(4)

where, f3 is the exfiltration rate [mm/min], φ2 is the void fraction of the storage layer [-],

d2 is the water depth in the storage layer [mm], and K3 is the saturated hydraulic

conductivity of the native soil [mm/min].

Surface runoff is computed in SWMM for individual rainfall events, considering

land-use types and topography, accounting for antecedent moisture conditions,

infiltration losses in pervious areas, surface detention, overland flow, channel/pipe flow,

and constituents carried by runoff into inlets. Important input parameters are then the

catchment slope, pervious and impervious depression storage, channel and conduit

layout, geometry and properties, the Manning’s roughness coefficients for both

overland and channel flow, and rainfall intensity, among others.

The RUNOFF block can also simulate the quality of the runoff process within a

drainage basin and the routing of flows and contaminants along storm drain lines,

leading to calculating several inlet hydrographs and pollutographs. According to Huber

and Dickinson 1988 [49], there are several options for computing accumulation and

wash-off of pollutants in SWMM. The following a power-build-up equation was used

in this study:

Lt = 𝑄𝐹𝐴𝐶𝑇(3). 𝐷𝐷𝐹𝐴𝐶𝑇(2) ………..…………………………………………………………………………….(5)

92

where Lt is the pollutant concentration at each rainfall event t(mg/l); DD is the preceding

dry weather period (days), QFACT(2) is the power or exponent for the buildup

parameter (dimensionless); and QFACT(3) is the coefficient for the buildup parameter

(mg/l.day-QFACT(2)). Wash-off is a method of erosion and transportation of pollutants

from a catchment surface during runoff [53]. The following relationship describes the

relationship between wash-off (Mt) at each time step and the runoff rate Q.

Mt = RCOEF. Qt WASHPO …………………………………………………………………………………………….(6)

Where WASHPO is the exponent of the wash-off parameter (dimensionless); and

RCOEF (conc/event [mg/l/day]-WASHPO ) is the coefficient for the wash-off parameter.

Equations (5) and (6) are commonly used equations in SWMM-LID quality analysis.

Other equations also exist those are most complex. Based on Hubor and Dickinson

(1988) [45]. Equations (5) and (6) are the easiest to use when total runoff volumes and

pollutant concentration are accessible. For the selected study area three land types are

dominant and three pollutants (Nitrate-N; Total Suspended Sediments, TSS; and

Orthophosphate-P) are considered for developed model. The following Wash-off

parameters of pollutants (Table 3) are considered for this study.

Table 3: Buildup and Wash off and parameters of pollutants on different land-use types

Area Parameter Nitrate-N Orthophosphate-

P

TSS

Commercial area

(includes parking

lots, impervious

areas)

Accumulation rate coefficient

(1/event)

wash off coefficient

wash off exponent

0.23

0.004

1.8

0.15

0.004

1.7

0.3

0.004

1.3

93

Agricultural/green

area

Accumulation rate coefficient

(1/event)


wash off exponent

0.3

0.002

1.4

0.18

0.001

1.2

0.34

0.003

1.4

Roads Accumulation rate coefficient

(1/event)


wash off exponent

0.23

0.004

1.8

0.15

0.004

1.7

0.3

0.004

1.3

Figure 2: Flowchart of applied SWMM-LID framework

2.3 Data collection, source, and preparation

2.3.1 GIS data

GIS application is used in this study for the primary data preparation of the

SWMM-LID model. Watershed delineation, stream order generation, sub-catchment

characteristics (width, slope, %impervious, and % pervious) are the primary input

parameters for SWMM 5.1. The calculated physical parameter of the sub-catchments

was validated with USGS Streamstats database for the study area. The USDA National

94

Resources Conservation Service Soil Survey Geographic Database (SSURGO) was

used to determine the soils [54]. It contains the most detailed county-level data (Soil

Survey Staff, 2015), which is likely to yield better model results than the other available

soil database [55,56] and has been used extensively in hydrologic modeling studies [57

–60]. The developed GIS shapefile and its attributes were exported into inout file (.inp)

through inpPINs application. Finally, the .inp file was imported into the SWMM for

further analysis. Data sources are representation in Table 4.

Table 4: Data source of the required parameter of SWMM-LID

NO. Parameter Meaning Data Range Data Source

1 Manning-N Manning Coefficient 0.005 – 0.04 SWMM manual

2 N-Imperv Mannings N of impervious area 0.005 – 0.04 RIDSM report,

SWMM manual

3 N-Perve Mannings N of pervious area 0.1 – 0.8 RIDSM report,

SWMM manual

4 S-Imperv Depression storage on impervi-

ous area

0.2 ~ 2 (mm) RIDSM report,

SWMM manual

5 S-Perv Depression storage on pervious

area

2 ~ 10 (mm) RIDSM report,

SWMM manual

6 Pct-Zero Percent of impervious area with

no depression storage

50 ~ 80% RIDSM report, Cross

check with GIS

7 MaxRate Maximum rate on infiltration

curve

3 ~ 50 (mm/h) SWMM manual

8 MinRate Minimum rate on infiltration

curve

1~3 (mm/h) SWMM manual

95

9 Imperv (%) Percent of impervious area 10~90 (%) Google Earth, cross

check with GIS

10 Width Width of overland flow path Depends on the

area of sub-

catchment

GIS

11 Slope Average surface slope 3-5 (%) GIS

2.3.2 Sample collection and analyses

Total forty-two individual rainfall events were monitored for the study duration

(2016-2019). Due to the low variation of elevation of the study sites, flow could not be

effectively monitored. For the calibration of the streamflow, we used the USGS gauge

station (USGS 01117350). Water quality is a sample based on the rainfall amount,

which is greater than 12.7 mm or 0.5 inches. Grab samples (1 Lt each) were collected

from six individual sites for 42 rainfall events (Total 6*42) for the entire duration of the

research. Samples were analyzed for the following parameters and used to characterize

concentration:

- Total Suspended Solid (TSS)

- Nitrate-N (NO3-N+NO2-N)

- Orthophosphate-P (PO4-P)

- Turbidity

Stormwater runoff samples were also analyzed for Polycyclic Aromatic

Hydrocarbon (PAH) using Shimadzu GCMS following EPA 8270d method. However,

the analyses showed that all the samples had very low to negligible PAHs (the below

detection limit (BDL) concentration (<0.5 mg/l). The study area is a suburban area, and

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gradually the suburban growth is expanding [33]. Therefore, for this developed

SWMM-LID model, we only considered TSS, -Nitrate-N, and Orthophosphate-P as

main water quality pollutants for this study.

2.4 Scenarios of the developed model

2.1.4 LID Selection and Developed Scenarios

SWMM 5.1 allows to incorporate modules for LID components including

bioretention (BR), permeable pavement (PP), and vegetative swales (VS). In this study,

these three LID components are evaluated, and. the characteristics of the three selected

LID controls are represented in Table 4. The comprehensive effectiveness of different

LID structures on stormwater pollutant was measured for BR, PP, and VS. The

developed SWMM-LID model itself represented the vegetative swale (base scenario),

two more scenarios (bioretention, and permeable pavement) were developed in this

study. Scenarios were developed, considering storm design events (1-yr, 2-yr, 5-yr, 10-

yr, 50-yr, and 100-yr) and the efficiencies were measured The LID module of SWMM

(Table 5), BR, PP, and VS are represented for three layers (surface, pavement, and

storage layers), two (surface and storage), and one (surface) vertical layers respectively.

The PP and BR LIDS have an optional layer, i.e., underdrain system.

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Table 5: Parameters of three LID controls used in the LID-SWMM Simulations

LID Controls System Components Parameter Value

Permeable Pavement Surface Layer Storage depth

Vegetative cover fraction

Surface roughness

Surface slope (%)

2

0

0.12

<9%

Pavement Layer Thickness (cm)

Void ratio

Impervious surface fraction

Permeability

18-25

0.65

0.038

2.3

Storage Layer Height (cm)

Void ratio

Filtration rate (2cm/hr)

50.8

0.65

2

Underdrain system Drain coefficient

Drain exponent

Drain offset height

3.8

0.5

3.1

Native soil Suction head

Conductivity

Initial deficit

n/a

2

n/a

Other Area (sq.km)

Width (km)

E.T. rate (simulation start from No-

vember)

0.04

0.02

0.71

Bioretention Cell Surface Berm height (cm)

Vegetative Volume Fraction

Surface Roughness (Manning’s n)

Surface Slope (%)

90

0.12

8

Soil Thickness (cm)

Porosity

Field Capacity

31

0.15

35

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Wilting point

Conductivity (cm/hr)

Conductivity slope

Suction head (cm)

0.2

3.6

6

20.32

Storage Thickness (cm)

Void ratio

25

67

Drain Flow coefficient

Flow exponent

Offset height (cm)

4

0.5

10.1

Vegetative Surface Layer Berm Height (cm)

Vegetative Volume Fraction

Surface Roughness

Surface Slope

Swale side slope

18

100

0.24

7% - 10%

10%

The model simulation results were calibrated (first 30 rainfall events) and

validated (rest of the 12 rainfall events) against the measured data. Measured TSS,

Orthophosphate-P, and Nitrate-N concentrations for the current study were derived from

42 event-scale from six different sites. Other onsite water quality data for different land

cover types were not available

2.5. Model performance evaluation

2.5.1. Sensitivity analysis

Sensitivity analysis was performed to identify which parameters would be most

effective in minimizing differences between observed and predicted results [61].

Parameters were adjusted over a range of 50% of their original value while keeping all

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other parameters unchanged, and the corresponding difference in runoff volume and

peak flow was calculated. Relative sensitivity was computed using equation (7):

Sensitivity = (𝜕𝑅

𝜕𝑃)(

𝑃

𝑅) ……………………………………………………………………………………………...(7)

where 𝛛R is the difference between the model input parameter and simulated parameter

value, 𝛛P is the difference between input and adjusted parameter value, R is the

simulated model output, and P is the input value of the parameter of interest [62].

2.5.2 Calibration and Verification of SWMM Models

SWMM 5.1 was developed with forty-two individual rainfall and calibrated with

entire sets of data from 2016 and 2019. Two individual calibration runs were obtained

with the daily discharge data from USGS 01117350 Chipuxet river at West Kingston,

RI. Calibration was conducted for two different model set up (Run 1 and Run 2) in two

different sub-catchments (sub-catchment 3 and 6) having two different types of land

types (agricultural and parking lot) and where the monitoring sites are located. For water

quality parameters calibration, we considered TSS, Nitrate-N, Orthophosphate-P for site

1 (site_1 is in the sub-catchment 3) and site 5 (Site 5 is in the sub-catchment 6). Among

42 individual events, 30 rainfall events were considered for the model calibration and

the rest of 12 rainfall events were applied for model validation. The calibration of

stormwater quality models usually is more complicated than quantity models due to the

build-up and wash-off methods process, sewer sediment transportation, pollutant

interactions, especially in the big urban watershed area [63, 64]. Also, the stormwater

quality is challenging to calibrate without sufficient on-site data [63, 64]. Due to

geographical characteristics (low relief and almost no variation in slope) of the study

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area, the flow meter couldn’t collect enough samples. Therefore, manual depth

measurements were done for each storm. The Kingston Campus Master Plan, 2018 [65]

and Stormwater management plan 2020 [66] for the South Kingstown was used to

retrieve the required supplementary data i.e., detail drainage design, land-use and land

cover, location basis detail soil profile and texture, detail bioretention, permeable

pavement, and vegetative swale design, and location-based soil information. These data

were used for model input parameterization. The adjusted calibration parameter is

presented in Table 7.

The model performance on runoff peak and runoff quality simulation were tested

using various performance statistics. The optimal parameter values for the study area

were selected according to the Nash-Sutcliffe Efficiency (NSE) [67].

NSE = 1 − ∑ (Oi−𝑃𝑖)2n

i=1

∑ (𝑂i−O′)2ni=1

……………………………..………………………………(8)

The coefficient of determination, R2, an established measure in statistics quantifying the

model’s fit, is described as:

R2 = [ ∑ (𝑂𝑖

ni=1 −𝑂′)(𝑃𝑖−𝑃′)

√∑ (𝑂𝑖−𝑂′)2√∑ (𝑃𝑖−𝑃′)2ni=1

𝑛𝑖=1

] 0 ≤ R2 ≤ 1 ……………………………………..(9)

where n is the number of observations in the period under consideration, Oi is the i-th

observed value, 𝑂′ is the mean observed value, 𝑃𝑖 is the i-th model-predicted value, and

𝑃′ is the mean model-predicted value. NSE ranges between −∞ and 1, where 1 is

considered a perfect fit for any model where NSE is sensitive to extremes [68].

Dongquan et al. 2009 [69] suggest that an NSE value of 0.5 or greater is acceptable for

SWMM simulation. The denominator is the total variance in the observed data, and the

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numerator is the variance between the simulated and observed data. The closer R2 is to

1, the larger proportions of the observed variability can be explained with the

simulations of estimated values [70].

To evaluate the developed model for the goodness of fit test root mean square

error (RMSE), the RMSE-observations standard deviation ratio (RSR) is calculated as

the RMSE and standard deviation ratio of measured data. The RMSE and RSR are

explained as follows

RMSE = √1

𝑛 ∑ (𝑃𝑖 − 𝑂𝑖)2𝑛

𝑖=1 ……………………………………….…………….. (10)

RSR = 𝑅𝑀𝑆𝐸

𝑆𝑇𝐷𝐸𝑉𝑜𝑏𝑠 …………………………………………………………………… (11)

where n is the number of observations in the period under consideration, 𝑃𝑖is the i-th

model-predicted value and 𝑂𝑖 is the i-th observed value, 𝑆𝑇𝐷𝐸𝑉𝑜𝑏𝑠 is the standard

deviation of the observed value. The ideal value for RSR is 0; however, it cannot be

expected to occur; otherwise, it would be a perfect model. So, values between 0 and 1

are acceptable for RSR when field-specific data are available for calibration. RSR varies

from the optimal value of 0 to a large positive value. The lower RSR, the lower the

RMSE, and the better the model simulation performance [71].


3.1 Model Performance Evaluation

Model performance evaluation is divided into two sections: (i) sensitivity

analysis and (ii) model calibration and validation.

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3.1.1 Parameters sensitivity analysis

The sensitivity analysis showed that seven parameters namely rainfall, sub-

catchment area, percent imperviousness, percent slope, conduit roughness, conduit

length, and storage depth were highly sensitive to peak runoff and water quality

predictions. These seven parameters were used for the calibration of the SWMM-LID

model.

a) Sensitivity Analysis for Hydrological Parameters

Figure 3(a) presents the sensitivity analysis for the simulation of the hydrology

module in LID. Rainfall response showed a strong positive relation with the amount of

runoff generated. Rainfall always plays a dominant role in soil loss and sediment

delivery [72], affecting runoff. The analysis showed the relatively weak or low but

positive response of percent imperviousness on the model outcome. The infiltration

capacity decreased with the increase of the impervious surfaces. Figure 3a also indicates

that, as the rainfall receiving area increases, the output runoff amount from sub-

catchment increases almost proportionally. Drainage and rainfall amounts are the most

important variables to predict event loads associated with runoff volume and is reported

by another research [73, 74]. SWMM model output is also sensitive to the percent slope,

but the geographical characteristics of the study area have relatively low or no variation

of slope and elevation. According to El-Hassanin et al., (1993) [74] runoff-rainfall ratios

are high under the steep slopes and soil loss per unit of rainfall and per unit of runoff

increase as the slope gradient increase. This study shows similar model results. The

model showed high sensitivity to the change in slope showing increase in the runoff

with increase in slope; however, percent imperviousness sensitivity increased was

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relatively lower. Since these parameters are related to the surface layer (Eq. 1), it can be

inferred that surface layer parameter are more sensitive than those related to the soil

layer (Eq. 2).

The peak runoff is directly proportional to the changes in imperviousness,

whereas it is inversely proportional to the D-Store impervious-pervious indicating that

the peak runoff will decrease with the reduction of percent imperviousness, and

depression storage. It should also be noted that in most cases the peak runoff is more

sensitive to the reduction of the D-Store impervious-pervious than the increase. In other

words, the peak runoff will only decrease 5% with the 30% increase in the D-Store

impervious-pervious, whereas it will increase 12% with the 30% reduction of D-Store

impervious-pervious. But in this study, the sensitivity analysis showed very minor

response (< 5% change) as the depression storage is less sensitive to the low slope areas.

Therefore, D-store impervious-pervious parameter was excluded from the model input

parameterization (Figure 3a).

(b) Sensitivity of Hydraulic Parameters

Figure 3(b) shows sensitivity of conduit roughness, conduit length, and the

storage depth to the runoff responses. The conduit roughness parameter of SWMM

stands for the Manning’s coefficient for the natural channel. Both conduit roughness

and conduit length respond inversely to total inflow volume. However, the peak runoff

showed a positive linear relationship with storage depth of the impervious. The positive

104

or negative responses of storage depth are largely dependent on the sub-catchment

characteristics.

Figure 3: a) Model runoff results of sub-catchments for hydrological parameters; b)

Model runoff results of the outfall of the catchment concerning hydraulic parameters.

[In SWMM, time patterns allow external dry weather flow to vary periodically. They

comprise of a set of adjustment factors applied as multiplier (SWMM 5.1 manual]

(c) Sensitivity of Buildup and Wash-off Parameters

Buildup and wash-off parameters are considered to complete the sensitivity

analysis for runoff pollutants (TSS, Nitrate-N, and Orthophosphate-P). The considered

buildup and wash-off parameters are accumulation rate coefficient (k), pollutant wash-

off coefficient (E1), and Pollutant wash-off exponent, E2. The initial value of the

parameter is mentioned in the Table 3. This analysis showed the %change in simulation

concentration due to the % change of buildup and wash-off parameters. Here, we range

of the parameter change is ±20%. The sensitivity analysis was performed by changing

each parameter while keeping other parameters constant. As shown in the Figure 4, it is

105

clearly observed that the pollutant wash-off exponent (E2) is the most sensitive

parameter for all three pollutants than other two considered sensitive parameters (k and

E1).

Figure 4: Buildup and Wash-off parameter sensitivity analysis a) TSS; b) Nitrate-N; and

(c) Orthophosphate-P

The results from a sensitivity analysis by Huang [75] and Wang [76] and the

sensitivity ranking of the hydraulic and hydrological parameters in SWMM are listed in

Table 6. The summary and the ranking of the considered seven sensitive parameters

presented in the Table 6 also showed the response the sensitive parameters with runoff.

