Using Multifunctional Green Infrastructure to Address Resilience in Cities

206 Pages·2017·5.54 MB·English

by  Franco Montalto

Using Multifunctional Green Infrastructure to Address Resilience in Cities

A Thesis
Submitted to the Faculty
of
Drexel University
by
Stephanie Marie Miller
in partial fulfillment of the
requirements for the degree
of
Doctor of Philosophy
August 2017
© Copyright 2017

Stephanie M. Miller. All Rights Reserved.
ii

Table of Contents
LIST OF TABLES iv
LIST OF FIGURES vii
ABSTRACT ix
INTRODUCTION 1
1. A PRELIMINARY ASSESSMENT OF THE ROLE OF GREEN
INFRASTRUCTURE DURING HURRICANE SANDY: A CASE STUDY OF THREE
COASTAL COMMUNITIES 6
1.1 Introduction 7
1.2 Materials and Methods 10
1.3 Results 20
1.4 Discussion 25
1.5 Conclusions 37
2. STAKEHOLDER PERCEPTIONS OF GREEN INFRASTRUCTURE AND
ECOSYSTEM SERVICES IN NEW YORK CITY 41
2.1 Introduction 42
2.2 Methods 49
2.3 Results 56
2.4 Discussion 67
2.5 Conclusions 73
3. THE LONG-TERM ECONOMIC AND SOCIAL IMPACT OF GREEN
INFRASTRUCTURE IN NEW YORK CITY 78
3.1 Introduction 79
3.2 Methods 82
3.3 Results 100
3.4 Discussion 113
3.5 Conclusions 119
CONCLUSION 121
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LIST OF REFERENCES 125
APPENDIX 1: SEMI-STRUCTURED INTERVIEWS 140
APPENDIX 2: DETERMINING THE NUMBER OF SIMULATIONS 144
APPENDIX 3: MODEL VALIDATION 147
APPENDIX 4: MAXIMUM BUILD OUT 149
APPENDIX 5: ODD PROTOCOL 151
VITA 191
iv

