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Understanding The Consequences And Costs OfClimate Change On Texas PavementsMegha SharmaUniversity of Texas at El Paso, [email protected]

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UNDERSTANDING THE CONSEQUENCES AND COSTS OF CLIMATE CHANGE ON

TEXAS PAVEMENTS

MEGHA SHARMA

Doctoral Program in Civil Engineering

APPROVED:

Vivek Tandon, Ph.D., P.E., Chair

Soheil Nazarian, Ph.D., P.E.

Carlos Ferregut, Ph.D.

Vinod Kumar, Ph.D.

Somnath Mukhopadhyay, Ph.D.

Rajib B. Mallick, Ph.D.

Charles Ambler, Ph.D.

Dean of the Graduate School

Copyright ©

by

Megha Sharma

2018

Dedication

To my family

UNDERSTANDING THE CONSEQUENCES AND COSTS OF CLIMATE CHANGE ON

TEXAS PAVEMENTS

By

MEGHA SHARMA, B.Tech., M.Tech.

DISSERTATION

Presented to the Faculty of the Graduate School of

The University of Texas at El Paso

in Partial Fulfillment

of the Requirements

for the Degree of

DOCTOR OF PHILOSOPHY

Department of Civil Engineering

THE UNIVERSITY OF TEXAS AT EL PASO

August 2018

v

Acknowledgements

Over the past five years, I have received support and encouragement from the number of

individuals. First and foremost, I want to acknowledge my advisor Dr. Vivek Tandon for his

invaluable guidance, constant encouragement, supervision, and patience during my research.

This dissertation would not have been possible without his wise counsel, support and

constructive suggestions.

Beside my advisor, I extend my gratitude to the members of my dissertation committee

Dr. Soheil Nazarian, Dr. Carlos Ferregut, Dr. Vinod Kumar, Dr. Rajib B. Mallick and Dr.

Somnath Mukhopadhyay for their expert guidance and invaluable suggestions for my

dissertation.

I want to express my gratitude and appreciation towards my parents Mahesh Sharma and

Rani Sharma for their inspiring attitude, patience, and endless affection. I also want to express

thankfulness for the affection and encouragement of my lovable sister Priya, brother Harshal and

all my family members.

I am very grateful to my friend Sundeep Inti for his constant support and help throughout

my dissertation. I also want to express my thankfulness to my friends Niharika Dayyala, Tripti

Pradhan, Anu Sachdev, and Swati Rawat. I extend my wholehearted thanks to the Department of

Civil Engineering, UTEP.

I take this opportunity to sincerely acknowledge the financial support for this project

received from the Texas Department of Transportation (TxDOT). I also want to extend my

gratitude to my friends and colleagues: Armando Esquivel, Anjuman Akhter, and Angel Rodarte.

vi

Abstract

Over the several centuries, the global temperatures have been rising, and the rate of rising

in temperature has increased significantly in the last century. The increase in temperature and

precipitation can substantially influence performance life of pavements because of their

continuous exposure to climate. The climate parameters like temperature and precipitation are

considered in the pavement design to reflect the influence of climate factors on the performance

of the pavements. Currently, designers use the historical weather patterns while designing

pavements. Since the climate is changing, the estimated service life of the pavement will be

reduced significantly if the historical data is used in designs. The primary purpose of this study

was to identify the influence of climate change on the service life of pavements and identify the

levels of loss in service life in the event of climate change. To understand how the climate

change will affect the pavement performance, a thorough review of information was conducted

to identify the current state of practice and research gaps. Based on the review, the impact of

climate change on the performance of pavements was performed using future climate models and

Pavement ME design software. The study used twelve future climate prediction models from

NARCCAP databases to develop an understanding of climatic factors like on the performance of

the pavement structures.

The pavement structure evaluation was performed (using Pavement Mechanistic-

Empirical (ME) Design software and future climate model predictions) to evaluate the influence

of climate on the performance of different pavement structures, mix types, and regional

variations, among others. The results of the impact were incorporated in SD Model for decision

makers. Various approaches were evaluated to mitigate the influence of climate change like

changing the thickness of the AC layer, using the high-quality material, etc. An economic

analysis approach was also developed to help decision-makers in selecting pavement designs that

can withstand and resist climate change.

vii

All the selected twelve climate model simulations showed different climate prediction,

and variations in performance of the pavement section and these projections vary geographically.