106

Table 6: Model sensitive Parameter and their ranking based on the sensitivity analysis

Parameter Sensitivity Class Correlation Sensitivity Rank

Rainfall Very high Proportional 1

Sub-catchment Area Very high Proportional 2

Conduit Roughness High Inverse 4

Conduit Length Moderate Inverse 5

Storage Depth Moderate Proportional 5

% Impervious Low Proportional 6

% Slope Moderate Proportional 6

Pollutant wash-off coefficient Moderate Proportional 5

Pollutant wash-off exponent High Proportional 3

Accumulation rate coefficient Moderate Inverse 4

3.1.2 Model Calibration and Validation

SWMM-LID is developed with forty-two individual rainfall events. Thirty

individual events were considered for the model calibration, and the remaining twelve

rainfall events were used for the model validation. The Figure 5 (a, b) showed the

observed and simulated flow rates, and the adjusted values are represented in Table 7.

To calibrate flow, soil conductivity was lowered (Table 7) and higher percent

imperviousness was applied. The simulation run began in November and initial soil

moisture was considered nonzero, i.e., the model default of 0.001. According to the

SWMM Manual, soil moisture deficit value is a required parameter of flow simulation

using the Green Ampt Method. For the wet month, the soil moisture deficit value is

0.001. But if the simulation start period is a dry month, the model requires soil moisture

deficit to be calculated based on the rainfall, evapotranspiration, and soil types using an

107

in-built equation. Considering the simulation start period, the suction head was lowered

for the LID sub-catchment for better model performance. Higher soil suction would be

associated with lower soil moisture content [77]. To improve the relationship between

simulated runoff with observed runoff, Manning’s n for the LID control was lowered

from its initial value.

Table 7: Adjusted SWMM calibration surface layer and soil parameters

Parameter Adjustment Initial Value Calibrated value

(Run 1)

Calibrated value

(Run 2)

% Impervious x 1.0 X 0.23 X 0.25

Depression Storage (mm) 2.032 1.524 2.2

Sub-catchment Width 1 1 1

Impervious "n" 0.02 0.025 0.03

Pipe "n" 0.013 0.011 0.012

Soil Conductivity (mm/h) 13.97 5.7 4.9

Manning’s n for swale 0.24 0.15 0.14

Suction head (cm) 20.32 18.2 17.5

DStore-Perv 0.25 0.22 0.2

DStore-Imperv 0.02 0.017 0.018

The calibrated output showed that the model overestimates the flow for a few

events in both runs. Nevertheless, it is obvious from Figure 4 that, overall, the predicted

and observed hydrographs showed a good match according to the NSE, R2, and RSR

value (Table 8) for both runs [69-71].

108

Table 8: Model Calibration and validation output

Model

Evaluation

Calibration Validation TSS Calibration Nitrate-N Cali-

bration

Orthophosphate-

P Calibration

Run

1

Run

2

Run

1

Run 2 Site_1 Site_5 Site_1 Site_5 Site_1 Site_5

NSE 0.73 0.69 0.77 0.70 0.73 0.77 0.78 0.68 0.71 0.68

R2 0.75 0.64 0.84 0.7991 0.87 0.82 0.93 0.92 0.90 0.88

RSR 0.53 0.61 0.42 0.51 0.58 0.52 0.41 0.55 0.62 0.57

Figure 5: Calibration and Validation of the developed SWMM (a) Run 1 in Sub-

Catchment 3 and (b) Run 2 in Sub-catchment 6

Figures 6, 7 and 8 represent the calibration for TSS, Nitrate-N and

Orthophosphate-P. The output of the calibration is summarized in Table 8.

109

Figure 6: The observed and predicted TSS concentration (a) Site 1 (Agricultural Land)

(b) Site 5 (Parking Lot)

110

Figure 7: The observed and predicted Nitrate-N concentration (a) Site 1 (Agricultural

Land) (b) Site 5 (Parking Lot)

Figure 8: The observed and predicted Orthophosphate-P concentration (a) Site 1

(Agricultural Land) (b) Site 5 (Parking Lot).

111

3.2 LID implementation on stormwater quality

Once the model calibration and validations were completed for the study sites, the four

LID scenarios were tested using the model. Then, the model was run for the Nitrate-

NOrthophosphate-P1-yr, 2-yr, 5-yr, 10-yr, 50-yr, and 100-yr rainfall return periods. The

results of the model with the applied LIDs are shown in figures 9, 10, and 11.

According to the Kingston Campus Drainage Master Plan, 2018” [60] which is

prepared for the university of Rhode Island, the structural controls are generally required

to achieve 85% removal of total suspended solids (TSS), 30% removal of

Orthophosphate-P for discharges to freshwater systems, and 30% removal of Nitrate-N

for discharges. Based on this statement, the target was fixed for the pollutant

concentration reduction for each of the selected structural controls.

3.2.1 Comparison of stormwater management scenarios

The simulated stormwater management scenarios were compared with each other and

with the original model without and with LIDs. The LID structures retain stormwater at

the source and reduce the considered pollutant concentration. The total runoff and

pollutant concentration were calculated for each scenario and found that all applied LID

controls can reduce the pollutant concentration. The scenarios reduced the different

pollutants to varying extent, depending on the specific LID location and pollutant. The

target level was fixed for the three different pollutants.

(a) Bioretention

These results (Figure 9) indicated that in a suburban environment condition, bi-

oretention systems can reduce concentrations of all target pollutants (TSS, Nitrate-N,

and Orthophosphate-P). Additionally, bioretention can effectively reduce peak flow and

112

runoff volume from six different rainfall return period (1 yr, 2-yr, 5-yr, 10-yr, 50-yr,

and 100-yr) that was examined and described in the third chapter. The Figure 8 shows

the reduction of TSS, Nitrate-N, and Orthophosphate-P after implementing bioretention

controls. The reduction rates were measured for all six different rainfall return periods

and sites. The analysis showed that the TSS reduction rate met the satisfactory level for

1-yr (64% – 81%), 2-yr (60% - 75%), and 5-yr (52% – 68%) rainfall return period.

However, after that, the reduction level dropped (63% – 72%) compared to 1yr return

period.

113

Figure 9: Performance of Bioretention for six different sites (a) TSS, (b) Nitrate-N, and

(c) Orthophosphate-P

On the other hand, Nitrate-N bioretention showed better performance until 10 yr

(10% - 15%) rainfall return period following the target level 30%. The analysis showed

that the NITRATE-N reduction rate met the satisfactory level for 1 yr (19% – 28%), 2

yr (18% - 23%), and 5 yr (12% – 20%) rainfall return period. For the 50 yrs. rainfall

return period, the reduction level dropped from 53% to 90% compared to 1yr return

period.

The model output showed the low performance in terms of pollutant reduction

over time. Based on the Stormwater plan report, all the structural controls need mainte-

nance within a specific time frame. For bioretention, it requires operational maintenance

annually for this study area [61].

(b) Permeable Pavement

As shown in the figure 10, the reductions of TSS, NITRATE-N, and Orthophosphate-P

after implementation of permeable pavement were consistent up to 5-yr rainfall return

design. Total suspended sediments were significantly reduced by the permeable

pavement system for 1-yr (65% – 80%), 2-yr (61% - 70%), and 5-yr (53% – 67%)

114

rainfall return period. For the return period of 50-yrs and 100-yrs rainfall design the

reduction rate was 40% and 25% respectively. The accumulation of suspended

sediments in this system can lead to clogging [32], which can make the reduction rate

lower over the time.

Figure 10: Performance of Permeable Pavement for six different sites (a) TSS, (b)

Nitrate-N, and (c) Orthophosphate-P

115

Nitrate-N is highly mobile in soils and groundwater, and it is difficult to remove

from the system. The nitrate-N concentrations usually increase through PP when they

are normally drained for example, underdrain at the bottom of the cross-section, while

aerobic environments are dominant. In this study, the PP scenario showed higher

reduction rate of 22%n for the 5-yrs rainfall return period. However, the performance

rate declined for 10-yr, 50-yr, and 100-yr rainfall designs (average reduction rate <8%).

For the Orthophosphate-P, PP showed a better performance compared to the

nitrate-N having an average removal efficiency 22% for the 1-yr, 16% for 2-yr, 22% for

5-yr rainfall design from all the sites. However, the removal efficiency did not improve

for the higher intensity rainfall (for 50-yr and 100-yr rainfall design it showed the about

3 – 5% Orthophosphate-P removal efficiency).

(c) Vegetative Swale (VS)

In general, according to literature (Barret et al., 1993, and GKY and Associates

Inc., 1991), a well-designed, well-maintained swale system can remove up to 70% of

total suspended solids, 30% of total phosphorus, and 25% of Nitrate-N. In this study

similar output for thesuburban sites was found (Figure 11). Vegetative swales were

found to be very effective in reducing TSS concentration with the average upto 70% for

1-yr, 2-yr, and 5-yr rainfall design indicating that VS is capable to reduce TSS for 120

mm of rainfall depth. For 10-yr design rainfall it’s average removal rate was 47%.

116

Figure 11: Performance of vegetative swale for TSS, Nitrate-N, and Orthophosphate-P

removal

The analysis showed a consistent removal efficiency of VS for Nitrate-N for 2-

yr and 5-yr rainfall design whereas it performed maximum for 1-yr rainfall design for

all the sites. Effectivity of removing Nitrate-N for high intensity and longer period of

rainfall is lower (<4%) than any other LID controls.

117

For the Orthophosphate-P, VS showed not satisfactory efficiency of NITRATE-

N. Study showed an average removal efficiency 20% for the 1-yr, 13.5% for 2-yr, 8.3%

for 5-yr rainfall design from all the sites. However, the removal efficiency is not

significant when it gets higher intensity of rainfall, for 50-yr (<3%) and 100-yr (0.5%)

rainfall design.

4. Conclusion

Implementing BMPs and LID practices can reduce the adverse impacts of urban

growth on hydrology and water quality. However, this study emphasized modeling

stormwater quality impacts of BMPs and LIDs practices in suburban areas where

surface runoff has started to get disturbed through suburban growth. This Study

conducted to assess LID performance on runoff pollutant control reported that LID

performs significantly differently in different rainfall return with different intensities

US EPA SWMM 5.1.010 is applied to develop the model, and the 18 km2 suburban

catchment is divided into 12 sub-catchments. The developed model was calibrated and

validated both for quantity and quality using real-time data. A detailed sensitivity

analysis was also performed beforehand to find out the most sensitive parameter of the

developed model. The model was to simulate LID techniques at the sub-catchment scale

by applying bioretention, permeable pavement, and vegetative swale as an efficient

structural control for the study area. Each of the LID controls' performance was

measured for six different rainfall design that was derived from depth duration

frequency (DDF) curves estimated by NOAA for the study area.

118

The following assumptions could be made based on the model performance and the

results attained

• The sensitive parameters of the model are precipitation, sub-catchment area, %

imperviousness, slope, conduit roughness, conduit length, and D-Store impervious-per-

vious. A small change in the area, the depression storage will significantly change the

simulated runoff and the peak flow. %imperviousness, slope showed slight impact on

the runoff. However, the model is more sensitive to precipitation, area, and D-Store

impervious-pervious rather than the other four parameters. Also, the peak runoff would

be more affected when D-Store impervious-pervious decreased rather than increased. It

means that storage is inversely related to runoff.

• A detail calibration and validation have done for hydraulic (Run 1 and Run 2)

and water quality (TSS and Nitrate-N) module. A well performed calibration output

were derived having 0.73 and 0.69 NSE value and 0.61 and 0.53 RSR value the calibra-

tion. NSE value of greater than 0.5 and lower RSR value are acceptable for SWMM.

Validation output is more significant as it has the RSR <0.6 (Run 1 is 0.42 and Run 2 is

0.51). on the hand, TSS and Nitrate-N calibration also showed significance perfor-

mance. TSS and Nitrate-N calibration was done for two different monitoring sites

(Site_1 and Site_5 from two different land-use). Nitrate-N calibration performed very

well in terms of NSE (0.78), R2 (0.93) and RSR (0.41) value especially for the Site_1.

• In terms of water quality, performances are evaluated for bioretention, permea-

ble pavement, and vegetative swale in terms of TSS, Nitrate-N, and Orthophosphate-P

removal from the source. The LID removal efficiency reached up to 26% for Nitrate-N

and up to 81% for TSS and up to 27% for Orthophosphate-P for 1-yr rainfall design.

119

The LID removal efficiency for three different controls of the model was independent

of the six different (1-yr, 2-yr, 5-yr, 10-yr, 50-yr, and 100-yr) rainfall intensity and du-

ration. Overall, the developed model performed acceptably for rainfall of up to 120 mm

(10 years rainfall design). Nonetheless, for the return period of more than 10-yrs and for

the rainfall amount of more than 120 mm, the assigned LIDs for the sub-catchment

cannot manage properly.

Finally, lower reduction efficiency showed for the return period for 50-yr and

100-yr for TSS, Nitrate-N, and Orthophosphate-P for all three designed LIDs. We need

more LID to be installed to handle the situation or minimize the impact in this suburban

area.

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3. Kayhanian, M., Fruchtman, B.D., Gulliver, J.S., Montanaro, C., Ranieri, E., Wuertz, S.

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CHAPTER 4

Manuscript IV

Predictive Uncertainty Estimation of Stormwater Management Model using

Bayesian Markov Chain Monte Carlo approach

prepared for Hydrological Processes Journal, July 2021

Khurshid Jahan1,*, Soni M Pradhanang1,*, and Russell D. Briggs2

1Department of Geosciences, University of Rhode Island, RI, Kingston, USA 2Department of Sustainable Resources Management, ESF SUNY College of Environ-

mental Science and Forestry, NY, USA


(S.M.P)



127

Abstract

The reliability of any hydrological model output depends on the uncertainty analysis of

both input and output. Uncertainty might arise mainly due to errors in data measurement,

data collection, assumptions, and limitations placed. This study analyzes the uncertainty

of Storm Water Management Model (SWMM) data input and output using the Bayesian

Markov Chain Monte Carlo (MCMC) approach due to its flexibile model fitting

capbility with data sets of different complexity and limitation. As the variation of inputs

contributes to output variation differently, the study adopts fully bayesian sensitive

analysis wherein the response variability is examined within the entire input space to

consider the interaction among the inputs. Thus, the quantification of uncertainty

facilitates the generation of more consistent or reliable flow predictions. The study

reveals that rainfall depth, slope, and manning coefficients are the important sensitive

parameters for the Chipuxet River watershed area, Rhode Island. The outcome showed

the Interaction between the model sensitive parameters affects peak flow. Moreover,

Nash-Sutcliffe Efficiency (NSE) values of 0.73, and 0.69, R2 values of 0.75, and 0.64,

and RSR value of 0.53 and 0.61 for runoff quantity module in two different runs prove

model efficiency. For runoff quality module in SWMM, total suspended sediments,

nitrate and nitrite, and ortho phosphate calibrated and measured NSE, R2, and RSR value

which also showed the significant performance. Posterior distribution of the model

parameter reveals that average observed values is within ±20% range from the model

values and most of the observed data are within the 95% uncertainty band.

Keyword: MCMC, SWMM, Bayesian, Uncertainty, Sensitivity, Rhode Island

128

1. Introduction

SWMM (Storm Water Management Model) is a popular and widely used

deterministic rainfall-runoff model for simulating water outflows, inflows, and storages

within a sub-catchment or watershed during a single event or continuous simulations

[1]. SWMM is designed primarily for urban areas [2]. SWMM is capable of estimating

a number of commonly measured runoff pollutants along with runoff quantity. During

SWMM modeling, different parameters are fed into the system. Parameters values can

be default values (established by the manual), measured values (taken as primary data),

estimated values (from the previous literature), and values obtained from the trial-and-

error method. As the watershed areas vary in terms of land-use, soil properties, slope,

etc., multiple interactions occur between features of the drainage system or among

different hydrological processes creating a complex modeling system [3].

Moreover, complexity might come from runoff depth, peak flow, lag time,

overflow volume, duration. Hence a unified theory for all kind of watershed and

catchment is difficult to establish at different temporal and spatial scales. As a result,

uncertainty analysis of hydrological model input and output is gaining attention in urban

environments [4, 5]. The many inputs that are used to predict both water quantity

(%imperviousness, depression storage for impervious and pervious, sub-catchment

width, slope, area, Manning’s coefficient, rainfall intensity) and quality (soil

conductivity rate, conduit roughness, conduit length, storage depth) have different levels

of uncertainty [6]. In some cases, especially in the highly urbanized watershed,

uncertainty is higher in water quality prediction [6, 7]. For example, SWMM had an

average prediction error for various pollutant loads of less than 20% [8]. To address

129

these issues numerous studies [9, 10, 11, 12, 13] adopted different uncertainty

techniques like the Bayesian estimation Monte Carlo approach on the Storm Water

Management Model.

Uncertainty of a model implies quantifying the uncertainty in the model output

due to uncertainty in the model input parameters. For example, uncertainty arises in a

hydrological model due to different variability and limitations in its input data, structural

parameter, and calibration. Natural conditions, measurement limitations, data scarcity-

all of those contribute to input data uncertainty [14]. Hence, instead of using a

deterministic model with the fixed input parameters (as in Figure 1a), an uncertainty

model is constructed using the distribution of possible values for each of the parameters

(as in Figure 1b) and outputs a distribution of possible values.

Inability or error in determining effective parameter along with natural

variability, observational limitation & errors in calibration data can lead to parameter

uncertainty [16, 17]. Hence model structures determine the model performance [18]. All

models are mostly approximations to the real-life situation due to the theoretical

constraints, inadequate knowledge, numerical and process simplifications, and lack of

a unifying theory, even with known input parameters [19]. Furthermore, this leads to

structural uncertainty i.e., predictive uncertainty of the output. In some cases, it can

contribute most to the predictive uncertainty [20, 21].

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Figure 1: (a)- Traditional Model, (b)- Uncertainty Model (adapted and modified from

[15])

Structural uncertainty cannot be wiped out, regardless of error-free input data,

since responses found in the catchment are averaged over space and time [22]; however,

it can be diminished if the predominant processes are sufficiently addressed [23];

Most of the time, calibration of model parameters and evaluation of prediction

take place using observed parameters like streamflow, discharge which might introduce

uncertainty due to measurement error, limitation, natural conditions, or seasonal

variation [24, 25].