List of Tables

1.1. Summary of datasets used in analytical and statistical analysis. 12
1.2. Description of CAP damage categories. 13
1.3. Description of the variables in models 1 & 2. 15
1.4. Comparison of Model 1 and Model 2 for the Coney Island, Rockaway, and South Shore
study sites. 21
1.5. Results of the Coney Island logistic regression (Model 2). Table includes mean and standard
deviation for each variable, beta values, variance inflation factors (VIF), significance, and how a
one-unit increase in each variable changes the odds of being damaged. 22
1.6. Results of the Rockaways logistic regression (Model 2). Table includes mean and standard
deviation for each variable, beta values, variance inflation factors (VIF), significance, and how a
one-unit increase in each variable changes the odds of being damaged. 23
1.7. Results of the South Shore logistic regression (Model 2). Table includes mean and standard
deviation for each variable, beta values, variance inflation factors (VIF), significance, and how a
one-unit increase in each variable changes the odds of being damaged. 24
2.1. List of ecosystem services considered in this research (right most column), adapted from
Costanza et al. (1997), de Groot et al. (2002), MA (2005), TEEB (2011), and Gomez-Baggethun
and Barton (2013). 45
2.2. Description of GI and GI practices used in this research. 50
2.3. Numbered list of ecosystem services included in the structured survey, as well as the
description of each service that was provided to survey takers. 51
2.4. Matrix declaring which ES specific GI types are likely able to provide (Y), likely unable to
provide (N), and where provision is uncertain or dependent on design characteristics (M). Y, N,
and M classifications are based on previous studies (Bolund and Hunhammar 1999, Oberndorfer
et al. 2007, COP 2008, CNT 2010, Wise et al. 2010, Lundy and Wade 2011, Burkhard et al. 2012,
Cameron et al. 2012, Farrugia et al. 2013, Lovell and Taylor 2013, Christin et al. 2014,
Kopperoinen et al. 2014, Coutts and Hahn 2015, USDA 2016) and the researchers’ own
experiences. 55
2.5. Demographic responses for practitioners (Group 1) and all three resident groups (Groups 2 -
4). 57
2.6. Survey responses to questions about prior familiarity with GI. 58
2.7. Average value of ES for each GI type expressed by the four survey response groups. 61
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2.8. Survey responses to question: Which types of green infrastructure would you like to see more
of in your neighborhood? The “Other” option included an opportunity to respondents to write-in
their own answer, responses not shown. 62
3.1. Impervious Surface Targets for Study Area (NYC DEP 2016d). 83
3.2. Description of agent classes included in model. Physical limitations of each agent class are
also listed, based on observed implementation challenges between 2010 and 2016 and interviews
with practitioners. 88
3.3. Description of agent classes included in model. Institutional limitations of each agent class
are also listed, based on observed implementation challenges between 2010 and 2016 and
interviews with practitioners. 89
3.4. Cost for implementing the GI typologies included in this model. 93
3.5. Public Value Coefficients (PVC) determined through surveys with GI practitioners and
residents of NYC (Miller and Montalto in preparation). 95
3.6. Annual carbon storage and pollutant removal rates for each GI type. 96
3.7. Annual economic value for the five co-benefits considered in this model. 97
3.8. Average acres greened and hydraulic loading ratio for six GI typologies included in this
model (trees are excluded from this table). 104
3.9. Average number of each GI type built under the two modeling scenarios. 105
3.10. Number of Happy People (HP) by scenario. 107
3.11. Economic value of GI’s co-benefits by scenario. 109
3.12. Comparing the economic value of the different GI types between Model 1 and
Model 2. 112
A1.1. Semi-structured interview responses to the question “What ecosystem services or co-
benefits that GI can provide do you feel are the most important?” 143
A5.1. Impervious Surface Targets for Study Area (NYC DEP 2016d). 153
A5.2. Description of agent classes included in model. Physical limitations of each agent class are
also listed, based on observed implementation challenges between 2010 and 2016 and interviews
with practitioners. 157
A5.3. Description of agent classes included in model. Institutional limitations of each agent class
are also listed, based on observed implementation challenges between 2010 and 2016 and
interviews with practitioners. 158
A5.4. Variables all agents use. 160
A5.5. Variables shared by all lots (Schools, Public Housing, Public Properties, Parkland &
Playgrounds, and Private Properties agents) used in this model. 160
A5.6. Street / ROW-specific variables used in this model. 161
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A5.7. Census-block specific variables used in this model. 161
A5.8. Global variables used in this model. 162
A5.9. Global lists used in Model 1 and Model 2. 162
A5.10. Initial values for all agent variables. 169
A5.11. Initial conditions for all agents. 170
A5.12. Initial conditions for all global variables. 171
A5.13. Annual carbon storage and pollutant removal rates for each GI type included in this
model. 172
A5.14. Annual economic value for the five co-benefits considered in this model. 173
A5.15. Public Value Coefficients (PVC) determined through surveys with GI practitioners and
residents of NYC (Miller and Montalto in preparation). 174
vii