The distresses in the pavement sections increases for the future climate as compared to the

historical weather data (Pavement ME Climate) indicating premature failure of the pavement

structure. This changing climate adversely impacts the pavements functionality by reducing the

service life of pavements. The performance of the pavement section is influenced more by the

combined effects of climate change and extreme events than the individual event. The Pavement

ME analysis was also compared with FPS software design. The comparison evaluation suggested

that the Pavement ME Design method predicts less service life in comparison to FPS 21.

However, the FPS 21 doesn’t take into consideration change in climate, and the pavement

designed using FPS 21 may require maintenance earlier than anticipated. To withstand changing

climate adaptation methods were adopted. Either pavement needs to use high-performance

materials or enhance layer thicknesses or consider both during pavement design. These

adaptation strategies improve the performance of the pavement sections, thus, mitigating the

impacts of the changing climate. Finally, the cost and emission analysis show that early

consideration of future weather changes into design yields long-term benefits regarding savings

in user costs and emissions.

viii

Table of Contents

Acknowledgements ........................................................................................................................ v

Abstract ......................................................................................................................................... vi

Table of Contents ........................................................................................................................ viii

List of Tables ................................................................................................................................ xi

List of Figures ............................................................................................................................. xiv

Chapter 1 : Introduction ................................................................................................................. 1

1.1 Problem Statement ....................................................................................................... 1

1.2 Research Objectives ..................................................................................................... 2

1.3 Organization ................................................................................................................. 3

Chapter 2 : Literature Review ........................................................................................................ 5

2.1 Background .................................................................................................................. 5

2.2 Review of Literature .................................................................................................... 6

2.2.1 Climate Scenarios and Models............................................................................ 6

2.2.2 Pavement Design Software’s ............................................................................ 10

2.2.3 Developed Frameworks .................................................................................... 12

2.2.4 Climate Adaptation Studies .............................................................................. 13

2.2.5 System Dynamics.............................................................................................. 19

2.2.6 Relevant Climate Change Research .................................................................. 22

2.3 Summary .................................................................................................................... 27

Chapter 3 : Research Methodology.............................................................................................. 28

3.1 Geographical Location ............................................................................................... 28

3.2 Pavement Sections ..................................................................................................... 29

3.2.1 Traffic Conditions ............................................................................................. 34

3.2.2 Design Criteria .................................................................................................. 36

ix

3.3 Scope of the Study ..................................................................................................... 36

3.4 Framework Adopted .................................................................................................. 37

Chapter 4 : Climate Data Extraction ............................................................................................ 40

4.1 Climate Data Requirements ....................................................................................... 40

4.2 Climate Model Databases .......................................................................................... 41

4.2.1 Preparing Data for Pavement ME Software from NARCCAP Climate Data ... 41

4.2.2 Climate Data Plots ............................................................................................ 43

4.3 Bias Correction Method ............................................................................................. 56

4.3.1 Temperature Bias Correction ............................................................................ 57

4.3.2 Precipitation Bias Correction ............................................................................ 57

4.4 Climate Change Parameters Summary ...................................................................... 59

Chapter 5 : Vulnerability Study of the Pavements....................................................................... 70

5.1 Pavement Performance Analysis ............................................................................... 70

5.1.1 Influence of Climate Models on IH30 Frontage Road Pavement Performance 70

5.1.2 Influence of Extreme Event on Pavement Performance ................................... 73

5.1.3 Combined Influence of Extreme Event and Climate Change on Pavement

Performance ...................................................................................................... 77

5.1.4 Influence of Climate Change on Selected Cities and Pavement Sections ........ 79

5.1.5 Influence of Geographical Location on Pavement Performance ...................... 84

5.2 FPS-21 Analysis......................................................................................................... 92

5.3 Adaptation Methods ................................................................................................... 95

5.3.1 Adaptation Methods for Climate Change ......................................................... 95

5.3.2 Adaptation Methods for Extreme Event ......................................................... 101

5.3.3 Adaptation to Extreme Events and Climate Change ....................................... 102

x

Chapter 6 : Economic and Environmental Analysis .................................................................. 104

6.1 Cost Analysis ........................................................................................................... 106

6.2 Emission Estimation ................................................................................................ 111

Chapter 7 : System Dynamics and Probabilistic Analysis ......................................................... 116

7.1 Introduction .............................................................................................................. 116

7.2 SD Model ................................................................................................................. 116

7.2.1 Climate Parameters ......................................................................................... 116

7.2.2 Sensitivity Analysis ........................................................................................ 118

7.2.3 System Dynamics Model ................................................................................ 119

7.3 Probabilistic Analysis .............................................................................................. 131

Chapter 8 : Closure .................................................................................................................... 136