All of the above uncertainties mentioned earlier take part in the predictive

uncertainty of model output. Predictive uncertainty is usually heteroscedastic, the

magnitude of uncertainty varying with the extent of the model output (non-Gaussian

residuals). Comparison of predicted data to observed data is necessary to perform a post-

(a)

(b)

131

audit validation of a model [26]. Observed data is needed in order to demonstrate

whether SWMM 5.1 LID controls can predict runoff.

Generalized Likelihood Uncertainty Estimation (GLUE) [27] and the classical

Bayesian Monte Carlo (i.e., Bayesian inference approach) [28] are widely used

uncertainty analysis approaches. As Bayesian methods require a large amount of data,

it is difficult to implement while there is data scarcity. Hence the GLUE method is easier

to implement than the Bayesian method, though it is questioned for its statistical

incoherence and inconsistency [29, 30, 31]. Because of these limitations, Monte Carlo

simulations are proposed for uncertainty quantification by estimating the probability

density function (PDF) of the model parameters and predictions. [32, 33, 34, 35]

Markov-chain Monte Carlo (MCMC) are extended version providing computational

efficiency. In MCMC, probability of next possible events depends on the state of the

existing events, whereas in MC, probability of next possible events is calculated based

on different sets of past events within a range of probability distribution.

Moreover, as some model parameters might not influence individual model

output, the consideration of all parameters in model simulation significantly increases

model convergence time. Therefore, it is essential to apply a parameter selection

methodology for fixing an appropriate set of parameters applicable for the particular

study.

In order to identify the parameters that influence model output, it is necessary to

carry out a sensitivity analysis (SA) during uncertainty quantification. In general, there

are two approaches for SA: (a) local sensitivity analysis where a single parameter is

changed one at a time and the corresponding output is analyzed, and (b) global

132

sensitivity analysis where responses are evaluated considering simultaneous changes of

all the variables within their respective input spaces. Parameters are ranked in order of

sensitivity and denote the effective ones for reducing variance between simulated results

and observed data [36]. Sometimes model sensitivity can be perceived from previous

studies and documentation for similar watersheds, but it might be necessary for a

different kind of watershed to make sensitivity analysis [37]. An overview of the SA

can be found from Saltelli et al., 2000 [38]. Furthermore, when a model is involved with

many inputs, SA is an effective tool for optimizing input space [39].

Numerous studies have cited that runoff volume is most sensitive to the percent

impervious area of a sub-catchment [6, 8, 40]. In addition, some studies concluded that

runoff volume was most sensitive to the impervious depression storage (Dstore-Imperv)

and Manning’s coefficient for impervious areas (N-Imperv) while the shape of the

hydrograph was sensitive to sub-catchment width [41]. The uncertainty of a simplified

urban drainage model developed in previous studies has been evaluated in this context.

Different approaches can be used for uncertainty identification. A Bayesian approach

coupled with Monte Carlo analysis has been used [42] in this study. The Bayesian

approach expresses uncertainties in the model parameters in terms of probability.

Parameter uncertainty is quantified first by introducing a prior probability distribution,

representing historical or expert information before collecting new data.

UA Applications to suburban watersheds are minimal. However, this study is the

first to apply an MCMC scheme that works within a formal Bayesian framework for

UA for suburban watersheds using SWMM. The conducted Bayesian uncertainty

approach applied on the developed SWMM model for a suburban watershed area. Due

133

to suburban growth [43], recently occurred (2010, and 2013) flood events and their

impacts, and having high chloride during the winter season due to overused of road salt

made the authors curious for detail analysis for the suburban areas [44]. It is important

to mention that suburban areas also have started to be affected through the stormwater

runoff like urban areas. We focus here on addressing the sensitive or responsive

parameters of linking the MCMC method to the developed stormwater model. The

linking output will help to update the quantity model parameter distribution. The overall

objective of this research is mentioned below:

• to identify sensitive parameters for the study area using monitored rainfall

events.

• to identify SWMM model output (peak flow) response with the sensitive param-

eters

• to estimate model parameter uncertainty distribution

• to identify predictive uncertainty of SWMM model output (peak flow) using

Bayesian Markov Chain Monte Carlo (MCMC) Method.

2. Study Area, Data & Materials

This research has been carried out in the Chipuxet watershed located in South

Kingstown, Rhode Island. The selected study area is split into 12 different sub-

catchments (Figure 2). The catchment has one rainfall gauge station (NOAA Station ID

54796), one automated stream gauge flow (USGS 01117350) run by USGS, six

stormwater quality monitoring sites and monthly average evapotranspiration data.

Stormwater runoff quality was measured with the samples collected from 42 individual

134

storm events from six different sites. The samples were collected from 2016 to 2019.

The area is a low elevated region having a low to moderate slope. The range of the slope

is (7-10) %.

Stormwater quality monitoring sites are established near the parking lot, agricultural

practice area.

Figure 2: Location Map of the Study Area

The original elevation data, including Digital Topographic Map (DTM) 1:2000

and Remote Sensing Images (RSI) 1:2000, are the required inputs for the SWMM model.

Watershed delineation for the Chipuxet Watershed was done using ArcGIS 10.6.1 using

135

DEM file data. The study area relatively very low elevation area having average

elevation 45 meter.

3. Material & Methodology

3.1 SWMM Model

The study is modeled with SWMM version 5.1 [47]. SWMM is designed with

four different computational routine or building blocks (runoff, storage, transport and

extran). Furthermore, low impact developments (LIDs) modeling like porous

pavements, bioretention cells, infiltration trenches, vegetative swales, and rain barrels

are also possible through SWMM5.1. As the system can capture complex processes and

different responses to different inputs and configurations, SWMM is advised to use

hydrological modeling in urban areas [48]. In this study, runoff routine is used for runoff

quantity simulation by transporting through a drainage network consisting of pipes,

channels, storage/treatment devices, pumps, and regulators [48 - 50].

Runoff is simulated using a dynamic-wave equation. Depression storage is used

for considering water loss through infiltration. The land-use map of the study area

provides the percentage of impervious and pervious portions. The depth of depression

storage on impervious and pervious portions of the sub-catchment, Manning’s

coefficient for overland flow over the impervious and pervious portions of the sub-

catchment, the percent of impervious area without depression storage, and the

infiltration parameters of Green Ampt’s equation [51]- all these are parameters in runoff

block of SWMM were used to calibrate the model. The minimum and maximum values

were obtained from the SWMM user’s manual [47] and relevant literature [52,53]. The

136

model is appropriately calibrated to identify an optimal set of parameters that provide

minimum deviation between simulated and observed results. Topographical and

catchment parameters are formulated using GIS applications and transferred to SWMM.

Runoff volumes produced by the model greatly vary by the types of land-uses like

residential, commercial, agricultural, recreational etc.

3.2 Objective Function

Model parameter sensitivity and the uncertainty analysis was done prior to the

calibration of SWMM. The model was developed for both quantity and quality using 42

individual rainfall events. Stormwater quality data were collected from the six different

site (Figure 2) and calibrated with entire sets of data from 2016 and 2019. Two

individual calibrations run obtained using the daily discharge data from USGS

01117350 Chipuxet river at West Kingston, RI (Figure 2). Calibration was conducted

for two different model set ups (Run 1 and Run 2) in two sub-catchments (sub-

catchment 3 and 6) from two different land types (agricultural and parking lot) where

the monitoring sites are located. For water quality calibration, we considered TSS,

Nitrate-N, and orthophosphate-P for site 1 and site 5. 30 rainfall events out of 42 events

considered for the model calibration and the rest of 12 rainfall events used for model

validation. The adjusted validation parameter is presented in Table 1.

The optimal parameter values for the study area were selected according to the

Nash-Sutcliffe Efficiency (NSE) [54].

NSE = 1 − ∑ (Oi−𝑃𝑖)2n

i=1

∑ (𝑂i−O′)2ni=1

………………………………………………………(1)

137

The coefficient of determination, R2, an established measure in statistics quantifying

the model’s fit, is described as:

R2 = [ ∑ (𝑂𝑖

ni=1 −𝑂′)(𝑃𝑖−𝑃′)

√∑ (𝑂𝑖−𝑂′)2√∑ (𝑃𝑖−𝑃′)2ni=1

𝑛𝑖=1

] 0 ≤ R2 ≤ 1 ……………….………………..(2)

where n is the number of observations in the period under consideration, Oi is the i-th

observed value, 𝑂′ is the mean observed value, 𝑃𝑖 is the i-th model-predicted value, and

𝑃′ is the mean model-predicted value. NSE range is −∞ and 1, where 1 is considered as

the best fit for any model [55]. According to Dongquan et al. 2009 [56] an NSE value

of ≥0.5 is acceptable for SWMM. The acceptable R2 is 1 or close to 1, the larger

proportions of the observed variability can be explained with the simulations of

estimated values [57].

To evaluate the developed model for the goodness of fit test root mean square error

(RMSE), the RMSE-observations standard deviation ratio (RSR) is calculated as the

RMSE and standard deviation ratio of measured data. The RMSE and RSR are

explained as follows

RMSE = √1

𝑛 ∑ (𝑃𝑖 − 𝑂𝑖)2𝑛

𝑖=1 …………………………………………..………….. (3)

RSR = 𝑅𝑀𝑆𝐸

𝑆𝑇𝐷𝐸𝑉𝑜𝑏𝑠 …………………………………………………………………… (4)

where n is the number of observations in the period under consideration, 𝑃𝑖is the i-th

model-predicted value and 𝑂𝑖 is the i-th observed value, 𝑆𝑇𝐷𝐸𝑉𝑜𝑏𝑠 is the standard

deviation of the observed value. For RSR values between 0 and 1 are acceptable when

field-specific data are accessible for calibration. RSR varies from the optimal value of

138

0 to a large positive value. The lower RSR, the lower the RMSE, and the better the

model simulation performance [58].

Calibration is conducted for two different sets of parameters. The adjusted

parameters are given in Table 1.

Table-1: Model Parameter and Ranges used for Calibration and Uncertainty Analysis

Name Description Minimum Maximum

SWMM Parameter Percent adjustment

Width Sub-catchment width (m) -90 120

Slope Sub-catchment Slope -15 10

%Imperv Percentage of impervious area (%) -20 25

N-Imperv Manning n for impervious area -65 85

N-Perv Manning n for pervious area -60 95

Dstore-Imperv Depression storage for impervious area (mm) -90 95

Dstore-Perv Depression storage for pervious area (mm) -95 150

Conduit n Manning’s roughness -55 100

Conduit length Length of the conduit (mm) -60 90

3.3 Bayesian Approach and Error Modeling

Bayesian approach or analysis is a statistical paradigm that allows combining

prior information of the research or project to guide the statistical interpretation process

using probability statements. The “starting level” of knowledge regarding a parameter

can be expressed by a statistical distribution that we call “Prior distribution.” The prior

reflects our level of knowledge concerning the parameter. The model representing the

data generating mechanism for the observed sample is called sampling distribution, or

likelihood. The updated or ultimate distribution of the parameter after observing the

139

sample is called posterior distribution. From the posterior, we can extract inferential

quantities of interest: mean, variance, probability intervals, etc.

For this study, the prior parameters are the sub-catchment characteristics, for

example, area, width, % imperviousness, depression storage impervious, depression

storage pervious, Manning’s roughness, slope, drainage parameters like conduit length,

roughness, storage, and the parameter of three LID controls (Bioretention, Porous

Pavement, and Vegetative Swales). The model outputs (e.g., peak flow, TSS, Nitrite

and Nitrate and ortho phosphate) can be defined as the following function

�̂� = 𝑓(𝑋, 𝜃) ……………………………………………………………………... (5)

where �̂� = vector of predicted (simulated) values = {�̂�1, … . �̂�𝑛}

𝑋 = matrix of input forcing; and

𝜃 = vector of model parameters.

Now if, 𝑌 = vector of observed or measured values = {𝑦1, … . 𝑦𝑛}

the prediction error or residuals is calculated as,

𝐸𝑛(𝜃) = 𝑌 − �̂� ………………………………………………………………… (6)

The traditional calibration method minimizes error prediction by determining a

single set of optimal parameter values. As uncertainty arises from a different perspective,

the model needs to take those into account during simulation. Hence, the Bayesian

approach is adopted in this study. The Bayesian method outputs posterior probability

distribution (PPD) of the parameters to address the associated identity [59].

The PPD is defined as:

𝑃(𝜃 𝑌⁄ ) =𝑃(𝑌 𝜃⁄ )𝑃(𝜃)

∫ 𝑃(𝑌 𝜃⁄ )𝑃(𝜃)𝑑𝜃 …………………………………………………………… (7)

140

where 𝑃(𝜃) = prior distribution or knowledge of the parameters, 𝑃(𝑌 𝜃)⁄ =

conditional probability for the measured data Y given the parameter vector 𝜃,

In many cases, the prior distribution is given a significant variance or a uniform

distribution, reflecting the lack of knowledge about the parameter. A prior distribution

can also be determined from previous studies or expert knowledge. The PPD states that

the current state of the parameters is proportional to the likelihood function multiplied

by the prior density. 𝑃(𝑌 𝜃)⁄ is also called the likelihood function and the critical

parameter in Bayesian analysis. As equation (5) has an integral at the denominator, it is

difficult to estimate by an analytical approach. Hence, Bayesian analysis is coupled with

the Markov Chain Monte Carlo Simulation.

3.4 Markov Chain Monte Carlo (MCMC)

The Monte Carlo method draws samples from the target distribution and

estimates the mean and variance of the drawn samples. When a sample drawing depends

on the prior sample, this is called the Markov Chain property. Hence MCMC allows

random sampling even with the distribution of many variables by narrowing in on the

quantity that is being approximated from the distribution.

The Metropolis-Hastings (MH) algorithm is a widely used flexible variant of the

MCMC algorithm. MH uses a proposed probability distribution for the samples and sets

acceptance criteria for the new sample to be included in the chain [60, 61]. Usually, in

each iteration, current likelihood values are compared with the new likelihood value,

and new PPD is accepted according to the likelihood ratio. Hence MH is helpful where

141

the subsequent state probability distribution cannot be sampled directly. Since model

outputs vary according to the input parameters’ influence, the study conducted a

sensitive analysis before Bayesian Monte Carlo Analysis.

3.5 Sensitivity Analysis (SA)

A model is highly sensitive to the parameter that leads to a significant variation

in model output even there is the slightest change in the parameter value. On the other

hand, if the model output varies insignificantly with the variation in the input parameter,

the model is low sensitive to that parameter. Thus, sensitivity analysis and uncertainty

quantifications are closely related to each other.

In this study, the variability of the response is investigated with respect to a

probability distribution over the entire input space [62], and it adopts the “Sobol”

method [63] where the objective function is the sum of smaller functions with subsets

of the input space. Sobol sensitivity indices denote the quantity of variations in model

output that each uncertainty parameter is responsible for. Parameter with lower Sobol

index has a small impact in variations of the output, whereas higher index denotes the

model output will vary significantly with the change in the parameter.

If model input and output are 𝑥 and 𝑧 respectively, then density 𝑢(𝑥), and the

appropriate marginal densities 𝑢𝑖(𝑥𝑖) defines the uncertainty distribution to the

sensitivity of 𝑧 with respect to change in 𝑥 , that is the input to the SWMM model. So,

the marginal conditional expectation

142

𝐸[𝑧]𝑥𝐽 = {𝑥𝑗: 𝑗 ∈ 𝐽} = ∫ 𝐸[𝑧|𝑥]𝑢(𝑥)𝑑𝑥−𝐽𝑅𝑑−𝑑𝑗 …………………………..……….. (8)

Where 𝐽 = {𝑗1, … … . 𝑗𝑑𝑗}, a subset of input spaces, 𝑥−𝑗 = {𝑥𝑗: 𝑗 ∉ 𝐽}, and the marginal

uncertainty distribution is given by 𝑢𝐽(𝑥𝐽) = ∫ 𝑢(𝑥)𝑑{𝑥𝑖 ∶ 𝑖 ∉ 𝐽}𝑅

𝑑−𝑑𝑗 .

If the inputs are not correlated, then the variability of 𝐸[𝑍/𝑥𝐽] is decomposed as

𝑣𝑎𝑟 (𝐸[𝑧|𝑥]) = ∑ 𝑉𝑗𝑑𝑗=1 + ∑ 𝑉𝑖𝑗1≤𝑖<𝑗≤𝑑 + … . + 𝑉1……..𝑑, …………………………... (9)

where 𝑉𝑗 = 𝑣𝑎𝑟 (𝐸[𝑧|𝑥𝑗]), 𝑉𝑖𝑗 = 𝑣𝑎𝑟(𝐸[𝑧|𝑥𝑖 , 𝑥𝑗]) − 𝑉𝑖𝑉𝑗,

Now, the Sensitivity Indices, 𝑆𝐽 = 𝑉𝐽

𝑣𝑎𝑟(𝑧), will sum to one over all possible 𝐽 and are

bounded to [0,1] and measure the significance of a set 𝐽 of inputs.

In this study, sensitivity for each input is calculated in two ways:

1st order index for 𝑗𝑡ℎ variable, 𝑆𝑗 = 𝑣𝑎𝑟 (𝐸[𝑧|𝑥𝑗])

𝑣𝑎𝑟(𝑧) ………………………………….. (10)

The total sensitivity for input j, 𝑇𝑗 =𝐸[𝑣𝑎𝑟(𝑧|𝑥−𝑗)]

𝑣𝑎𝑟(𝑧) …………………………………. (11)

where 𝑥−𝑖 denotes all uncertain parameters except 𝑥−𝑖.

The 1st order indices measure the contribution variability to the output for each

input variable. In contrast, the total effect indices reflect the portion of variability that

total variation in each input is responsible for. Hence the sum of the total Sobol

sensitivity indices is equal to or greater than one [60]. If no higher-order interactions are

present, the sum of both the first and total order Sobol indices equals one.

Due to the involvement of many integrals, estimation of the sensitivity indices

is not straightforward, and fully Bayesian Markov Chain Monte Carlo simulation is

143

adopted during sensitivity analysis [65]. At each MCMC iteration, the output is

predicted by randomly sampling from the input set according to the Latin Hypercube

Sampling (LHS) scheme. Finally, a single realization of the sensitivity indices is

calculated [62].

In summary –

• Variance-based global sensitivity analysis is carried out using Monte Carlo sim-

ulation to find out influential mode parameters.

• The prior distribution is assumed either from the available knowledge of the past

studies [66] or from experimental measurement. Lack of knowledge of the pa-

rameter often defines prior distribution as uniform distribution in many cases.