List of Figures

1.1. Location of the three study areas – (a) Coney Island, Brooklyn; (b) Rockaway Peninsula,
Queens; and (c) South Shore, Staten Island. 14
1.2. Location of all large-scale GI in the NYC. 16
1.3. Close-up of damaged buildings near to Marine Park and Shell Bank Creek in the Coney
Island study area. 26
1.4. Elevation map of the Coney Island study area. 28
1.5. Map of soil permeability for the Coney Island study area. 29
1.6. Comparison of elevation (a) and surge depth (b) on the Rockaway Peninsula during
Hurricane Sandy. 31
1.7. Map of soil permeability on Rockaway Peninsula. 34
1.8. Location of damaged and undamaged buildings around Great Kills Park on the South Shore
in Staten Island. 36
2.1. Structured survey results. Results demonstrate the percentage of practitioners (Group 1),
residents collected through the Citizen Scientists snowball effort (Group 2), residents collected in
person (Group 3), and residents collected through SurveyMoneky’s Audience tool (Group 4) who
value the ES from each GI type. Values range from 0 (red squares, which indicate no members of
that group found the ES / GI combination valuable) to 100 (blue squares, which indicate all
members of that group found the ES / GI combination valuable). 60
2.2. The values Group 2 (a), Group 3 (b), and Group 4 (c) respondents vs the values of Group 1
respondents (practitioners) assigned to each ES / GI combination. Graphs only show ES / GI
combinations that the literature suggests can manifest (“No” classifications according to
Table 4). 65
2.3. The values Group 2 (a), Group 3 (b), and Group 4 (c) respondents vs the values of Group 1
respondents (practitioners) assigned to each ES / GI combination. Graphs only show ES / GI
combinations that the literature suggests cannot manifest (“No” classifications according to
Table 4). 66
3.1. Location of the study area within the Bronx, NY and of the four watersheds modeled in this
research. 84
3.2. Schematic of model design. 86
3.3. Schedule of decision-making for Model 1. 90
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3.4. Schedule of decision-making for Model 2. 91
3.5. Total acres greened for each modeling scenario. The redline represents the target acres
greened for the study area (778 acres). 101
3.6. Acres greened predicted by the baseline and co-benefit scenarios for the a) Bronx River
Watershed, b) Hutchinson River Watershed, c) Westchester Creek Watershed, and d) East River /
Open Waters tributary areas. Grey bars represent the target acres greened for each watershed at
years 5 (1.5%), 10 (4%), 15 (7%), and 20 (10%), while black bars (a) represent the observed acres
greened. 102
3.7. Capital expenditures for each watershed for a) Model 1 and b) Mode 2l. Red Lines represent
target expenses. 106
3.8. Change in HP between Model 1 and Model 2. Census blocks in reds and oranges represents
areas in the study area where fewer people were made happy under Model 2, while census blocks
in blue represent neighborhoods where more people were made happy under Model 2’s siting
rules. 108
3.9. Economic Value of GI and its co-benefits over time. 110
3.10. The economic value of GI under both the baseline and co-benefit scenario. Figure a) shows
the total value of each GI type after 20 years and b) shows the annual value at the end of the
simulation. GI types include B – bioswales, CG – community gardens,
PPave – permeable pavement, PPlay – permeable playgrounds, RG – rain gardens, GR – green
roofs, and T – trees. 111
A1.1. Summary of practitioner employment sectors. 141
A2.1. Cumulative Mean over 1,200 model runs of Model 1. 145
A2.2. Cumulative standard deviation and standard error of the mean (SEM) over 1,200 model
runs of Model 1. 145
A3.1. Boxplot of acres greened predicted in the Bronx River Watershed under Model 1 conditions
by year 6 of the GI program. The red line represents the observed number of acres greened
(26 acres). 148
A5.1. Location of the study area within the Bronx, NY and of the four watersheds modeled in this
research. 154
A5.2. Schematic of model design. 155
A5.3. Schedule of decision-making for Model 1. 165
A5.4. Schedule of decision-making for Model 4. 166
ix

Abstract
Using Multifunctional GI to Address Resilience in Cities
Stephanie Marie Miller

Cities are currently facing numerous challenges and uncertainties – climate
change, aging infrastructure, growing urbanization - with many turning towards green
infrastructure (GI) as a possible solution. Perhaps the most common application of GI is
for stormwater management, though some cities are also hoping to use GI to combat
climate change and urban heat islands, recharge groundwater supplies, and restore habitat
for native fish and wildlife. Though many municipalities are using GI, few have a
comprehensive vision for GI or consider all the possible benefits when planning, siting,
and designing new sites. As a result, efforts remain largely uncoordinated and disjointed,
with many missed opportunities. This is partly because the exact benefits of GI remain
largely unquantified. Without knowing what to expect, cities are hindered in their efforts
to integrate GI into broader goals and coordinate across programs. To address this need,
this dissertation adopts a mixed method approach to address some of the knowledge gaps
surrounding GI’s co-benefits. Specifically, statistical modeling, participatory
engagement, and complex, multi-agent modeling are used to more clearly define and
measure the ES available from various GI sites. For all efforts, New York City was used
as a case study, although the methods used here are applicable to all cities.