8.1 Summary .................................................................................................................. 136

8.2 Findings.................................................................................................................... 137

8.3 Recommendations and Limitations.......................................................................... 139

References .................................................................................................................................. 141

Appendix A: Bias Correction Plots............................................................................................ 151

Appendix B: Pavement Analysis Data ....................................................................................... 160

Appendix C: Probabilistic Analysis ........................................................................................... 218

Curriculum Vita ......................................................................................................................... 237

xi

List of Tables

Table 2-1 SRES Based Emission Scenario (IPCC, 2000). ..............................................................7

Table 2-2 RCPs as per AR5, 2014. ..................................................................................................7

Table 2-3 Climate Data Sources Based on Different Emission Scenario and Downscaling

Methods............................................................................................................................................9

Table 2-4 Climate Change Studies Conducted in the US. .............................................................15

Table 2-5 Studies Conducted on Climate Change Adaptation. .....................................................18

Table 2-6 Studies Documenting Influence of Climate Change on Transportation. .......................25

Table 3-1 Traffic Information Used in Pavement ME Design. .....................................................33

Table 3-2 Pavement Layer Properties. ..........................................................................................34

Table 3-3 Pavement Section Distress Criteria. ..............................................................................36

Table 4-1 Characteristics of NARCCAP Climate Data and Requirements of Pavement ME. ......42

Table 4-2 Mean Annual Temperature. ...........................................................................................47

Table 4-3 Mean Annual Precipitation. ...........................................................................................48

Table 5-1 Design Traffic for Pavement Section in Fort Worth, TX. .............................................70

Table 5-2 Range of Change in Maintenance Years for Selected Pavement Sections with IRI as

Maintenance Criteria. .....................................................................................................................72

Table 5-3 Range of Change in Maintenance Years for Selected Pavement Sections with AC

Rutting as Maintenance Criteria. ...................................................................................................73

Table 5-4 Change in Maintenance Years for Selected Pavement Sections with IRI as

Maintenance Criteria ......................................................................................................................76



xii





Table 5-8 Asphalt Mix Type Specifications. .................................................................................99

Table 6-1 Life Cycle Costs for Agency and User. .......................................................................109

Table 6-2 Models and Sources for LCA. .....................................................................................113

Table 6-3 User Emissions. ...........................................................................................................114

Table 7-1 Mean Annual Temperature Summary for Fort Worth, TX. ........................................117

Table 7-2 Mean Annual Precipitation Summary for Fort Worth, TX. ........................................118

Table 7-3 Rate of Increase in Mean Annual Temperature and Precipitation. .............................118

Table 7-4 Equations for SD Model 1. ..........................................................................................124

Table 7-5 Equations for SD Model 2. ..........................................................................................127

Figure 7-12 Monte Carlo Simulation for AC Rutting (with Bias-Correction). ...........................133

Figure 7-13 Monte Carlo Simulation for IRI (with Bias-Correction)..........................................134

Table C-1 Annual Average Mean Temperature Summary for Amarillo, TX. .............................218

Table C-2 Annual Average Mean Precipitation Summary for Amarillo, TX. .............................219

Table C-3 Annual Average Mean Temperature Summary for Austin, TX. ................................219

Table C-4 Annual Average Mean Precipitation Summary for Austin, TX. ................................220

Table C-5 Annual Average Mean Temperature Summary for Corpus Christi, TX. ....................220

Table C-6 Annual Average Mean Precipitation Summary for Corpus Christi, TX. ....................221

Table C-7 Annual Average Mean Temperature Summary for Dallas, TX. .................................221

Table C-8 Annual Average Mean Precipitation Summary for Dallas, TX. .................................222

xiii

Table C-9 Annual Average Mean Temperature Summary for El Paso, TX. ...............................222

Table C-10 Annual Average Mean Precipitation Summary for El Paso, TX. .............................223

Table C-11 Annual Average Mean Temperature Summary for Houston, TX. ...........................223

Table C-12 Annual Average Mean Precipitation Summary for Houston, TX. ...........................224

Table C-13 Annual Average Mean Temperature Summary for McAllen, TX. ...........................224

Table C-14 Annual Average Mean Precipitation Summary for McAllen, TX. ...........................225

Table C-15 Annual Average Mean Temperature Summary for San Antonio, TX. .....................225

Table C-16 Annual Average Mean Precipitation Summary for San Antonio, TX. .....................226

xiv

List of Figures

Figure 2-1 Pavement ME Design Process (MEPDG Guide, 2004). ............................................. 11