• When there is limited data availability, the posterior distribution assumes a sim-

ilar shape to the prior. On the contrary, data sufficiency greatly influences the

shape of the posterior distribution [67]. Posterior distributions are calculated us-

ing the Bayesian MCMC method

• The uncertainty band is calculated using the 95% percentiles of the posterior

distribution of the predicted output. Model is rejected if most of the observed

points fall outside the band. The wider the bands, the higher the uncertainty in

estimation and the lower confidence in model output.

• For Each Bayesian update stage, MCMC methods applied above are run 10000

times for computing posterior distribution

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4. Findings and Discussions

4.1 Sensitivity Analysis

Identification and selection of significant parameters and their values have a

great impact on the model simulation. The model’s simulations were carried out using

input data on both hydrological and hydraulic parameters in SWMM5.1. Impervious

coefficient, depression storage (D) of both the impervious and pervious areas, width (W)

of the model area, manning coefficient (n) are identified as significant parameters by

some studies [68, 69]. The developed SWMM found rainfall depth, sub-catchment

area, %impervious, and slope as the sensitive or responsive parameters (Figure 3a) for

the hydrology module. In addition, SWMM found conduit roughness, conduit length,

and storage depth as responsive parameters for this suburban catchment (Figure 3b) for

the water quality module. The present studies also reveal that rainfall depth, area,

impervious percentage, slope, and manning coefficients are important sensitive

parameters. Rainfall depth shows linear effects almost, slope behaves like sine waves,

impervious and Manning Roughness shows the inverse relationship (Figure 4a). Here,

Figure 4b shows that the rate of change of the random parameters used in this analysis

generated different absolute relative sensitivity indexes for first-order indices. The

sensitivity index (SI) reveals rainfall depth and slope have higher sensitive indices,

where rainfall depth is more influential than slope (Figure 4b). Impervious percentage

and manning roughness are relatively low first-order indices.

145

Figure 3: a) Model runoff sensitivity analysis of sub-catchments for a) hydrological

parameters; b) the outfall of the catchment concerning hydraulic parameters [In SWMM,

time patterns allow external dry weather flow to vary periodically. They comprise of a

set of adjustment factors applied as a multiplier (SWMM5.1 Manual)]

Full effect sensitive index shows that sensitive index for the total effect of all

these parameters significantly higher than corresponding first-order indices. So the

effect on peak flow manifests largely through an interaction between input parameters

(Figure 4(c)). However, there is a significant variation in the main effects with 90%

central error bars. (Figure 4(d)). Uncertainty distribution of the sensitive parameters is

given in Figure 4(e).

(a) (b)

(c)

146

Figure 4: Sensitive Parameters Response of SWMM Quantity Module (a) Main

Response of the selected parameter (b) first-order SI indices represent the response of




(e) Uncertainty distribution of Sensitive parameters

Sensitivity analysis (Figure 5) was also analyzed for the quality module in this

study, like the hydrology module. The analysis reveals that conduit length, conduit

roughness, and storage depth are the most sensitive parameter. Conduit roughness and

(e)

(d)

147

storage depth behave like sine waves, whereas the conduit length responds inversely

(Figure 5a). Figure 5b indicates that the rate of change of the random parameters used

in this analysis generated different absolute relative sensitivity indexes for first-order

indices. The analysis showed that conduit depth and storage are more sensitive than

conduit roughness. Main effects of the selected parameter (Figure 5c), significant

variation in the main effects with 90% central error bars. (Figure 5d), uncertainty

distribution of the sensitive parameters Figure 5(e) is also analyzed here for the quality

module.

(d)

)

(b)

(a)

(b)

(c)

148

Figure 5: Sensitive Parameters Response of SWMM hydraulic Module (a) Main

Response of the selected parameter (b) first-order SI indices represent the response of




(e) Uncertainty distribution of Sensitive parameters

4.2 Model Efficiency

Based on the above sensitivity analysis and past studies, the different

combinations for model parameters are set for model simulation, and the best result

found according to the parameter settings (in Table-2)

Table-2: Model Parameter Settings

Parameter Adjustment Initial Value Calibrated value (Run 1) Calibrated value (Run 2)

% Impervious x 1.0 X 0.23 X 0.25

Depression Storage (mm) 2.032 1.524 2.2

Sub-catchment Width 1 1 1

(e)

149

Impervious "n" 0.02 0.02 0.017

Pipe "n" 0.013 0.011 0.012

Soil Conductivity (mm/h) 13.97 5.7 4.9

Manning’s n for swale 0.24 0.15 0.14

Suction head (cm) 20.32 18.2 17.5

DStore-Perv 0.25 0.22 0.2

DStore-Imperv 0.02 0.017 0.018

Model efficiency is calculated in terms of NSE, R2, and RSR: Table 3 is

represented the calibration and validation output for the SWMM, corresponding Nash-

Sutcliffe Efficiency (NSE) showed an average value 0.73 which indicates the good fit

of model because NSE values greater than 0.5 are acceptable for SWMM simulation

[70]. R2 values also signify the model performance. In terms of RSR value, the values

between 0 and 1 are acceptable though the ideal value of RSR is 0. The lower RSR, the

lower the RMSE, and the better the model simulation performance [64]. For the

developed SWMM model, the avg RSR value is less than 0.52 that also admit the model

efficiency.

Table 3: Model Calibration and Validation Output

Model

Eval-

uation

Peak Flow Cali-

bration

Peak Flow Vali-

dation

TSS

Calibration

Nitrate and Ni-

trite

Calibration

Ortho Phos-

phate Calibra-

tion

Run 1 Run 2 Run 1 Run 2 Site_1 Site_5 Site_1 Site_5 Site_1 Site_5

NSE 0.73 0.69 0.77 0.70 0.73 0.77 0.78 0.68 0.71 0.68

R2 0.75 0.64 0.8425 0.7991 0.87 0.82 0.93 0.92 0.90 0.88

RSR 0.53 0.61 0.42 0.51 0.58 0.52 0.41 0.55 0.62 0.57

150

4.3 Parameter Uncertainty

Posterior distribution of the model parameter (slope and intercept) indicates

uncertainty between observed and model peak flow. Posterior mean value of the slope

is 0.87 (red circle in Figure 6 (c)) with ranges from 0.81 to 0.97 and mean value of

intercept is 0.12 ((red circle in Figure 7 (a))) with ranges from 0.00 to 0.24. Few negative

values in interception occur because of the algorithm’s effort to minimize error, though

these negative values have no meaning in reality. An average observed value will be

within ±20% range from the model values considering the above values. For example,

if the model assumes the average peak flow is 100 m3/sec, then the actual value will be

from 80m3/sec to 120m3/sec. The green dotted line indicates the true values of the

parameter. The range will shrink and converge to a single value for the greater number

of simulations, indicating a greater confidence in the model parameters.

Figure 6: (a) Posterior distribution of Intercept (b) 10000 Iteration of MCMC Simulation

with the random progression of the intercept of the model (c) Posterior distribution of

151

Slope (d) 10000 Iteration of MCMC Simulation with the random progression of the

slope of the model

Posterior distribution of intercept (Figure 6a) and slope (Figure 6b) extends key

information regarding the comparative significance of the developed SWMM

parameters considered for the analysis. Except for percentage imperviousness (Imperv)

for impervious subareas (Dstore-Imperv), slope and mannings roughness for both

pervious and impervious subareas are not comprehensive. This suggests that the

SWMM parameters that exhibited no wide uncertainty range substantially affect the

Chipuxet Watershed rainfall-runoff characteristics. This also has a practical

consequence in terms of prioritizing resources on data collection. This means the

availability of more accurate or sensitive data may help in improving the accuracy of

runoff quantity and quality simulation for the selected study area.

4.4 Predictive Uncertainty

Runoff predictive uncertainty is estimated by propagating the different samples

of the posterior distribution through the SWMM5.1 model after the posterior

distribution of the model parameters is done, and the runoff and reporting the respective

prediction uncertainty ranges (e.g., 95% confidence interval). However, this prediction

interval represents parameter uncertainty only; it doesn’t consider other sources of error,

including model structural, forcing data, and calibration data uncertainty. Figure 7(a)

represents the bands derived from MCMC simulations of predicted & observed peak

flow. The light grey line indicates the 95% uncertainty band. The black line denotes the

fitted line of the model, which is within 95% uncertainty limits.

152

The uncertainty on model output can be quantified by the mean and the

maximum amplitude of the 95% confidence bounds. While the mean bound amplitude

(MBA) represents the average uncertainty on the computed hydrograph over the entire

event (Peak flow, TSS, Nitrate-N, and Orthophosphate-P), the maximum bound

amplitude (MaxBA) usually occurs for the most significant value. It is, therefore, an

indicator of the uncertainty in the prediction of the peak flow, TSS, Nitrate-N, and

Orthophosphate-P. The MBA is (differences lower than 10%) when the acceptability

threshold is lower than 0.8 (Fig. 7), 0.95 (TSS), TN (0.8), and 0.75 (phosphate).

Figure 7: (a) Predictive Uncertainty Band (b) MCMC simulation of the data

(a)

(b)

153

The red and blue lines indicate the predictive uncertainty that considers both the

parametric uncertainty and the residual error model. Most of the observed points are

within this limit, with some exceptions that might result from uncertainty in

measurement. For example, a wider band in high volume peak flow means higher

uncertainty as the frequency of high volume rainfall is lower than the low and medium

intensity rainfall. Figure 7(b) also shows that more than 70% of data are within Monte

Carlo lower and upper limits.

154

Figure 8: Predictive Uncertainty Band MCMC simulation of a) TSS, b) Nitrate-N , and

c) Orthophosphate-P

5. Conclusion

The study adopted the Bayesian Markov Chain Monte Carlo approach in

demonstrating parameter uncertainty and predictive uncertainty in SWMM model

simulation on the Chipuxet watershed, Rhode Island, USA, by generating 10000 sets of

sensitive parameters identified sensitive analysis. The value of the non-random

parameters has importance in uncertainty quantification. Hence during calibration, most

likelihood values of the nonrandom parameters need to be set up. The numerical

efficiency between observed and simulated hydrographs was computed by measuring

RSR, R2, and NSE. The calibration attempt was in good agreement with the observed

counterparts when evaluated graphically using several goodness-of-fit measures (RSR,

R2, and NSE). The average NSE value is greater than 0.7, R 2 is 0.82, and the average

value of RSR (<0.6) placed the model in good shape.

155

Sensitive analysis suggests that runoff in the study area is sensitive to rainfall

depth, impervious percentage, slope and manning coefficients, conduit length, conduit

roughness, storage depth and combined interaction between these parameters

influencing peak flow. Total predictive analysis with 95% confident interval captures

most of the observed discharge data supporting the uncertainty quantification of the

model.

The prior knowledge of the distribution of the parameter plays a significant

impact in the model uncertainty structure. In this study, parameters are considered to

follow uniform distributions.

Future studies include considering surrounding environment response, spatial

variation, management criteria in uncertainty quantification, and cost-benefit analysis

for accommodating the wide range of peak flow, TSS, Nitrate-N, and Orthophosphate-

P considering the occurring probability and duration.

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161

CHAPTER 5

Manuscript V

Predicting Runoff Chloride Concentrations in Suburban Watersheds Using an

Artificial Neural Network (ANN)

Published in Hydrology 2020, 7, 80

Khurshid Jahan 1 and Soni M Pradhanang 2,*

1 Khurshid Jahan1,*, Soni M Pradhanang1,*, and Russell D. Briggs2

1Department of Geosciences, University of Rhode Island, RI, Kingston, USA 2Department of Sustainable Resources Management, ESF SUNY College of Environ-

mental Science and Forestry, NY, USA

*Correspondence: [email protected] (S.M.P)


162

Abstract: Road salts in stormwater runoff, from both urban and suburban areas, are of

concern to many. Chloride-based deicers [i.e., sodium chloride (NaCl), magnesium

chloride (MgCl2), and calcium chloride (CaCl2)], dissolve in runoff, travel downstream

in the aqueous phase, percolate into soils, and leach into groundwater. In this study, data

obtained from stormwater runoff events were used to predict chloride concentrations

and seasonal impacts at different sites within a suburban watershed. Water quality data

for 42 rainfall events (2016-2019) greater than 12.7 mm (0.5 inches) were used. An

artificial neural network (ANN) model was developed, using measured rainfall volume,

turbidity, total suspended solids (TSS), dissolved organic carbon (DOC), sodium, chlo-

ride, and total nitrate concentrations. Water quality data were trained using the Leven-

berg-Marquardt back-propagation algorithm. The model was then applied to six differ-

ent sites. The new ANN model proved accurate in predicting values. This study illus-

trates that road salt and deicers are the prime cause of high chloride concentrations in

runoff during winter and spring, threatening the aquatic environment.

Keywords: stormwater; artificial neural network (ANN); water quality; road salts;

deicers

163

1. Introduction

Urban areas require the construction of buildings, roads, and parking areas, yet

such urban development causes hydrologic impacts and pollution as pervious surfaces

are made impervious [1]. For safety, given abundant snowfall during the winter season,

most communities in New England use salt or deicing on roads and parking areas. Road

salts or deicing during the winter season are the primary factors for increasing salinity

in surface soils, surface water, groundwater, and runoff. In the USA, an average of 24

million metric tons of road salt is applied each year to roads [2]. It is well-established

that the application of road salts leads to the accumulation of sodium and chloride in

soils and surface waters [3–5], with adverse impacts on downstream aquatic ecosystems

[6]. In fact, when impervious surface areas increase, the areas that need to be deiced

also increase.

Chloride-based deicers [i.e., sodium chloride (NaCl), magnesium chloride

(MgCl2), and calcium chloride (CaCl2)] dissolve in runoff, percolate into soils, and leach

into groundwater. Chloride from chloride-based deicers does not efficiently precipitate

or biodegrade but is absorbed by mineral/soil surfaces [7]. Although winter road deicing

is an essential service for urban areas in USA (especially in the upper Midwest and

Northeast), it contributes to a significant increase in chloride concentration [8]. The

United States Geological Survey (USGS) (2014) conducted a temporal, seasonal, and

environmental analysis of chloride concentrations in urban areas and assessed effects

on water quality and the environment, especially on aquatic organisms across the USA

164

[8]. This study concluded that there is an increasing trend of high chloride concentra-

tions in urban areas due to expansion of impervious cover that requires deicing.

An increasing trend in chloride concentrations in some US rivers is shown in

Figure 1 and attributed to increased usage of road salt; the trend is positive in New

England (USGS, 2014). The increasing salinity not only threatens aquatic ecosystems,

but also contributes to corrosion in water distribution systems. Salinity can increase

even with little snowfall due to efficient transfer of chloride to wastewater discharge or

septic systems [8]. Areas that have no snow, such as Florida, also showed increasing

salinity. In the case of Florida, less than normal rainfall since 1990 combined with

groundwater level decline due to over pumping explain the observed increasing salinity

[9–11]. The characteristics and degradation of urban runoff quality and its impact on

the environment largely depend on the urban land-use practices, site geology, and hy-

drogeology. The large quantity of road salt that is applied every year for snow removal

in the USA is one of the major contributors to declining stormwater quality.

165

Figure 1. Chloride concentration trends from 1992 to 2012 in the USA show regional

differences (modified from United States Geological Survey Report, 2014)

Recently, several studies have applied artificial neural network (ANN) methods to

predict resulting water quality based upon input variables [12]. Since 1990, ANN has

been applied in many fields, including environmental sciences, ecological sciences, and

water engineering [13]. According to Haykin (1999) [14], ANN is highly capable in

modeling nonlinear system estimation and is highly adaptable. ANN allows precise pre-

dictions of the target parameter for specific materials or stages [14, 15].

In this study, an ANN model is developed with a back-propagation algorithm. The

back-propagation algorithm incorporates highly nonlinear relationships [15]. The ANN

model was developed for rapid calculation and prediction of selected water quality var-

iables at any location of interest. Within the model, unknown parameter weights are

166

adjusted to obtain the best correlation between appropriate input parameters or a histor-

ical set of model inputs and the corresponding outputs [16]. This study provides the

ANN modeling method needed to simulate and forecast chloride concentrations in run-

off. The aim of the study is to (i) develop an ANN model of the system trained using a

small data set, (ii) obtain the best-fit models for predicting chloride concentrations using

data from monitoring sites, (iii) evaluate the ANN model performance using 3 years

(2016-2019) of observed data versus predicted data from the model, and (iv) determine

the accuracy of the ANN model performance. The model also assesses the impact of

road salt applications through assessment of a spatial density distribution focused on

probable high chloride concentration in an area.

2. Materials and Methods

A three-year study on the effectiveness of the stormwater best management prac-

tices (BMPs) was conducted in the Chipuxet watershed of South Kingstown, Rhode

Island, USA (Figure 2).

Figure 2. The map illustrates the location of the study area in Rhode Island, USA. Red

bounding box shows the exact location of the study site shown above (Google Earth

screenshot- on the right).

167

An overview of the chloride concentration for three years (2016–2019) from six

sites represented in Figure 3. Based on the analysis of the stormwater runoff quality

data, an apparent seasonal variation of chloride concentration is observed (Figure 3).

The higher concentration of chloride was found during winter and early spring season

(at the tail end of winter). Our study results are highly consistent with the study

conducted by the USGS (2014) [7]. The USGS study showed the increasing trend of

chloride concentration in the New England zone, and our data also provided the same

impression. As stated above, high chloride concentration (0.8–197.9) mg/L was seen on

the winter samples for all the sites. Site 5 and 6 are in close proximity to the parking lot;

parking lots are highly impermeable, and exhibited higher chloride concentration (0.9–

197.9) mg/L than the remaining four sites that are close to the agricultural field (Figure

3). The chloride concentration data were then used to investigate the future scenario

through the ANN model. The steps used to develop the model include the choice of

model performance criteria, preprocessing of available data, the selection of appropriate

model inputs, and network structure.

Figure 3. Variability in observed chloride concentrations in runoff are shown, from

2016 to 2019.

168

2.1. Artificial Neural Network (ANN)

The ANN concept was first introduced by McCulloch and Pits in 1943, and ANN

applications in research areas started with the back-propagation algorithm for feed-for-

ward ANN in 1986 [17,18]. ANNs consist of multiple layers; basic layers are common

to all models (i.e., input layer, output layer), and several hidden layers may be needed

(located between the input and output of the algorithm) [19]. Each of the layers in an

ANN consists of a parameterizable number of neurons. Neurons are activation functions

of adjustable weight based on a priori and domain knowledge [20]. In this study, an

ANN with three different learning approaches, such as back-propagation neural network

(Levenberg–Marquardt), curve fitting, and density distribution, were considered and

adapted to develop the final model for predicting and validating chloride concentration

in the runoff. The overall objective of the ANN model was to reduce model error, E,

defined as

𝐸 =1

𝑝∑ 𝐸𝑝

∞

𝑛=1

(1)

where p = total number of training patterns and Ep = error for the training pattern p.