Figure 2-2 Climate Change and Extreme Weather Vulnerability Assessment Framework (Hayhoe

and Stoner, 2012). ......................................................................................................................... 14

Figure 3-2 Pavement Sections Used for Analysis......................................................................... 29

Figure 3-3 AADTT Distribution by Vehicle Class. ...................................................................... 35

Figure 3-4 Axles per Truck by Vehicle Class. .............................................................................. 35

Figure 3-5 Adopted Framework.................................................................................................... 39

Figure 4-1 Mean Annual Temperature (a) Historical Climate (1979-2015) (b) CRCM-CCSM

Future (2038-2070) (c) CRCM-CGCM3 Future (2038-2070). .................................................... 45

Figure 4-2 Monthly Mean Temperature of Some of the Texas Counties for all Climate Prediction

Models (2030-2050)...................................................................................................................... 49

Figure 4-3 Mean Annual Temperature of Cities of Texas for all Climate Prediction Models

(2030-2050)................................................................................................................................... 52

Figure 4-4 Mean Annual Precipitation of Cities of Texas for all Climate Prediction Models

(2030-2050)................................................................................................................................... 53

Figure 4-5 Mean Annual Wind Speed of Cities of Texas for all Climate Prediction Models

(2030-2050)................................................................................................................................... 54

Figure 4-6 Mean Annual Relative Humidity of Cities of Texas for all Climate Prediction Models

(2030-2050)................................................................................................................................... 55

Figure 4-7 CRCM-CCSM Model Simulated and Observed Temperature (Pavement ME Existing)

Over the years. .............................................................................................................................. 57

Figure 4-8 Bias Correction for Mean Annual Temperature Fort Worth, TX. .............................. 58

xv

Figure 4-9 Bias Correction for Mean Annual Precipitation Fort Worth, TX. .............................. 58

Figure 4-10 Probability of Increase or Decrease in Seasonal Mean Temperature. ...................... 61

Figure 4-11 Quartile Range Plot for Mean Annual Temperature. ................................................ 62

Figure 4-12 Quartile Range Plot for Mean Annual Precipitation. ................................................ 63

Figure 4-13 Quartile Range Plot for Mean Annual Wind Speed. ................................................. 64

Figure 4-14 Quartile Range Plot for Mean Annual Relative Humidity. ....................................... 65

Figure 4-15 Range of Mean Annual Temperature Compared with Pavement ME Climate. ........ 66

Figure 4-16 Range of Mean Annual Precipitation Compared with Pavement ME Climate. ........ 67

Figure 4-17 Range of Mean Annual Wind Speed Compared with Pavement ME Climate. ........ 68

Figure 4-18 Range of Mean Annual Relative Humidity Compared with Pavement ME Climate.

....................................................................................................................................................... 69

Figure 5-1 Influence of Climate Change on IRI (Fort Worth, Texas). ......................................... 71

Figure 5-2 Influence of Climate Change on AC Rutting (Fort Worth, Texas). ............................ 72

Figure 5-3 Influence of Extreme Events on Rutting (Base and Subgrade) of Fort Worth (Texas)

Pavement Section. ......................................................................................................................... 75

Figure 5-4 Influence of Extreme Events on IRI of Fort Worth (Texas) Pavement Section. ........ 75

Figure 5-5 Influence of Extreme Events on Subgrade Modulus of the Fort Worth (Texas)


Figure 5-6 Influence of Extreme Events and Climate Change on Rutting (Base and Subgrade) of

Fort Worth (Texas) Pavement Section. ......................................................................................... 77

Figure 5-7 Influence of Extreme Events and Climate Change on IRI of Fort Worth (Texas)


xvi

Figure 5-8 Influence of Extreme Events and Climate Change on Subgrade Modulus of Fort

Worth (Texas) Pavement Section. ................................................................................................ 78

Figure 5-9 Observed IRI and AC Rutting Variation (Various Cities in Texas). .......................... 81

Figure 5-10 Observed IRI and Base + Subgrade Rutting Variation (Various Cities in Texas). ... 82

Figure 5-11 Observed IRI and Base + Subgrade Rutting Variation (Various Cities in Texas). ... 83

Figure 5-12 Influence of Dallas Climate on Performance of El Paso Pavement Section. ............ 85

Figure 5-13 Influence of Amarillo Climate on Performance of El Paso Pavement Section. ........ 86

Figure 5-14 Influence of Corpus Christi Climate on Performance of El Paso Pavement Section.86

Figure 5-15 Influence of El Paso Climate on Performance of El Paso Pavement Section. .......... 87

Figure 5-16 Influence of San Antonio Climate on Performance of El Paso Pavement Section. .. 88

Figure 5-17 Influence of Austin Climate on Performance of El Paso Pavement Section. ........... 88

Figure 5-18 Influence of Fort Worth Climate on Performance of El Paso Pavement Section. .... 89

Figure 5-19 Influence of Houston Climate on Performance of El Paso Pavement Section. ........ 89

Figure 5-20 Influence of Lubbock Climate on Performance of El Paso Pavement Section. ........ 90

Figure 5-21 Influence of McAllen Climate on Performance of El Paso Pavement Section. ........ 90

Figure 5-22 Influence of Geographical Location on Performance of El Paso Pavement Section. 91

Figure 5-23 Pavement Section using FPS 21. ............................................................................... 93

Figure 5-24 FPS Pavement Design with Maintenance at IRI of 100 in./mile. ............................. 93

Figure 5-25 IRI of the Pavement Section over the Years. ............................................................ 94

Figure 5-26 AC Rutting of the Pavement Section over the Years. ............................................... 95

Figure 5-27 Change in Performance of Pavements with Increase in AC Thickness. ................... 96

Figure 5-28 Change in Performance of Pavements with Changing Binder Type. ........................ 98

Figure 5-29 AC Rutting of the Pavement Section for Different Material Types. ......................... 99

xvii

Figure 5-30 IRI of the Pavement Section for Different Material Types. .................................... 100

Figure 5-31 Change in Design Life of the Pavement Section with Changing Asphalt Material. 100

Figure 5-32 Change in Performance of Pavements with Binder Grade Change and Increase in AC

Thickness .................................................................................................................................... 101

Figure 5-33 Adaptation against Extreme Event in the Subgrade. ............................................... 102

Figure 5-34 Mitigation Approach to Minimize Premature Pavement Failure (due to change in

temperature and precipitation). ................................................................................................... 103

Figure 6-1 Design for Cost-Benefit Analysis (a) Design A (ME Design) (b) Reduced Life of

Design A (ME Design) due to Climate Change (c) Climate Adopted Design (Design B) to meet

Design Life (d) Design B with Historical Existing Climate Data. ............................................. 105

Figure 6-2 Phases in Life Cycle of Pavements, Models used for LCCA and LCA. ................... 106

Figure 6-3 Range of Benefit with Increasing Traffic. ................................................................. 110

Figure 6-4 Range of Benefit with Increasing Traffic. ................................................................. 111

Figure 6-5 Range of Emission Savings with Increasing Traffic. ................................................ 115

Figure 6-6 Range of Emission Savings with Increasing Traffic. ................................................ 115

Figure 7-1 Influence of Mean Annual Temperature on AC Rutting. ......................................... 120

Figure 7-2 Influence of Mean Annual Precipitation on IRI. ....................................................... 120

Figure 7-4 SD Model 1 for Evaluating Influence of Climate Change on Performance Life of

Pavements. .................................................................................................................................. 123

Figure 7-5 IRI and Rut Depth vs. Mean Annual Precipitation for SD Model 1. ........................ 125

Figure 7-6 IRI and Rut Depth vs. Mean Annual Temperature for SD Model 1. ........................ 125

Figure 7-7 SD Model 2 for Evaluating Influence of Climate Change on Performance Life of

Pavements. .................................................................................................................................. 128

xviii

Figure 7-8 IRI and Rut Depth vs Mean Annual Precipitation for SD Model 2. ......................... 129

Figure 7-9 IRI and Rut Depth vs. Mean Annual Temperature for SD Model 2. ........................ 129

Figure 7-10 Impact of IRI and Rut Depth on Performance Life for SD Model 2. ..................... 130

Figure 7-11 Impact of IRI and Rut Depth on Performance Life for SD Model 2. ..................... 130

Figure A-1 Bias Correction for Mean Annual Temperature for Amarillo, TX. ......................... 151

Figure A-2 Bias Correction for Mean Annual Precipitation for Amarillo, TX. ......................... 152

Figure A-3 Bias Correction for Mean Annual Temperature for Austin, TX. ............................. 152

Figure A-4 Bias Correction for Mean Annual Precipitation for Austin, TX. ............................. 153

Figure A-5 Bias Correction for Mean Annual Temperature for Corpus Christi, TX. ................ 153

Figure A-6 Bias Correction for Mean Annual Precipitation for Corpus Christi, TX. ................ 154