Ep is calculated with the following equation:

𝑬𝒑 =𝟏

𝟐∑(𝒐𝒌 − 𝒕𝒌)𝟐

𝑵

𝒌=𝟏

(2)

where N = total number of output nodes; ok = network output at the kth output node;

and tk = target output at the kth output node. Additional details on the mechanics of this

study are described below.

169

2.2. Back Propagation (BP) Algorithm

Back propagation (BP) is the most widely used method for training multiplier feed-

forward networks. Before BP, almost all of the networks used non-identifiable complex

binary nonlinear methods to self-test, such as step functions, statistical time series mod-

els, auto regressive integrated moving average (ARIMA), and moving average (MA)

[21,22]. Layered networks from BP algorithm are useful for nontrivial calculations with

the different attractive features such as fast response, fault tolerance, the ability to ob-

servation from input parameters, and the capability to generalize beyond the training

data. A set of input variables is needed to train the network to match desired outputs,

with a function that measures the “value” of differences between network outputs and

desired values [22]. The most straightforward implementation of the standard BP algo-

rithm adjusts the network weights and biases in the target direction, and this adjustment

helps to achieve the model accuracy.

The back-propagation neural network structure consists of two or more layers of

neurons, and network weights connect all the neurons [22,23]. The final output is cap-

tured by the developed system, when input data pass through the hidden layers to the

output layer. This process is shown in Equation 3.

𝑢𝑗 = ∑ 𝑤𝑗𝑖𝑥𝑖

𝑝

𝑖=1

(3)

In Equation 3, Wji represents the weights that connect two neurons i and j, and every

neuron calculates its output based on the number of stimulations it obtains from the

given input vector xi, where xi is the input of neuron i. The “net input” of a neuron is

170

measured as the weighted sum of total number of input variables, and the output of the

neuron is based on the active function (active function indicates the magnitude of the

“net input” [24]). BP is a training algorithm consisting of two steps: first, values are fed-

forward, and second, error is calculated and propagated back to the earlier layers.

2.2. Curve Fitting Algorithm

Polynomial models for curves are given by equation (4)

𝑦 = ∑ 𝑝𝑖 𝑋𝑛+1−𝑖

𝑛+1

𝑖=1

(4)

where n+1 and n represent the order of the polynomial and the degree of the polynomial,

respectively, and the range of n is 1 ≤ n ≤ 9.

A third-degree (cubic) polynomial equation (5) is also pertinent

𝛾 = 𝑝1 𝑥3 + 𝑝2 𝑥

2 + 𝑝3𝑥 + 𝑝4 (5)

Polynomials (as in Equation 5) are frequently used when a simple experiential

model is required, or when a model needs interpolation or extrapolation. The main ad-

vantages of polynomial fitting comprise cognitive flexibility for the most complex and

large data sets [25]. The polynomial curve fitting process is simple and linear [25].

2.4. Density Distribution algorithm

Distribution fitting applies to model the probability distribution of a single variable.

The normal distribution is the most applied statistical distribution approach in research.

171

In this study, we calculated the probability density function (PDF) of the predicted chlo-

ride concentration. The following equation (6) is used in this study to specify the prob-

ability of the predicted output [25].

𝑓(𝑥) = 1

√2𝜋𝜎exp(

−(𝑥 − µ)2

2𝜎2, > 0 (6)

Where 𝜎 is standard deviation, 𝜎2 is variance, and µ is mean.

2.5. Model Structure

In recent years, neural network technology has been adopted in water quality

prediction, in which the back-propagation network is commonly used [26,27]. The

model created in this study is a BP neural network model with a single hidden layer

(Figure 4). In this ANN, the input layer is R, the hidden layer is a1, the output layer is

a2, the weight matrix of the input layer is IW1.1, and the weight matrix from the hidden

layer to the output layer is LW2.1. The threshold values of the hidden and output layers

are b1 and b2, respectively. f1 and f2 are the neuron transfer functions of the hidden and

output layers, respectively.

As theoretically verified, the BP model as shown in Figure 4 can handle any

nonlinear function with minimum interruptions at any accuracy as long as there are a

sufficient number of neurons in the hidden layer of the model and the number of neurons

are determined based on a priori and domain knowledge [28]. The proposed ANN model

(Figure 5) has two hidden layers of sigmoid neurons that are followed by an output layer

of linear neurons. Sigmoid neuron makes the output smoother, and a small change in

172

the input only causes a little variation in the output [29]. This network system can be

utilized as a general function approximator. It can estimate any function with a finite

number of discontinuities given sufficient neurons in the hidden layer [30,31].

Figure 4. This diagram illustrates the structure of a conventional feed-forward back-

propagation neural network model

In the developed model structure (Figure 5), the input and output variables are

established for the evaluation of water quality. Multiple layers of neurons of the devel-

oped ANN structure with nonlinear transfer functions let the network assess nonlinear

and linear relationships that underlay input and output vectors. The final output layer

is linear, allowing the network to produce values outside the range -1 to +1.

Figure 5. This diagram illustrates the structure of the artificial neural network (ANN)

model developed in this work.

173

2.5.1. ANN Parameter Selection: Hidden Layers and Nodes

The number of hidden layers in ANN model is usually determined by trial and error.

The number of training set samples should be higher than the number of synaptic

weights, a rule of thumb for defining the number of hidden nodes [31,32]. Most ANN

modelers usually consider a one-hidden-layer network (i.e., the number of hidden nodes

is between input nodes and (2*(input nodes) + 1) [30]). However, hidden nodes should

not be less than the maximum of one third of input nodes and the number of output

nodes. The optimum value of hidden nodes is fixed by trial and error. Networks with

minimum number of hidden nodes are usually preferred due to better generalization

capabilities and fewer overfitting problems. For this study, a trial and error procedure

for the number of hidden node selection was carried out by gradually changing the num-

ber of hidden layer nodes.

2.5.2. ANN Parameter Selection: Learning Rate and Momentum

The functions of the learning rate and momentum parameters are to enhance model

training and ensure that error is reduced. There is no precise rule for the selection of

values for these parameters. Here, the learning rate was controlled by internal valida-

tion: after the end of each epoch, the weights were updated. The number of epochs with

the smallest internal validation error indicates which weights to select [33]. In this study,

the learning rate for the weights connecting input layer and the hidden layer was set at

double the size of the learning rate for the weights connecting the hidden layer to the

output layer, to increase the rate of network convergence. The momentum was initially

fixed at a value of 0.015, with the number of hidden nodes initially estimated as number

of input nodes +1, similar to the study conducted by Maier and Dandy (1996) [34].

174

2.5.3. ANN Parameter Selection: Initial Weights

When the weights of a network is trained by BP, it is always better to initialize from

small, non-zero random values, although ANN modelers can start over with a different

set of initial weights [22,23]. In this study, the amplitude of a connection between two

nodes (synaptic weights) of the proposed ANN networks was adjusted using the nor-

mally distributed random numbers having the range from-1 to 1.

2.5.4. ANN Parameter Selection: Selection of Input Variables

In an ANN, one of the main tasks is to determine the model input variables that

significantly affect the output variable(s). The selection of input variables is usually

related to a priori knowledge of output variables, inspections of time series plots, and

statistical analysis of potential inputs and outputs. In this study, the input variables for

the present neural network modeling were selected based on a statistical correlation

analysis of the runoff quality data, the prediction accuracy of water quality variables,

and domain knowledge. Domain knowledge is the specific field knowledge that sup-

ports interpretation of data when applying machine learning algorithms like regression,

stepwise approach, and classification to predict some test data [35]. In a stepwise ap-

proach, separate networks are trained for each input variable [36]. We experimented

with the water quality variables included in the parameters listed above in several mod-

els to both identify the optimal predictive model and reduce the monitoring cost by in-

cluding fewer input parameters. After selecting the appropriate input variables, the next

step involved determining appropriate lags for each of these variables. The selected ap-

175

propriate input variables are rainfall amount, duration of rainfall, intensity, runoff coef-

ficient, runoff depth, peak discharge, turbidity, total suspended solids (TSS), dissolved

organic carbon (DOC), sodium, chloride, and total nitrate concentrations that were used

to develop the ANN model. Appropriate lags are needed for complex problems, where

the numbers of potential inputs are significant, and no a priori knowledge is available.

Lags allow the model to establish significant connection or bonding between the output

and the input variables. By doing so, the best network performance is retained, and the

effect of adding each of the remaining inputs in turn is assessed. The correlations be-

tween the input variables and output variables are computed separately for each lagged

input variable [37]. In this study, optimal networks for each of these combinations were

obtained with these time-lagged variables, and the results were compared with the target

dataset.

2.5.5. ANN Parameter Selection: Data Partition

It is essential to divide the data set in such a way that both training and overfitting

test data sets are statistically comparable. The test set should be approximately 10–30%

of the size of the training set of data [38]. In this study, the water quality data were

divided into three partitions: the first set contained 70% of the records used as a training

set, the second test contained 15% of the records and was used as an overfitting test set,

and the rest of the data (15%) were used as the validation set. This process is necessary,

because the efficiency of the developed neural network model is highly dependent on

the quantity and quality of the data as stated by Palani et al., 2008 [18].

176

2.5.6. ANN Parameter Selection: Model Performance Evaluation

The model's efficiency was evaluated using the root mean square error (RMSE, see

Equation 7), the mean absolute error (MAE, see Equation 8), and R2 (see Equation 9)

[39]. Scatter plots and time series plots were used for visual comparison of the observed

and predicted chloride concentrations values.

R2 values of zero indicate that the observed mean is as good a predictor as the

model, R2 value of one represents a perfect fit, and a negative R2 value reflects a better

predictor than the model [40]. Depending on the sensitivity of water quality parameters

and any mismatch between the forecasted and measured water quality variables, one

can decide whether the predictive power of the ANN model is accurate enough to inform

crucial decisions regarding data usage.

RMSE = √1

𝑁∑(𝑋𝑜𝑏𝑠𝑒𝑟𝑣𝑒𝑑 − 𝑋𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑)2 (7)

MAE = 1

𝑁 ∑ |𝑋𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑 − 𝑋𝑜𝑏𝑠𝑒𝑟𝑣𝑒𝑑| (8)

𝑅2 = 1 − 𝐹

𝐹0 (9)

F and F0 could be described using following two equations.

F = ∑(𝑋𝑜𝑏𝑠𝑒𝑟𝑣𝑒𝑑 − 𝑋𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑)2 (10)

F0 = ∑(𝑋𝑜𝑏𝑠𝑒𝑟𝑣𝑒𝑑 − 𝑋𝑀𝑒𝑎𝑛 𝑂𝑏𝑠𝑒𝑟𝑣𝑒𝑑)2 (11)

Where N is the total number of observations.

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The other primary criterion used to select the optimum ANN model was the sum of

square error (SSE), determined from the following empirical equation:

𝑆𝑆𝐸 = ∑ 𝜔𝑖

𝑛

𝑖=1

(𝑦𝑖 − ŷ𝑖)2 (12)

where wi are the weights and yi and ŷi are the observed response value and the fitted

response value, respectively.

The weights determine how much each response value influences the final pa-

rameter estimates. A high-quality data point influences the fit more than a low-quality

data point. Weighting data is recommended if the absolute weights are known, or if

there is good cause for weighting data differently.

3. Results and Discussion

3.1. Model Output

The BP ANN architecture was applied to five hidden layers with different acti-

vation functions and initial weights of 0.3, optimum learning rate (0.1), and momentum

(0.015), as described in Sections 2.5.2 and 2.5.3. The proposed ANN model was de-

signed considering 11 input variables from 42 storm events. The sensitivities of the in-

put parameters for the chloride concentration prediction are smaller than those used for

the validation dataset. An individual ANN model was run for each of the sites consid-

ering the same ANN model structure shown in Figure 5 and the input parameters. The

parameters that produce the ‘‘best results” for all sites (Table 1) were then used as the

final chloride concentration prediction. The model output or the performance of the

ANN model was evaluated based on the R2 values for training, validation, and testing.

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R2 values for each of the sites were similar for the three data partitions, as indicated in

Table 1. The weights are methodically changed by the learning algorithms such that for

a given input, the difference between the ANN output, and the actual output was small.

The developed ANN model with nine hidden nodes was considered optimal here, con-

sidering the output (Table 1 and 2). The optimum network parameters associated with

the model output are presented in Tables 1 and 2. Validation errors were calculated after

the optimization of the network parameters and the topology. Error was calculated in

two ways. First, the cross validation was applied. In this method, data were separated

into three parts: training (70%), validation (15%), and testing (15%). The output of the

first technique is shown in Table 1. Secondly, the ANN predicted outputs were validated

using curve fitting technique. Curve fitting analysis showed a good fit between the tar-

geted or observed and the predicted value (Figure 6). The model outputs were consid-

ered acceptable based on the R2 values for training, validation, and testing. In general,

the accuracy of the model can be improved by adding data to the validation step or to

input variables.

Table 1. ANN model output for all the sites.

Location Performance (Epoch) Training (%) Validation (%) Testing (%)

Site_1 9 95 87 88

Site_2 5 100 72 95

Site_3 5 100 52 93

Site_4 3 99 97 93

Site_5 3 99 78 91

Site_6 2 97 83 88

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3.2. Curve Fitting Analysis

Curve fitting analysis examines the relationship between target output and the

model output. The fit between the target and predicted values were represented for all

six sites (Figure 6). Except for site number 1, all the other sites had the best fitting

between the two datasets (target and model output). This fulfilled the aims of applying

the polynomial bi-square fitting for the presence of concentrated chloride. Polynomial

fitting with a high-order polynomial uses the large predictor values as the basis for a

matrix, which often creates scaling problems [25]. In this study, most of the analyzed

chloride concentration range is from 1 to 20 mg/l, but during the winter these ranges are

from 25 to 200 mg/l. No axis range modifications were made here; axes ranges were

kept as appropriate for the chloride concentration range. A reasonably good match be-

tween the output from the developed ANN model and the curve fitting output was ob-

tained for the sites. To illustrate this, a prediction boundary is provided in Figure 6 for

every site with a 95% confidence interval.

180

Figure 6. Curve fitting assessment between ANN output and target data of six different

sites. (a) Site 1, (b) Site 2, (c) Site 3, (d) Site 4, (e) Site 5, and (f) Site 6. Site 1, 3, 4 and

5 showed the similar trend and data were not scattered. On the other hand, site 2 and 6

has scattered data point but 80% of dataset were in the confidence interval.

181

Table 2. Summary of the curve fitting assessment.

File Name SSE R-Square Adj R -sq RMSE

Site_1 1.96 0.99 0.99 0.22

Site_2 171.58 0.93 0.93 2.07

Site_3 7.9 0.99 0.99 0.44

Site_4 231.3 0.99 0.99 2.4

Site_5 473.6 0.98 0.98 3.4

Site_6 4051 0.93 0.92 10.06

Curve-fitting information regarding the developed ANN model output is pre-

sented in Table 2; note the SSE, R-Square, and RMSE values are robust. All the ANN

models constructed using nine nodes in the hidden layer produced the lowest SSE.

Therefore, the site 3 ANN model showed the lowest value of SSE, and the model for

Site 6 showed the highest SEE value having the lowest R-square value. In summary, all

the ANN models developed here for six sites showed an acceptable range for all the

model justification factors. The predicted values are reasonable for all the sites. Curve

fitting assessment is a cross-validation approach, proving the accuracy of the developed

ANN model. No significant difference in the R2 values can be seen in the Table 2.

3.3. Density Distribution of the Predicted Chloride Concentration

The density distribution was applied to show the spatial distribution of the pre-

dicted chloride concentration values (Figure 7). Usually, the continuous data values tend

to cluster around the mean in a normal distribution, and the farther a value is from the

182

mean, the more uncertain it is. The tails are asymptotic, which implies that they ap-

proach but never meet the X-axis. In this study, density distribution curve fitting resulted

in a 95% confidence interval. This 95% confidence interval means that 95% of values

fall within two standard deviations from the mean.

Considering the six study sites, four sites are close to an agricultural field and

two are close to a parking lot; the spatial distribution range of the agricultural field sites

is smaller than those close to the parking lot. For sites 5 and 6, more than 70% of pre-

dicted chloride concentrations are clustered at the mean value, and the peak is wide. On

the other hand, the density distribution for sites 1, 2, 3, and 4 cover less than 50% pre-

dicted chloride concentration values. Sites 5 and 6 have higher chloride concentrations,

because they received salt/chloride from both sides (from the road and the parking lot).

The highest spatial range was observed for sites 5 and 6. These results are consistent

with the fact that sites 5 and 6 received the chloride from both sides.

3.4. Cross Validation Based on Snow and Precipitation Events

Predicted data were cross validated by taking advantage of temporally close

snow and precipitation events. Chloride concentrations were high in storm events that

followed severe snow events. Here, we analyzed data from US climate data repositories

with three years (2017, 2018, and 2019) of snow and daily precipitation [41] (Figure 8).

183

Figure 7. Density distributions (ANN outputs) for chloride concentrations at all sites (a) Site 1, (b)

Site 2, (c) Site 3, (d) Site 4, (e) Site 5, and (f) Site 6 reveal shifting means. Spatial distribution covered

<50% for site 1, 2, 3 and 4 whereas site 5 and 6 covered >70% of predicted chloride.

184

Figure 8. Precipitation and snow events (a) 2017, (b) 2018, and (c) 2019 in South Kings-

town, RI. (a) total four significant snow events (>10 cm) and low to moderate precipi-

tation events appeared during winter in 2017; (b) three significant snow (>10 cm) events

(among them one was the worst >25 cm) and low to moderate with high frequency

(c)

185

precipitation occurred during winter in 2018; and (c) only one significant snow and low

to moderate precipitation happened during the winter in 2019.

Figure 9. Predicted concentrations for three sites reflect heavy Cl inventory in Winter

2018.