Figure A-7 Bias Correction for Mean Annual Temperature for Dallas, TX. ............................. 154

Figure A-8 Bias Correction for Mean Annual Precipitation for Dallas, TX. ............................. 155

Figure A-9 Bias Correction for Mean Annual Temperature for El Paso, TX. ............................ 155

Figure A-10 Bias Correction for Mean Annual Precipitation for El Paso, TX. .......................... 156

Figure A-11 Bias Correction for Mean Annual Temperature for Houston, TX. ........................ 156

Figure A-12 Bias Correction for Mean Annual Precipitation for Houston, TX. ........................ 157

Figure A-13 Bias Correction for Mean Annual Temperature for McAllen, TX. ....................... 157

Figure A-14 Bias Correction for Mean Annual Precipitation for McAllen, TX. ....................... 158

Figure A-15 Bias Correction for Mean Annual Temperature for San Antonio, TX. .................. 158

Figure A-16 Bias Correction for Mean Annual Precipitation for San Antonio, TX. .................. 159

Figure B-1 IRI of the Pavement Section over the Years for Amarillo, Texas. ........................... 160

Figure B-2 AC Rutting of the Pavement Section over the Years for Amarillo, Texas............... 161

Figure B-3 IRI of the Pavement Section over the Years for Austin, Texas. ............................... 162

xix

Figure B-4 AC Rutting of the Pavement Section over the Years for Austin, Texas. ................. 163

Figure B-5 IRI of the Pavement Section over the Years for Corpus Christi, Texas. .................. 164

Figure B-6 AC Rutting of the Pavement Section over the Years for Corpus Christi, Texas...... 165

Figure B-7 IRI of the Pavement Section over the Years for Dallas, Texas. ............................... 166

Figure B-8 AC Rutting of the Pavement Section over the Years for Dallas, Texas. .................. 167

Figure B-9 IRI of the Pavement Section over the Years for El Paso, Texas. ............................. 168

Figure B-10 AC Rutting of the Pavement Section over the Years for El Paso, Texas. .............. 169

Figure B-11 IRI of the Pavement Section over the Years for Houston, Texas. .......................... 170

Figure B-12 AC Rutting of the Pavement Section over the Years for Houston, Texas. ............ 171

Figure B-13 IRI of the Pavement Section over the Years for McAllen, Texas. ......................... 172

Figure B-14 AC Rutting of the Pavement Section over the Years for McAllen, Texas. ............ 173

Figure B-15 IRI of the Pavement Section over the Years for Paris, Texas. ............................... 174

Figure B-16 AC Rutting of the Pavement Section over the Years for Paris, Texas. .................. 175

Figure B-17 IRI of the Pavement Section over the Years for San Antonio, Texas. ................... 176

Figure B-18 AC Rutting of the Pavement Section over the Years for San Antonio, Texas. ...... 177

Figure B-19 IRI of the Concrete Pavement Section over the Years for Fort Worth, Texas. ...... 178

Figure B-20 CRCP Punch-out of the Pavement Section over the Years for Fort Worth, Texas. 179

Figure B-21 Base + Subgrade Rutting of the Pavement Section over the Years for Amarillo,

Texas. .......................................................................................................................................... 180

Figure B-22 IRI of the Pavement Section over the Years for Amarillo, Texas. ......................... 180

Figure B-23 Subgrade Modulus of the Pavement Section over the Years for Amarillo, Texas. 181

Figure B-24 Base + Subgrade Rutting of the Pavement Section over the Years for Austin, Texas.

..................................................................................................................................................... 181

xx

Figure B-25 IRI of the Pavement Section over the Years for Austin, Texas.............................. 182

Figure B-26 Subgrade Modulus of the Pavement Section over the Years for Austin, Texas..... 182

Figure B-27 Base + Subgrade Rutting of the Pavement Section over the Years for Corpus

Christi, Texas. ............................................................................................................................. 183

Figure B-28 IRI of the Pavement Section over the Years for Corpus Christi, Texas. ................ 183

Figure B-29 Subgrade Modulus of the Pavement Section over the Years for Corpus Christi,

Texas. .......................................................................................................................................... 184

Figure B-30 Base + Subgrade Rutting of the Pavement Section over the Years for Dallas, Texas.