Rhode Island receives approximately 94 cm of snow every year, but snowfall

totals can vary significantly from town to town, even though the state is relatively small

and the terrain is flat [42]. Moreover, the number of snow events vary widely from year

to year. Both salt and sand are used on roads during snow events. Given additional chlo-

ride derived from snow removal deicers, Rhode Island collects a considerable amount

of chloride in its surface water and groundwater, and the salts accumulate in the soils

and later percolate into the groundwater. The groundwater becomes saltier every year,

since chloride is a dissolved phase and cannot be removed naturally from the water [42–

44]. Seventy percent of the salt applied to roads stays within the region’s watershed

[45]. In this study, chloride data for rain events occurring immediately after snow events

in 2017, 2018, and 2019 showed that chloride concentrations increased.

186

Runoff pollutant concentrations also depend on the size and duration of precip-

itation event. Both longer duration storms and storms of high intensity impact chloride

concentrations. Longer period storms and high intensity storms can dilute the pollutant

concentration. For example, on January 4th, 2018, a 220 mm snow event preceded 830

mm of rainfall on January 13th, 2018, in a storm of 4 hours’ duration. Chloride concen-

trations were highest after the January 13th storm events. On the other hand, a 147 mm

snow event occurred on March 10th, 2017, followed by March 17th, 2017 storm of 7

hours’ duration. The detected chloride concentration from March 17th, 2017 storm

events were not significant relative to 2018 storm events. In the ANN model predicting

chloride concentration, runoff volume and duration of the rainfall are considered as a

positive sensitivity parameter. The March 2017 storm pair, which did not lead to ele-

vated chloride, could reveal the counteracting impacts of street density, street width,

and location of the street. Furthermore, the accumulated chloride could be attributed

primarily to the amount of salt application, which varies from event to event.

Chloride concentrations greater than 600 ppt (1 mg/l = 1ppt) are considered

harmful for freshwater aquatic life and for the groundwater in general [46]. The devel-

oped ANN model prediction (Figure 9) and probability density output (Figure 7) indi-

cated that aquatic habitats at sites 5 and 6 are at risk. State planners need to take neces-

sary action regarding the implications of road salting and snow removal.

4. Conclusion

In this study, a new ANN model is developed to predict elevated chloride con-

centrations due to road salt and deicer applications in a suburban watershed, based on

187

three years of data (2016-2019) collected at six study sites. Study sites are close to ag-

ricultural land (Sites 1, 2, 3, and 4) and an impervious parking lot (Sites 5 and 6). Sea-

sonal variation is evident in the three years of collected data. For the ANN model, input

variables were derived from the hydrometeorological database, stormwater runoff qual-

ity, key network parameters, and network topology. Preliminary ANN models were con-

structed using a subset of all data (for 42 storm events from 2016 to 2019) where it

covered all four seasons (15 winter events, 6 spring events, 7 summer events, and 15

fall events). A series of sensitivity analyses were considered to determine the relative

significance of input variables used in the ANN models. Applying the BP algorithm,

developed ANN models showed a good fit between observed and predicted data (about

91%). Model accuracy was initially optimized using a cross-validation approach, and

the developed model offers an appropriate and time-efficient approach to constraining

the target water quality parameter. The curve fitting assessment resulted in a 95% con-

fidence interval, used here as cross validation of ANN outputs, and provided an opti-

mum summary for every site. The predicted ANN outcome could be more significant

or could be trained better if the study duration were longer than three years and/or in-

volved more frequent events. This study focused on the winter season because of the

amount of road salt applied to the impervious surfaces, generating high concentrations

of chloride in runoff water. The presence of chloride in non-winter season data is neg-

ligible compared to the winter season, but the detection of chloride could be due to the

use of fertilizer in the agricultural zone. According to the best-fit results, chloride in the

study area is mostly affected by the rain and snow that occurred during the winter sea-

188

son, and chloride concentration depends on storm duration, intensity, and runoff vol-

ume. We propose neural network modeling as an effective tool for water quality param-

eter prediction. Finally, density distribution analysis revealed the spatial distribution

(the amount of clustered data value around the mean) of the chloride concentration.

Density distributions showed about 70% of clustered value is detected for Sites 5 and 6,

due to the nearby parking lot. The other four sites, which are close to the agricultural

zone, cover less than 50%. These findings again point to used road salt as the main agent

of chloride delivery to the groundwater. ANN modeling of environmental data has great

potential in future work on improved prediction of chloride and other pollutant concen-

trations, and provides a useful tool for water resource and environmental managers.

Author Contributions: KJ and SMP, conceptualization; KJ, data collection, analysis,

and writing the original draft preparation; SMP, writing, review, and editing, SMP,

funding acquisition. All authors have read and agreed to the published version of the

manuscript.

Funding: The research study is funded by the Rhode Island Department of Transporta-

tion, SPR-234-2362, and the University of Rhode Island.

Acknowledgments: Authors would like to show their gratitude to the Rhode Island

Department of Transportation (RIDOT) for their financial support. In addition, we also

thank Dr. Gavino Puggioni and Dr. Nisa Khan for their valuable comments and recom-

mendations. We are also thankful to Dr. Thomas Boving for site preparation help. Eng-

lish editing assistance was received through Dr. Dawn Cardace.

189

Conflicts of Interest: The authors declare no conflicts of interest. The funders had no

role in the design of the study; in the collection, analyses, or interpretation of data; in

the writing of the manuscript; or in the decision to publish the results.

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CHAPTER 6

CONCLUSION

The novelty of the study is assessed the stormwater BMPs’ performances on

runoff volume reduction, assessed their performance on peak flow attenuation, and the

effectiveness of BMPs on runoff pollutant reduction in a suburban watershed. A four-

pronged analysis based on 1) RS-GIS based SCS-CN model developed; 2) EPA SWMM

5.1 for hydrologic and hydraulic simulation; 3) parameter estimation using the Bayesian

approach, and 4) predictive modeling through Artificial Neural Network.

An integrated study based on RS-GIS and SCS-CN model was conducted to

assess the suburban growth and its impacts on surface runoff over the study area in

South Kingstown, Rhode Island, USA. First, land-use pattern changes were detected

through remote sensing application using the supervised classification method on four

different Landsat images from 1994, 2004, 2014, and 2020. The analysis revealed 68.6%

urban land expansion from 1994 to 2020 in this suburban area for 36 years. Then surface

runoff estimated using a GIS-based SCS-CN model that showed an increasing spatial

pattern of 2% to 10% of surface runoff from 1994 to 2020. This study also told that

suburban growth began after 2000 in this area, and within 16 years, this land-use change

started to show its substantial impact on surface runoff.

Suburban areas are becoming equally important as urban areas to investigate

stormwater management. After ensuring the suburban growth and its impact on surface

194

runoff EPA SWMM5.1 is developed in simulating stormwater runoff quantity in this

study area. The conducted study investigated the comparative performance in terms of

runoff depth and peak flow of different low impact development (LID) structures under

various rainfall designs (1-year, 2-year, 5-year, 10-year, 25-year, 50-year, and 100-year

return periods). The study is carried out to test the performance of 4 different scenarios

(absence of LID, permeable pavement, bioretention, and vegetative) controlling runoff

depth and peak flow. The developed model calibration and validation were conducted

for three setups, i.e., run1, run 2, and run 3, in three different sub-catchments. The

respective NSE values of run1, 2, and 3 were 0.73, 0.59, and 0.64, with an average value

of 0.65. The average R2 value was 0.76. Environmental benefit also analyzed, and the

outcome suggests that LID structures (bioretention, permeable pavement, and

vegetative swale) are effective for suburban areas. In summary, the reduction rates of

LID facilities for different rainfall intensities per unit area (sq. km) were ranked as

follows: BR > PP > VS. Bioretention cell showed steady effectiveness in every rainfall

intensity even for the 100 years return period (31% ± 5%).

The water quality module of LID in the stormwater management model

(SWMM) was used through the RUNOFF block to develop the SWMM-LID model

using the buildup and wash-off method in this study. The developed SWMM-LID

applied to assess the effectiveness of the LID structures (bioretention, permeable

pavement, and vegetative swale) in controlling the runoff pollutants (total suspended

sediments, Nitrate-N, and Orthophosphate-P) based on 42 individual rainfall events

from 2016 – 2019 under various designs storms (1-year, 2-year, 5-year, 10-year, 25-

195

year, 50-year, and 100-year return periods). We used 30 rainfall events for model

calibration, and the rest of the 12 rainfall events were considered for model validation.

Moreover, Nash-Sutcliffe Efficiency (NSE) values of 0.73, and 0.69, R2 values of 0.75

and 0.64, and RSR values of 0.53 and 0.61 for runoff quantity module in two different

runs prove model efficiency. The selected LID structures were effective to removing

runoff pollutants up to 27% to 83% for the TSS, 11% to 28% for Nitrate-N, and 14% to

27% for Orthophosphate-P. Also, a detailed uncertainty of SWMM-LID model data

input and output analysis was conducted applying the Bayesian Markov Chain Monte

Carlo (MCMC) approach. Sensitivity analysis suggests that runoff in the study area is

sensitive to rainfall depth, impervious percentage, slope and manning coefficients,

conduit length, conduit roughness, storage depth, and combined interaction between

these parameters runoff and water quality (Total suspended solid, Nitrate-N, and

Orthophosphate-P). The prior knowledge of the parameter distribution plays a

significant impact in the model uncertainty structure, whereas parameters are considered

to follow uniform distributions in this study. Total predictive uncertainty for peak flow,

TSS, Nitrate-N, and Orthophosphate-P analysis with 95% confident interval captures

most of the observed data supporting the uncertainty quantification of the model.

The predictive model is developed using Back Propagation Neural Network (BPNN)

method for chloride from road salt in the study area. The innovation of the conducted

study were to develop an ANN model of the system trained using a small data set, to

obtain the best-fit models for predicting chloride concentrations using data from

monitoring sites, to evaluate the ANN model performance using 3 years (2016–2019)

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of observed data versus predicted data from the model, and to determine the accuracy

of the ANN model performance. The model also assesses the impact of road salt

applications by assessing a spatial density distribution focused on probable high

chloride concentration in an area. The conducted study suggests that ANN modeling off

environmental data has great potential in future work on improved prediction of chloride

and other pollutant concentrations and provides a helpful tool for the water resource and

environmental managers.

Scope of the work

Even though much progress has been made in the science of LID to understand

these practices' performance, there are still many aspects and challenges that must be

assessed and addressed to support widespread LID adoption. These needs are discussed

hereafter in more detail. They include, among others:

• There is a need for continued data collection to evaluate LID systems over different

spatial and temporal scales and climatic conditions.

• Need for assessing the removal of emerging and difficult-to-measure contaminants by

LID practices.

• Need to rank the performance of LID practices from lot scales to watershed and re-

gional scales.

• Development of easy-to-use decision support tools incorporating LID practices.

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CHAPTER 7

Supplementary

Data Presentation and Analysis

How effective are the bioswales in improving water quality? A case study of

a sub-urban catchment

An overview of field and laboratory analysis data along with statistical analysis.

Khurshid Jahan1,*, Soni M Pradhanang1,*, and Rebecca Brown2

1Department of Geosciences, University of Rhode Island, RI, Kingston, USA 2Department of Plant Science and Entomology, University of Rhode Island, Kingston,

RI, USA


(S.M.P)



198

Abstract

Best management practices (BMPs) are popular approaches to improve hydrology and

stormwater quality in urban areas. This study applied the BMPs such as bioretention

and vegetative filter strips to assess its performance in removing the non-point pollutants

of stormwater runoff from a suburban area. Sedimentation and filtration within the grass

layer are the primary mechanisms of pollutant treatment. The results compared the

reduction efficiency between control and treatment sites. The treatment sites were

treated with organic soil mix with native grass and Rhode Island Department of

Transportation (RIDOT) park mix. The entire analysis was done for 42 individual

rainfall events from 2016 to 2019 applying the ANOVA where three different variables

were considered (season, rainfall category, and the site type) for nitrate, ortho-phosphate,

and total suspended sediments. The study found that pollutants significantly responded

with seasons and differed in the control and treatment sites. Furthermore, the treatment

sites are capable of reducing pollutants. All three pollutants are consistently removed

during the post-treatment period. For the total suspended solids, the applied bioretention

and vegetative swale reduce from 20% to 90%, nitrate and nitrite reduced up to 60%,

and it also reduces ~75% ortho-phosphate. Statistical analysis also found that both

treatment mechanisms were equally effective for the selected area.

Keywords: Best Management Practices, Suburban, Analysis of Variance,

Effectiveness.

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1. Introduction

The US Environmental Protection Agency [1] considers non-point sources as

the leading threat to water quality in the Nation. Urban stormwater runoff is a non-point

pollution source and contributes to excess sediments and nutrients to the receiving water

[1-4]. Excess nutrients can interrupt the delicate balance of the aquatic ecosystem.

Nutrients come from the natural breakdown of human and animal wastes and fertilizers

applied to residential, agricultural, recreational, and commercial landscapes. Best

management practices are considered to counteract the non-point pollutant sources and

reduce their impact [5]. Roadside best management practices (BMPs) are techniques or

methods that aim to prevent or reduce the overall negative impacts of stormwater and

improve the quality of stormwater runoff cost-effectively [6]. These BMPs can be

characterized into three types based on their performance: (i) source control, (ii) flow

control, and (iii) runoff treatment [6]. A source control BMP is effective at preventing

and/or redirecting pollutants before entering the storm sewer system. It can be a

structural component of a planned site or a procedural BMP. Only a few permanent

source control BMPs (such as street sweeping, deicing, and spill control) can be

frequently used for a roadway. Therefore, source control BMPs are used more

commonly during construction and for the permanent portion of non-roadway projects

such as rest areas and park and ride lots. Stormwater flow control BMPs are designed

to control the flow rate or the amount of runoff leaving a site after development. The

primary mechanisms used to manage flow control include dispersion, infiltration, and

detention. Increased flows can cause downstream damage due to flooding, erosion, and

scour, and degradation of water quality and in-stream habitat because of the channel and

200

streambank erosion. Runoff treatment BMPs are designed to remove runoff pollutants

using various mechanisms, including sedimentation, filtration, plant uptake, ion

exchange, adsorption, precipitation, and bacterial decomposition. Different types of

structural BMPs such as infiltration, dispersion, bio-infiltration, wetpool, oil control

BMPs are widely applied as runoff treatment.

Structural stormwater treatment practices apply primarily in Rhode Island for

taking care of water quality, and it includes 1) wet vegetated treatment systems, 2)

infiltration practices, 3) filtering systems, 4) green roofs, and 5) open channel practices

[7]. An open channel practice (vegetative swale) is applied to this study. Vegetative

swales are usually shallow open channels with gentle side slopes, filled with erosion

and flood-resistant vegetation, designed to convey, control, and improve stormwater

through infiltration, sedimentation, and filtration [8,9]. Vegetative swales are mainly

used to slow runoff velocity and improve water quality. The use of vegetated filter strips

and swales in the highway and adjacent roadside environment has been extensively

studied in field trials in Texas [10,11], North Carolina [12], Kansas [13], Washington

[14], Maryland [15], and internationally [16]. Vegetated filter strips have been shown

to remove 50 to 98% of TSS, 23 to 50% of nitrate, and 33 to 80% of phosphate [15, 17],

while infiltration rates are lower than for bioretention BMPs, standard vegetated front

slopes, and swales are just as effective at treating stormwater [18].

Organic amendments of roadside soils combined with plantings of native

perennially grass noticeably improve the persistence of perennial vegetation by

201

increasing soil fertility and water retention [19, 20, 21] and enhancing stormwater

infiltration treatment. The benefits of the soil amendments are expected to last for years

after the initial application. The ideal soil organic matter mix has sufficient nitrogen and

phosphorus to support vegetation and sufficient carbon to support a healthy soil

microbial and invertebrate community [22]. In addition, many native kinds of grass have

deeper root systems than the turfgrass species typically seeded on roadsides [23]; such

deep-rooted species can anchor slopes and absorb nitrate and phosphate from deep

within the soil profile, preventing leaching to groundwater. Therefore, the proposed

BMP applied organic soil amendments and vegetative grasses to control the runoff

quality.

This study analyses the effectiveness of vegetative swales and bioretention for

the removal of stormwater pollutants. Vegetative swale effectively removes many

pollutants (nitrate and phosphate) from urban runoff depending on structural design and

composition [24 – 27]. The standard bioretention design and the soil mix constituents

and amounts are presented in Figure 1 that generally applied in Rhode Island for

stormwater management. Rhode Island faced nitrate, phosphate, and TSS problem

through the stormwater runoff. Effectiveness is measured based on the removal capacity

of nitrate, phosphate, and TSS. According to Hunt et al. (2006) [25], the phosphorus

removal efficiency increased with decreasing phosphorus content of the soil mixture,

and Dietz and Clausen (2005) [27] explained that the phosphorus removal efficiency

worked better after adding the soil mixture. Nitrogen removal is also effective through

the soil media of bioretention.

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2. Site Description

Two sites are considered as control sites, and four are vegetative swales BMP

treatment sites (Figure 1). The size of each site depends on the width of the bioretention

swales. Unlike the BMP sites, the control sites received no soil amendment (compost and

biosolids) and vegetated with the original grass mix. For a treatment site, approximately

3 lbs of nitrogen per 1000 sq. ft was used to prepare compost and biosolids. The carbon-

to-nitrogen ratio of this mix is about 12:1. The mixtures were added to the treatment sites.

After soil amendments have been placed, two kinds of grass mixtures were seeded; one

was the standard RIDOT grass mix, which includes Lolium Perenne, Festuca Rubra, and

Poa Pratensisand the other was a native grass mix containing Schizachyrum Scoparium,

Agrostis Capillaries, Eragrostis Spectabilis, and Festuca Rubra. Both RIDOT and native

grass mixes represent common grass species found in Rhode Island. The plots were

seeded by hand, followed by hydro-mulching to prevent erosion. The soil amendments

and vegetative strips worked together as a filter for the stormwater runoff. Total sus-

pended solids (TSS), Nitrate (NO3--), Nitrite (NO2-), Phosphate (PO43-), Chloride (Cl-),

Dissolved Organic Carbon (DOC), and Iron (Fe) were tested in the Hydro-systems and

Water Quality Laboratory at the URI. Instrumental analysis methods are provided in Ta-

ble 3.

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Figure 1: Roadside Best Management Practices Sites within the URI Campus

The results will compare the filtration effectiveness on the control versus the

treated sites over the three years. All data is statistically analyzed and visualized to

compare the stormwater runoff quality between control and treatment sites.

Table 1: Monitoring Strategies

Sites Location

ID

Activity Plan Sample collection

Treatment

Sites

Site 1 Sites are seeded

RIDOT mix and

native grass mix.

Stormwater runoff samples

were collected during

every storm event (>0.5

inches) from 2016 to 2019.

Site 3

Site 4

Site 6

Control

Sites

Site 2 Control sites

kept as it is. Site 5

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3. Materials and methods

3.1 Study Design

In this study, two control sites and four treatment sites were monitored during

the study period from August 2016 to July 2019. Each of these sites are equal length of

50ft and approximately equal width of ~8ft. Furthermore, the pre-treatment (calibration

period) started from August 2016, whereas the post-treatment started from June 2017.

The relationship between control and treatment sites during the treatment period

(sample collection period for control sites) is described by a simple linear regression

between the paired observation, taking the form:

𝑋𝑖 = 𝑏0 + 𝑏1(𝑌𝑖) + 𝑒

where 𝑋𝑖 and 𝑌𝑖 represent water quality concentration 𝑏0 and 𝑏1 are the regression

coefficients representing the regression intercept and slope, respectively, e is the

residual error. The significance of the connection between paired observations is tested

using the analysis of variance (ANOVA) between treatment and control sites. So, the

entire analysis is designed to evaluate sediments and nutrient removal capabilities. We

considered here three treatments:

a) Treatment 0: 2 control sites (Site 2 and Site 5)

b) Treatment 1: 2 treatment sites (added organic soil with native grass mix) (Site_1

and Site_4)

c) Treatment 2: 2 treatment sites (added organic soil with RIDOT park mix) (Site_3

and Site_6)

Site 1 to Site 4 is close to agricultural land and Sites 5 and 6 are situated close to the

parking lot (Figure 1).

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3.2 Rainfall pattern and size

This study is investigated using forty-two rainfall events from August 2016 to

July 2019. rainfall eventsThe actual concentration of a pollutant in stormwater runoff

varies with several factors land use, the time between runoff events, duration and

intensity of rainfall and runoff, characteristics of the pollutant, season, extent of exposed

soils, such as with construction sites, and extent of connected impervious surfaces [28 -

30]. Therefore, in this study, we considered rainfall intensity and season to assess the

stormwater pollutant characteristics.

Each of the rainfall events is categorized based on the rate of rainfall. According

to Table 2 and the characteristics of the rainfall events, we found four categories of

rainfall (light-moderate, moderate-heavy, heavy, and heavy-torrential).

Table 2: Types of rainfall based on rainfall intensity

Rainfall Categories Rate of the rainfall

Light rain 0 - 4 mm/day

Light-Moderate 4 – 16 mm/day

Moderate - Heavy rain 16 – 32 mm/day

Heavy rain 32 - 64 mm/day

Heavy- Torrential rain 64 – 128 mm/day

Torrential rain >128 mm/day

Source: Alpart et al., 2002 [31]

3.3 Monitoring Data Collection and Analysis

In this study, stormwater runoff samples were collected from 6 different sites

from 2016 – 2019, covering about 42 rainfall events. All those rainfall events were

selected based on rainfall—only those events were greater than 12.7 mm (0.5 inch).

From all the sites total of 6 stormwater runoff samples were taken during each event,

206

and pH, temperature, and turbidity analysis were conducted immediately after collecting

the samples. All samples were refrigerated (<40C) until testing for parameters including

seven anions (Fluoride, chloride, nitrite, nitrate, bromide, sulfate, and phosphate) and

six cations (lithium, sodium, ammonium, potassium, magnesium, and calcium) along

with total suspended sediments (TSS), Dissolved Organic Carbon (DOC), and Iron. The

water quality parameters, testing methods, and their relative analytical method detection

limits (MDL) are summarized in Table 3. All the analytical data were gathered to

conduct robust statistical analysis.

Table 3: Summary of the parameters and analytical method used including detection

limits(MDL)

Parameters Reference Method MDL*

(mg/l)

Determination of Turbidity Turbidity EPA method 180.1 -

Total Suspended Sediments TSS Calculation -

Dissolved Organic Carbon DOC NDIR detection method -

Nitrite NO2− Thermo Scientific™ Dionex™ AERS™ 500

Anion Electrolytically Regenerated Suppressor

(2 mm); P/N 082541

0.02

Nitrate N03- Thermo Scientific™ Dionex™ AERS™ 500


(2 mm); P/N 082541

0.02

Sulfate SO42- Thermo Scientific™ Dionex™ AERS™ 500


(2 mm); P/N 082541

0.05

207

Phosphate PO43- Thermo Scientific™ Dionex™ AERS™ 500


(2 mm); P/N 082541

0.005

Potassium K+ Thermo Scientific™ Dionex™ CERS™ 500

Cation Electrolytically Regenerated Suppressor

(2 mm): P/N 082543

0.01

Chloride Cl- Thermo Scientific™ Dionex™ AERS™ 500


(2 mm); P/N 082541

0.05

Iron (Total) Fe 1,10 phenanthroline method. USEPA 8008 0.02

Bromide Br- Thermo Scientific™ Dionex™ AERS™ 500


(2 mm); P/N 082541

0.05

Sodium Na+ Thermo Scientific™ Dionex™ CERS™ 500

Cation Electrolytically Regenerated Suppressor

(2 mm): P/N 082543

0.01

Fluoride F- Thermo Scientific™ Dionex™ AERS™ 500


(2 mm); P/N 082541

0.05

*MDL: Method detection limit collected from Western Washington Phase II Municipal

Stormwater Permit report [32].

3.4 Turbidity

Turbidity is a principal physical and optical property of water that usually

measure the clarity. It is known to be the quickest and cost-effective parameter to assess

the amount of eroded sediment. Generally, turbidity is measured in both nephelometric

208

turbidity units (NTUs) and Jackson turbidity units (JTUs). In this study, NTU are

applied here for turbidity measurement. It therefore can be an unintended indicator of

potential health risks associated with the outflow water from stormwater runoff [33].

Turbidity of the stormwater runoff is highly dependent on rainfall intensity and the

duration or the substrate materials [34]. In this study, turbidity was measured for six

different monitoring sites from 2016 – 2019 (Figure 2). The average range of the

turbidity is 9 NTU excluding 5 high range turbidity (60.7, 64, 184, 49, and 102 NTU).

Those 5 extreme values were represented in the figure in separately with value. Among

5 extreme values 3 values were measures in summer season and rest of the two values

found during the spring season. Turbidity showed a variation in different types of

rainfall intensity and season. It showed that the maximum Turbidity (NTU) in the

control site 5 in heavy rainfall. and minimum turbidity found for all sites during

moderate-heavy rainfall. It showed almost same turbidity in the heavy torrential because

of size of sample (sample size=2). On the other hand, maximum turbidity concentration

(3.5 mg/l) was detected during winter season for site 5.

Figure 2: An overview of Turbidity in the Six different monitoring sites

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3.5 Chloride

Chloride concentrations are represented in Figure 3 from six different

monitoring sites for three years (2016–2019). Seasonal variation of chloride

concentration is observed in this analysis and the higher concentration of chloride was

found during winter and early spring season in most of the sites having the higher

median than the other two season (summer and fall). According to the study, the highest

range chloride concentration (0.8-197.9) mg/l was measured from winter runoff samples.

A detail study about the chloride prediction applying artificial neural network (ANN)

[35] and its impact is presented in Chapter Five. In the figure, four extreme values were

represent separately including their values.

Figure 3: An overview of Chloride concentration in the Six different monitoring sites

In this study, a detail statistical analysis is conducted for Nitrate-N,

Orthophosphate-P, and total suspended sediments (TSS).

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3.5 Statistical Analysis

The significance of the relationship between paired observations was tested

using analysis of variance (ANOVA). Analysis of variance (ANOVA) is a statistical

degree analysis method that separates an observed cumulative variability into two sets,

i.e., systematic, and random [36, 37]. Usually, ANOVA test is applied to determine the

influence of independent variables on the dependent variables. A model was developed

to test the stormwater quality of the samples collected from the six different sites. The

test assumes that the regression residuals: usually are distributed, have equal variances

between treatments, and are independent [36, 37]. Statistical analyses are done in two

steps. For the first step, analysis of variance is applied and in the other step, stormwater

pollutant reduction rates are analyzed using the reduction rate (RR) equation. Statistical

analyses were applied using the R software. For comparing differences between samples

in two different types of sites (control and treatment) taken simultaneously, both one-

way and two-way ANOVA was applied in this study. One-way ANOVA helped to

assess the significance of each of the considered variables on the stormwater pollutants.

In contrast, two-way ANOVA allowed assessing the interaction between

continuous quantitative output and categorical variables and assessing the significant

impact of every experimental variable. Two-way ANOVA is used here for water quality

assessment on turbidity, TSS, DOC, chloride, nitrate-N, and orthophosphate-P, which

shows the difference of variation in both types of sites and explained the seasonal and

rainfall intensity impact on impact on the stormwater pollutant. The methodology of

one and two-way ANOVA are described below:

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3.5.1 One-way ANOVA

One-way ANOVA analyze and compare the means between the selected groups

to determine the statistical significance between them. The ANOVA produces an F-

statistic, the ratio of the variance calculated among the means to the variance within the

samples [36, 37].

F = variation between sample means/variation within the samples

F- critical (0.05) = 𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒 1

𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒 2

R2 = 1 − 𝑆𝑆𝐸

𝑆𝑆𝑇

R2 (adj) = 1 - 𝑀𝑆𝐸

𝑀𝑆𝑇

P-value = 2*P (TS≥|ts||Ho is true) = 2* (1-cdf(|ts|))

where SSE is the sum of square error, SST is the total sum of the square, MSE is mean

square error, MST is the mean square of the variable.

3.5.2 Two-way ANOVA

Two-way ANOVA is a statistical analysis method for a study with a continuous

quantitative consequence and two or more categorical independent variables [36, 37].

The usual expectations of normality, equal variance, and independent errors apply. A

two-way ANOVA was employed to examine whether temporal changes in stormwater

quality occurred over the study period. Post Hoc Least Significant Difference and

Multiple Comparison tests were again used to determine where the differences between

means were located. The first one-way ANOVA was applied to determine the statistical

differences between each season and the rainfall categories in this study. Furthermore,

in this study, a two-way ANOVA was employed to determine the temporal changes in

212

the individual control and treatment sites during the period of this study. Homogeneity

is also tested for the categorical variables before conducted a two-way ANOVA analysis.

Table 4: Analysis of variance for linear regression

Source Degrees of

Freedom

Sun of Squares Mean squares F

Regression

Residual

Total

1

n-2

n-1

(𝑆𝑥𝑦)2/𝑆𝑥2

𝑆𝑦2 − (𝑆𝑥𝑦)2/𝑆𝑥

2

𝑆𝑦2

(𝑆𝑥𝑦)2/𝑆𝑥2

𝑆𝑦𝑥2

[(𝑆𝑥𝑦)2/𝑆𝑥2]/ 𝑆𝑦𝑥

2

The value for Table 2 is calculated from

𝑆𝑦2 = ∑ 𝑌𝑖

2 − (∑ 𝑌𝑖)2

𝑛

𝑆𝑥2 = ∑ 𝑋𝑖

2 − (∑ 𝑋𝑖)

2

𝑛

𝑆𝑥𝑦 = ∑ 𝑋𝑖𝑌𝑖 − ∑ 𝑋𝑖 ∑ 𝑌𝑖

𝑛

𝑆𝑦𝑥2 =

𝑆𝑦2 − (𝑆𝑥𝑦)2)/𝑆𝑥

2

𝑛 − 2

Also, the regression and coefficient of determination are determined from:

𝑏1 = 𝑆𝑥𝑦

𝑆𝑥2

𝑏0 = 𝑌′ − 𝑏1𝑋′

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𝑟2 =(𝑆𝑥𝑦)2/𝑆𝑥

2

𝑆𝑦2

The resulting F statistic would indicate that the regression relationship adequately

explains a significant amount (p<0.001) of the variation in paired quality data.

Table 5: Categorical variables

Categorical Variables Parameters

Season Fall

Spring

Winter

Summer

Rainfall types based on rainfall intensity Light

Light-moderate

Moderate-Heavy

Heavy

Heavy-Torrential

Torrential

Site Types Treatment 0 (Control)

Treatment 1 (amended soil with new vegetation)

Treatment 2 (amended soil with RIDOT park

mix)

The null hypothesis that we considered for this study are described as follows:

• There is no difference in average TSS, nitrate and nitrite, and ortho-phosphate

concentration for any treatment type.

• There is no difference in average TSS, nitrate and nitrite, and ortho-phosphate

concentration for any rainfall type.

214

• There is no interaction effect between treatment type and rainfall type on the

average TSS, nitrate and nitrite, and ortho-phosphate concentration.

3.6 Reduction Rate

To assess the effectiveness of the roadside BMPs, we compared pollutant

concentrations between the control and treatment sites for every events. Due to shallow

relief, event mean concentration couldn’t be generated in this site. Flow data and

pollutant flux could not be achieved even after installed shallow v-notch weirs. Grab

sample data are available for each of the events for six different sites. We compared

treatment sites 1, 3, and 4 with the control site 2 as all these four sites are close to the

agricultural field. The other two sites, Site 5 (control) and Site 6 (treatment), are close

to the parking lot. Site 5 and site 6 are compared to each other. Pollutant concentration

reductions (in percent) were determined applying the following formula:

ΔC =( 𝑃𝑐−𝑃𝑡

𝑃𝑐) x 100

where ΔC= Pollutant concentration reduction (%), Pc = concentration of stormwater

runoff pollutant for control site, Pt = concentration of stormwater runoff pollutant for

the treatment site.


In the 2.1 section, we described the location of the site and the land types. Sites

1 to 4 are close to agricultural sites, and sites 5 and 6 are located at the edge of the

parking lot. So, here we compared the reduction rate and the average concentrations

215

based on land types. Control sites 2 are compared for treatment sites 1, 3, and 4. And

sites 5 and 6 are compared to each other due to having the same land types.

5.1 Nitrate-N

5.1.1 analysis on one-way and two-way ANOVA

Nitrate-N concentration was compared with control and treatment sites and

efficacy of each site were evaluated. The control sites' nitrogen composition closely

matched the parallel studies of urban runoff [33-35], with nitrate representing 45% of

the total nitrogen concentration and the rest of the remaining nitrite making up the

average. Therefore, nitrate was measured as an indicator of the nutrient content of the

runoff. Nitrate concentrations showed a variation in different types of rainfall (Figure

4), where it showed that the nitrate concentration was maximum (15.7 mg/l) in the

control site 2 for heavy rainfall (Table 2) and minimum nitrate concentration is detected

in the site 5 and 6 (parking lot sites) for moderate-heavy and heavy rainfall. Figure 5

shows seasonal differences for site 2-4. The maximum nitrate concentration (15.7 mg/l)

is detected during the spring season for site 2 followed by site 3 (13.5 mg/l) and 4 (11.3

mg/l). The fall and summer seasons showed a similar nitrate concentration for site 1 to

site 4. The lowest median observed in the winter season.

216

Figure 4: Statistical parameter of the different types of rainfall

Figure 5: Statistical parameter of the different types of seasons

To assess the overall interaction between three variables, a two-way model was

developed for this study, and AIC (1139.9) showed the lowest value for the treatment

type, rainfall and seasonal variables interaction (Table 6. The model revealed that for

this area and the analysis is more interactive with season and treatment rather than

rainfall category. The reason for having lower interaction on rainfall category is the low

elevation and almost flat slope of the study area. The residual and Normal Q-Q plots

(Figure 6) showed the efficiency of the model.

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Table 6: Model selection-based AIC for Nitrate-N, Orthophosphate-P and TSS

Model Nitrate-N Orthophosphate-P TSS

A 1139.9 914.7 293.3

B 1141.3 921.5 296.1

C 1145.8 922.8 299.5

D 1157.9 930.6 300.7

Model A: Treatment type + Rainfall + Season

Model B: Treatment Type + Rainfall

Model C: Treatment Type + Season

Model D: Rainfall+ Season

The diagnostic plots (Figure 6) showed the variance (residuals) across the range

of the observed data. The red lines represent the mean of the residuals and since it lies

closer to the zero lines horizontally means no significant outliers and no high difference

in variance. In this figure, the mean of the residual lied horizontally on the zero lines.

The normal Q-Q showed an inverted s shape. Approximately from the values (-3, -2.2),

the sample grows lower than the standard normal distribution; therefore, it takes longer

for sample quantiles to increase. From the values (-2.2, 2), the sample seems to grow at

approximately the same pace as the standard normal distribution.

218

Figure 6: Residuals and Normal Q-Q plot of the developed model

There was a statistically significant difference between the NO3-N level of

control and treatment runoff samples as indicated by two-way ANOVA (F(3, 50)=15.07,

p < 0.01). Significant variance was observed among the treatments with F(3, 59)=11.11,

p<0.01 with control for rainfall intensity and season. A post-hoc multiple comparison

test method was applied, and it also showed significant differences (p<0.01) among the

nitrate levels of intensive treatment compared to the control site.

The study found a significant response of nitrate for treatment variables

(treatment 0, treatment 1, and treatment 2) (Figure 7). Figure 7 showed the mean of

three different treatment and the result are presented in the table 7

Table 7: Nitrate-N response on treatment type

Treatment Type Group No of the Sites Mean (mg/l)

Treatment 0 A Site_2 5.43

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B Site_5 1.89

Treatment 1 C Site_1 4.21

D Site_4 4.22

Treatment 2 E Site_3 4.43

F Site_6 1.4

Figure 7: Nitrate and Nitrite response to treatment types in each of the sites

Due to a significant interaction between treatment 2 and treatment 0 (P< 0.05),

it was clearly proved that the treatment 2 is the more effective in reducing nitrate

concentration. The interaction between treatment 1 and treatment 0 (P > 0.05) found

treatment site 1 less effective than treatment 2. However, the mean of the two treatment

sites (Table 6) (treatment 1 and treatment 2) is very close. So, the very close variance

of treatments 1 and 2 proved that they both are equally effective.

220

5.1.2 Reduction rate of the Nitrate-N

As shown in figure 9, the pre-treatment and post-treatment periods are separated

with a line. The first 9 rainfall events were under the pre-treatment period for all sites,

and the rest of the 33 events were under the post-treatment. Reduction of nitrate is

represented in figure 8 where it showed the overall reduction between control and

treatment sites (Site 2 (control) vs. Site 1 (treatment). Out of total 42 rainfall events,

NO3-N reduction was seen for 29 rainfall events, and the range of reduction (%) was (0

to 55)% whereas for the site 3 NO3-N reduction was seen for 34 rainfall events, and the

range was (0 to 52)%. For site 4, reduction range was up to 60% for 37 rainfall events.