..................................................................................................................................................... 184

Figure B-31 IRI of the Pavement Section over the Years for Dallas, Texas. ............................. 185

Figure B-32 Subgrade Modulus of the Pavement Section over the Years for Dallas, Texas. .... 185

Figure B-33 Base + Subgrade Rutting of the Pavement Section over the Years for El Paso,

Texas. .......................................................................................................................................... 186

Figure B-34 IRI of the Pavement Section over the Years for El Paso, Texas. ........................... 186

Figure B-35 Subgrade Modulus of the Pavement Section over the Years for El Paso, Texas. .. 187

Figure B-36 Base + Subgrade Rutting of the Pavement Section over the Years for Houston,

Texas. .......................................................................................................................................... 187


Figure B-38 Subgrade Modulus of the Pavement Section over the Years for Houston, Texas. . 188

Figure B-39 Base + Subgrade Rutting of the Pavement Section over the Years for McAllen,

Texas. .......................................................................................................................................... 189


Figure B-41 Subgrade Modulus of the Pavement Section over the Years for McAllen, Texas. 190

xxi

Figure B-42 Base + Subgrade Rutting of the Pavement Section over the Years for Paris, Texas.

..................................................................................................................................................... 190


Figure B-44 Subgrade Modulus of the Pavement Section over the Years for Paris, Texas. ...... 191

Figure B-45 Base + Subgrade Rutting of the Pavement Section over the Years for San Antonio,

Texas. .......................................................................................................................................... 192


Figure B-47 Subgrade Modulus of the Pavement Section over the Years for San Antonio, Texas.

..................................................................................................................................................... 193

Figure B-48 Base + Subgrade Rutting of the Concrete Pavement Section over the Years for Fort

Worth, Texas. .............................................................................................................................. 193

Figure B-49 IRI of the Concrete Pavement Section over the Years for Fort Worth, Texas. ...... 194

Figure B-50 Subgrade Modulus of the Pavement Section over the Years for Fort Worth, Texas.

..................................................................................................................................................... 194


Texas. .......................................................................................................................................... 195

Figure B-52 IRI of the Pavement Section over the Years for Amarillo, Texas. ......................... 195

Figure B-53 Subgrade Modulus of the Pavement Section over the Years for Amarillo, Texas. 196


..................................................................................................................................................... 196

Figure B-55 IRI of the Pavement Section over the Years for Austin, Texas.............................. 197

Figure B-56 Subgrade Modulus of the Pavement Section over the Years for Austin, Texas..... 197

xxii


Christi, Texas. ............................................................................................................................. 198

Figure B-58 IRI of the Pavement Section over the Years for Corpus Christi, Texas. ................ 198


Texas. .......................................................................................................................................... 199


..................................................................................................................................................... 199

Figure B-61 IRI of the Pavement Section over the Years for Dallas, Texas. ............................. 200

Figure B-62 Subgrade Modulus of the Pavement Section over the Years for Dallas, Texas. .... 200


Texas. .......................................................................................................................................... 201

Figure B-64 IRI of the Pavement Section over the Years for El Paso, Texas. ........................... 201

Figure B-65 Subgrade Modulus of the Pavement Section over the Years for El Paso, Texas. .. 202


Texas. .......................................................................................................................................... 202


Figure B-68 Subgrade Modulus of the Pavement Section over the Years for Houston, Texas. . 203


Texas. .......................................................................................................................................... 204


Figure B-71 Subgrade Modulus of the Pavement Section over the Years for McAllen, Texas. 205


..................................................................................................................................................... 205

xxiii


Figure B-74 Subgrade Modulus of the Pavement Section over the Years for Paris, Texas. ...... 206


Texas. .......................................................................................................................................... 207



..................................................................................................................................................... 208

Figure B-78 Change in Performance of Pavements with Changing Binder Type for Amarillo,

TX. .............................................................................................................................................. 208

Figure B-79 Change in Performance of Pavements with Changing Binder Type for Austin, TX.

..................................................................................................................................................... 209

Figure B-80 Change in Performance of Pavements with Changing Binder Type for Corpus

Christi, TX. ................................................................................................................................. 209

Figure B-81 Change in Performance of Pavements with Changing Binder Type for Dallas, TX.

..................................................................................................................................................... 210

Figure B-82 Change in Performance of Pavements with Changing Binder Type for El Paso, TX.

..................................................................................................................................................... 210

Figure B-83 Change in Performance of Pavements with Changing Binder Type for Houston, TX.

..................................................................................................................................................... 211

Figure B-84 Change in Performance of Pavements with Changing Binder Type for McAllen, TX.