Considering the other sites, site 5 and site 6 had a lower amount of nitrate concentration

than the other 4 sites. However, the comparison between site 5 and site 6 showed that a

significant amount of nitrate was removed (average reduction rate 45%) through the

implemented organic soil layer.

For most of the storm events, especially for the treatment sites with a small

number of events showing high export of nitrogen, mainly during the summer season.

The transport of NO3-N during summer rainfall events is likely due to the organic nature

of the swales, perhaps tied to unnecessary sources of nutrients or other organic debris

[36]. This study also showed Nitrate-N removal through the soil media, and the

comparison reflected the positive influence of the BMPs for this area.

222

Figure 8: Comparison of Nitrate-N concentration reduction between control and

treatment site. Blue colored symbol represented the reduction (%), and the red color

symbol represented the negative reduction for the treatment site. The dot lines separate

Pre-treatment and post-treatment periods.

5.2 Orthophosphate-P (PO4-P)

Orthophosphate-P is one of the primary nutrients for the metabolic responses in

plants and animals. However, as soon as it arrives on surface, the groundwater or a

wetland making it provisionally inaccessible to active organisms by developing bonds

to soil particles [37].

5.2.1 analysis on one-way and two-way ANOVA

Orthophosphate-P (PO4-P) concentrations showed a variation for different types

of rainfall (Figure 9) intensity. The PO4-P concentration was maximum (8.4 mg/l) in the

control site 2 for heavy and moderate-heavy (8.3 mg/l) rainfall, and minimum

orthophosphate-P concentration is detected in the site 5 and 6 (parking lot sites) as well

for moderate-heavy and heavy rainfall. On the other hand, figure 10 showed that

223

maximum PO4-P concentration (8.4 mg/l and 8.3 mg/l) was detected during the fall and

winter season for site 2. Figure 9 also showed a variation in different types of rainfall

intensity, where it showed that the phosphate concentration was maximum (8.4 mg/l) in

the control site 2 for heavy rainfall and moderate-heavy (8.3 mg/l) and minimum

phosphate concentration is detected in the site 5 and 6 (parking lot sites) as well for

moderate-heavy and heavy rainfall. On the other hand, figure 11 described that

maximum phosphate concentration (8.4 mg/l and 8.3 mg/l) was detected during the fall

and winter season for site 2.


224

Figure 10: Statistical parameter of the different types of seasons

The results of the two-way ANOVA were F(5,21) = 8.4, p < 0.05, which showed

significant differences among the phosphate values. A two-way ANOVA was employed

to understand the effects of rainfall intensity and season on orthophosphate-P variation.

The results found a significant main effect of heavy rainfall (F(1.02, 8.5) = 8.3, p <

0.05), and a significant main effect of season (F(4, 9) = Fall, p b< 0.05, ηp 2 = 0.44).

The post-hoc multiple comparison test disclosed significant differences (pb<0.05)

between control and treatment orthophosphate-P concentrations. Furthermore, the PO4-

P concentrations were higher in the control sites than in treatment sites. Additionally,

lower phosphate concentration was observed in the parking lot sites rather than those

close to the agricultural field.

To assess the overall interaction between three variables, a two-way model was

conducted for this study, and AIC (914.7) showed the lowest value for the treatment and

seasonal variables interaction like nitrate and nitrite (Table 6). The model revealed that

225

for this area and the analysis is more interactive with season and treatment rather than

rainfall category. The reason for having lower interaction on rainfall category is the low

elevation and almost flat slope of the study area. The residual and Normal Q-Q plots

(Figure 11) showed the efficiency of the model.


of the observed data. The red lines represented the mean of the residuals. The more it

lies on the zero lines horizontally means no significant outliers and no high difference

on variance. In this figure, the mean of the residual lied horizontally on the zero lines.

The Normal Q-Q showed an inverted s shape. Approximately from the values (-3, -1.8),


for sample quantiles to increase. From the values (-1.8, 2.3), the sample seems to grow

at approximately the same pace as the standard normal distribution.


226

The study found a significant response of orthophosphate-P for treatment variables

(treatment 0, treatment 1, and treatment 2) are shown in the figure 12 and the result of

the mean of each treatment types are represented in the Table 8.

Table 8: Orthophosphate-P response on treatment type



B Site_5 1.69


D Site_4 2.59


F Site_6 1.32

Figure 12: Orthophosphate-P response to treatment types in each of the sites

Due to a significant interaction between treatment 2 and treatment 0 (P< 0.05) it

was clearly proved that the treatment 2 is the more affective in terms of reducing nitrate


227




5.2.2 Reduction rate of the Orthophosphate-P (PO4-P)

The reduction of PO4-P concentration is presented in figure 13 where it showed

the overall reduction between control and treatment sites (Site 2 (control) vs Site 1

(treatment) A removal was observed here. Out of the total 42 rainfall events, PO4-P

concentration decreased for 35 rainfall events, and the range of reduction (%) was (0 to

80) % whereas for the site 3 removal range of PO4-P concentration was rainfall events

(0 to 78)% considering 30 rainfall events. For site 4, the removal rate was up to 80% for

32 rainfall events, Considering the other sites, site 5 and site 6 had a lower amount of

PO4-P concentration than the other four sites. However, the comparison between site 5

and site 6 showed 28 rainfall events removal through the implemented organic soil

media.

228

Figure 13: Comparison of Orthophosphate-P concentration reduction between control

and treatment site. Blue colored symbol represented the reduction (%), and the red color

symbol represented the negative reduction for the treatment site.

229

5.3 Total Suspended Sediments (TSS)

Total suspended sediments (TSS) are defined as the particles in water that will

not pass through a 0.45-μm filter.) The TSS include clay and silt particles, fine organic

debris, and other particulate matters [38]. Elevated concentrations of sediments affect

the clarity of the water [38]. Higher concentrations result in less light passing through

water, which reduces the photosynthesis of aquatic plants and can lead to rapid heating

of water that might adversely affect the aquatic life that has been adapted to a lower

temperature [38]. In addition, suspended sediments can serve as carriers of toxins such

as pesticides that readily cling to the particles’ surfaces [38]. In the following sections,

one and two-way analysis of various for TSS are presented.

5.2.1 Analysis of One-way and Two-way ANOVA

TSS showed a variation in different types of rainfall (Figure 14) intensity and

season (Figure 15). This results however is different from that is NO3-N and PO4-P. It

showed that the maximum TSS (3.5 mg/l) in the control site 5 in heavy rainfall and

moderate-heavy (2.5 mg/l) and minimum TSS concentration is detected in site 1 through

site 4 during heavy and moderate-heavy rainfall. Figure 16 also shows that maximum

TSS concentration (3.5 mg/l) was seen during the winter season for site 5.

230


Overall, the TSS range in all six sites was low (0.074 – 3.5) mg/l. The results of

the two-way ANOVA were F(3,12) = 3.5, p < 0.05, which showed significant

differences among the TSS values among the sites. A two-way ANOVA was employed

to understand the effects of rainfall intensity and season on TSS variation. The results

showed that there was a significant main effect of heavy rainfall (F(2.4, 1.5) = 3.5, p <

0.05) and a significant main effect of season (F(3, 1.1) = winter, p< 0.05, ηp 2 = 0.14).

The post-hoc multiple comparison test disclosed significant differences (p<0.05)

between control and treatment TSS concentrations.

To assess the overall interaction between three variables, two-way model

conducted for this study and AIC (293) (Table 6) showed the lowest value for treatment

and seasonal variables interaction. The model revealed that for this area and the analysis

is more interactive with season and treatment rather rainfall category. The reason for

having lower interaction on rainfall category is the low elevation and almost flat slope

231

of the study area. The residual and Normal Q-Q plot (Figure 16) showed the efficiency

of the model.

Figure15: Statistical parameter of the different types of seasons


of the observed data. The red lines represented the mean of the residuals. The more it

lies on the zero lines horizontally means no significant outliers and no high difference

on variance. In this figure, the mean of the residual lied horizontally on the zero lines.

The Normal Q-Q showed an inverted s shape. Approximately from the values (-3, -2.4),


for sample quantiles to increase. From the values (-2.4, 1.9) the sample seems to grow

at approximately the same pace as the standard normal distribution.

232


The study found a significant response of ortho-phosphate for treatment

variables (treatment 0, treatment 1, and treatment 2) are shown in the figure 17 and the

result of the mean of each treatment types are represented in the Table 9.

Table 9: TSS response on treatment type



B Site_5 1.02


D Site_4 0.507


F Site_6 0.550

233

Figure 17: TSS response to treatment types in each of the sites

Due to a significant interaction between treatment 2 and treatment 0 (P< 0.05) it

was clearly proved that the treatment 2 is the more affective in terms of reducing nitrate





5.2.2 Reduction rate of the Total Suspended Sediments (TSS)

Reduction of TSS is represented in figure 18 where it showed the overall

reduction between control and treatment sites (Site 2 (control) vs Site 1 (treatment).

234

Again, result showed that TSS also reduced through the treatment sites was observed

very clearly from this analysis.

235

Figure 18: Comparison of TSS concentration reduction between control and treatment


represented the negative reduction for the treatment site.

Out of the total 42 rainfall events, TSS reduction was seen for 37 rainfall events,

and the range of reduction (%) was (0 to 100)% in site 1, whereas for the site 3 TSS

reduction for 31 rainfall events ranged from 0% to 988% For site 4, PO4-P concentration

reduction was calculated. For 34 storms TSS reduction range was up to almost 95%.

Site 6 also showed TSS reduction for 33 rainfall events for the implemented soil media,

and the removal range was from (0 to 94)%.

6. Conclusion

The roadside best management practices (BMPs) performance is evaluated to

reduce or improve the quality of stormwater runoff. The grass swales are evaluated as a

simple and effective stormwater control measure for this suburban pollutant treatment.

The grass swales significantly reduced pollutant and mean concentrations for nitrate and

236

nitrite, ortho-phosphate, and TSS. Nutrient treatment was variable and effective for

most rainfall events, and there is a seasonal effect and usually occurring during the

summer months. The grass swales consistently remove all three nutrients except for a

few numbers of rainfall events. Generally, Nitrate-N, orthophosphate-P, and total

suspended sediments are the common pollutant problem for Rhode Island areas from

the stormwater runoff. In addition, non-point source (NPS) pollution is a significant

management concern in Rhode Island’s waters – both surface waters and groundwaters.

The performance of the roadside best management practices is evaluated in this

study. The grass swales significantly reduced pollutant concentration for nitrate,

phosphate, and total suspended sediments. Statistical analysis showed that these soil

media are capable of reducing about (20 – 45)% of Nitrate-N, (20-60)% of

Orthophosphate-P, and (20-90)% of TSS through implemented organic soil media.

This research suggests that the grass swales generally improve the suburban

runoff water quality and can be employed to treat non-point source pollution.

Therefore, they should be considered to reduce the environmental impacts due to

stormwater runoff.

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240

Appendix

Table: Statistical Parameters value for Nitrate-N of Rainfall category [ Instrument: Ion

Chromatography, Standard: DionexTM combined seven Anion standard, Standard

concentration unit: mg/l, Method: EPA 300.1 (A), Detection Limit: 0.02mg/l] Types of

Rainfall

Paramet

er

Site_1

(Treatme

nt)

Site_2

(Control)

Site_3

(Treatment

)

Site_4

(Treatment)

Site_5

(Control)

Site_6

(Treatment)

Moderate-

Heavy

Min 2 2.1 1.5 1.4 0.54 0.3

Median 4.55 5.2 4.7 4 1.8 1.25

Max 6.1 8.1 6.1 6.8 3.45 2.6

Light-Moderate Min 2.6 3.6 3.18 3.3 0.6 0.9

Median 5.4 5.05 4 4.3 1.9 1.3

Max 7.1 7.5 7.9 4.8 3.1 2.6

Heavy Min 2.1 2.3 1.3 1.4 0.4 0.23

Median 3.7 5.6 3.2 4.1 1.8 1.3

Max 7.6 15.7 13.5 11.3 4.1 2.1

Heavy-

Torrential

Min 3.6 5.4 4.7 5.2 2.2 1.7

Median 5.4 6.1 5.2 5.7 2.5 1.7

Max 7.3 6.8 5.7 6.3 2.9 1.7

241

Table: Statistical Parameters value for Nitrate-N of Rainfall category [Instrument: Ion


concentration unit: mg/l, Method: EPA 300.1 (A), Detection Limit: 0.02mg/l] Season Param

eter

Site_1

(Treatm

ent)

Site_2

(Control)

Site_3

(Treatme

nt)

Site_4

(Treatmen

t)

Site_5

(Control)

Site_6

(Treatmen

t)

Fall Min 2 2.3 1.3 1.4 0.4 0.23

Median 3.6 5 3.7 4.1 1.4 0.9

Max 6.2 7.5 7.9 6.3 4.1 2.6

Spring Min 2.1 2.7 2.2 2.8 1.8 1.2

Median 4.4 6.7 5.8 4.7 2.2 1.4

Max 6.1 15.7 13.5 11.3 2.9 2.1

Summer Min 3.1 4.4 2.4 3.3 1.1 0.6

Median 5.4 7.2 4.7 5.2 1.7 1.4

Max 7.3 8.4 6.3 6.8 2.9 2.1

Winter Min 2.1 2.1 1.5 1.4 0.4 0.3

Median 4.1 4.7 3.2 3.8 1.9 1.4

Max 7.6 8.2 11.1 6.2 3.7 2.6

242

Table: Statistical Parameters value for Orthophosphate-P of Rainfall category

[ Instrument: Ion Chromatography, Standard: DionexTM combined seven Anion

standard, Standard concentration unit: mg/l, Method: EPA 300.1 (A), Detection Limit:

0.005 mg/l] Types of

Rainfall

Param

eter

Site_1

(Treatm

ent)

Site_2

(Control)

Site_3

(Treatme

nt)

Site_4

(Treatmen

t)

Site_5

(Control)

Site_6

(Treatmen

t)

Moderate-

Heavy

Min 0.5 0.98 1.2 0.8 0.2 0.2

Median 2.05 4.64 2.8 2.25 2.5 1.3

Max 3.6 8.3 4.4 3.7 4.8 2.4

Light-

Moderate

Min 1.8 1.93 1.9 1.1 0.8 0.4

Median 2.5 4.365 2.7 2.15 1.55 1.1

Max 3.2 6.8 3.5 3.2 2.3 1.8

Heavy Min 1.0 1.8 0.8 0.9 0.1 0.1

Median 2.5 4.5 2.5 2.6 1.5 1.3

Max 7.8 8.4 5.5 5.7 3.0 2.5

Heavy-

Torrential

Min 3.1 3.9 1.2 2.1 1.4 1.4

Median 3.1 4.05 3.25 3.1 2.55 1.75

Max 3.1 4.2 5.3 4.1 3.7 2.1

243

Table: Statistical Parameters value for Orthophosphate of Season [ Instrument: Ion


concentration unit: mg/l, Method: EPA 300.1 (A), Detection Limit: 0.005 mg/l] Season Param

eter

Site_1

(Treatm

ent)

Site_2

(Control)

Site_3

(Treatme

nt)

Site_4

(Treatmen

t)

Site_5

(Control)

Site_6

(Treatmen

t)

Fall Min 0.98 1.9 1.08 0.78 0.23 0.21

Median 2.1 4.1 2.8 2.6 1.32 0.98

Max 4.2 8.3 4.9 4.9 3.1 2.12

Spring Min 2.1 2.9 1.3 1.8 0.9 0.6

Median 4.5 4.5 2.1 2.3 1.8 2.1

Max 7.8 6.3 2.7 3.6 2.1 2.5

Summer Min 2.1 3.1 1.7 1.4 1.12 1.2

Median 3.1 4.2 3.2 2.1 2.8 1.5

Max 3.8 5.98 5.3 4.2 4.8 2.4

Winter Min 0.5 1.0 0.8 0.9 0.1 0.1

Median 2.5 3.1 2.5 2.1 1.6 1.3

Max 4.3 8.4 5.5 5.7 2.4 2.4

244

Table: Statistical Parameters value for TSS of Rainfall category [Instrument: Filter

paper, oven, weight scale, Method: 2540D] Types of

Rainfall

Parame

ter

Site_1

(Treatme

nt)

Site_2

(Control)

Site_3

(Treatmen

t)

Site_4

(Treatment

)

Site_5

(Control)

Site_6

(Treatment

)

Moderate-

Heavy

Min 0.06 0.10 0.02 0.06 0.09 0.10

Median 0.53 0.87 0.62 0.46 1.00 0.30

Max 1.10 1.80 1.10 1.60 2.50 1.40

Light-Moderate Min 0.11 0.06 0.07 0.08 0.43 0.50

Median 0.70 0.89 0.47 0.54 0.98 0.65

Max 1.10 0.98 1.02 0.78 1.90 1.50

Heavy Min 0.07 0.12 0.01 0.03 0.12 0.19

Median 0.40 0.98 0.76 0.50 0.98 0.70

Max 0.96 1.60 1.40 1.10 3.50 1.40

Heavy-

Torrential

Min 0.34 1.08 0.76 1.08 0.91 0.2

Median 0.57 1.19 0.98 1.24 1.205 0.65

Max 0.8 1.3 1.2 1.4 1.5 1.1

245

Table: Statistical Parameters value for TSS of Rainfall category [Instrument: Filter

paper, oven, weight scale, Method: 2540D] Season Param

eter

Site_1

(Treatm

ent)

Site_2

(Control)

Site_3

(Treatme

nt)

Site_4

(Treatme

nt)

Site_5

(Control

)

Site_6

(Treatme

nt)

Fall Min 0.07 0.118 0.007 0.028 0.09 0.2

Median 0.56 0.9 0.67 0.65 0.54 0.7

Max 1.1 1.7 1.2 1.4 2.1 1.5

Spring Min 0.074 0.6 0.015 0.102 0.54 0.1

Median 0.26 0.94 0.62 0.45 1.09 0.496

Max 0.7 1.4 0.76 1.2 2.4 1.1

Summer Min 0.06 0.10 0.12 0.13 0.81 0.10

Median 0.45 1.02 0.72 0.89 1.31 0.45

Max 0.80 1.80 1.10 1.60 2.50 0.80

Winter Min 0.11 0.06 0.021 0.08 0.69 0.2

Median 0.45 0.9 0.78 0.4 1.1 0.56

Max 1.1 1.6 1.1 1.1 3.5 1.4

EFFECTIVENESS OF ROADSIDE BEST MANAGEMENT PRACTICES …

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