..................................................................................................................................................... 211

Figure B-85 Change in Performance of Pavements with Changing Binder Type for Paris, TX. 212

xxiv

Figure B-86 Change in Performance of Pavements with Changing Binder Type for San Antonio,

TX. .............................................................................................................................................. 212

Figure B-87 Change in Performance of Pavements with Increase in AC Thickness for Amarillo,

TX. .............................................................................................................................................. 213

Figure B-88 Change in Performance of Pavements with Increase in AC Thickness for Austin,

TX. .............................................................................................................................................. 213

Figure B-89 Change in Performance of Pavements with Increase in AC Thickness for Corpus

Christi, TX. ................................................................................................................................. 214

Figure B-90 Change in Performance of Pavements with Increase in AC Thickness for Dallas,

TX. .............................................................................................................................................. 214

Figure B-91 Change in Performance of Pavements with Increase in AC Thickness for El Paso,

TX. .............................................................................................................................................. 215

Figure B-92 Change in Performance of Pavements with Increase in AC Thickness for Houston,

TX. .............................................................................................................................................. 215

Figure B-93 Change in Performance of Pavements with Increase in AC Thickness for McAllen,

TX. .............................................................................................................................................. 216

Figure B-94 Change in Performance of Pavements with Increase in AC Thickness for Paris, TX.

..................................................................................................................................................... 216

Figure B-95 Change in Performance of Pavements with Increase in AC Thickness for San

Antonio, TX. ............................................................................................................................... 217

Figure C-1 Monte Carlo Simulation for AC Rutting for Amarillo (with Bias-Correction)........ 226

Figure C-2 Monte Carlo Simulation for AC Rutting for Austin (with Bias-Correction). .......... 227

Figure C-3 Monte Carlo Simulation for IRI for Austin (with Bias-Correction). ........................ 227

xxv

Figure C-4 Monte Carlo Simulation for AC Rutting for Corpus Christi (with Bias-Correction).

..................................................................................................................................................... 228

Figure C-5 Monte Carlo Simulation for IRI for Corpus Christi (with Bias-Correction). ........... 228

Figure C-6 Monte Carlo Simulation for AC Rutting for Dallas (with Bias-Correction). ........... 229

Figure C-7 Monte Carlo Simulation for IRI for Dallas (with Bias-Correction). ........................ 229

Figure C-8 Monte Carlo Simulation for AC Rutting for El Paso (with Bias-Correction). ......... 230

Figure C-9 Monte Carlo Simulation for AC Rutting for Houston (with Bias-Correction). ....... 230

Figure C-10 Monte Carlo Simulation for IRI for Houston (with Bias-Correction). ................... 231

Figure C-11 Monte Carlo Simulation for AC Rutting for McAllen (with Bias-Correction). ..... 231

Figure C-12 Monte Carlo Simulation for AC Rutting for San Antonio (with Bias-Correction). 232

Figure C-13 Monte Carlo Simulation for IRI for San Antonio (with Bias-Correction). ............ 232

Figure C-14 Monte Carlo Simulation for AC Rutting for Amarillo (without Bias-Correction). 233

Figure C-15 Monte Carlo Simulation for AC Rutting for Austin (without Bias-Correction). ... 233

Figure C-16 Monte Carlo Simulation for AC Rutting for Corpus Christi (without Bias-

Correction). ................................................................................................................................. 234

Figure C-17 Monte Carlo Simulation for AC Rutting for Dallas (without Bias-Correction). .... 234

Figure C-18 Monte Carlo Simulation for AC Rutting for El Paso (without Bias-Correction). .. 235

Figure C-19 Monte Carlo Simulation for AC Rutting for Houston (without Bias-Correction). 235

Figure C-20 Monte Carlo Simulation for AC Rutting for McAllen (without Bias-Correction). 236

Figure C-21 Monte Carlo Simulation for AC Rutting for San Antonio (without Bias-Correction).

..................................................................................................................................................... 236

1

Chapter 1 : Introduction

1.1 Problem Statement

The assessment of climate change’s impact on transportation infrastructure is a complex

phenomenon because of the variability in the infrastructure design as well as variability

associated with climate change prediction models.

To predict climate change, the Intergovernmental Panel on Climate Change (IPCC) was

established with an objective of assessing and understanding the risks of human-induced climate

change. IPCC generated a set of socioeconomic scenarios by forecasting world population and

industrial growth for estimating the future Greenhouse Gas (GHG) emissions (which impacts

climate) with each scenario having an equal probability of occurrence (IPCC 2000). General

Circulation Models (GCMs) and Regional Climate Models (RCMs) uses these scenarios to

simulate GHG emissions and predict future climate (Meyer et al., 2014). The prediction of

intensity and frequency of precipitation and temperature due to climate change can be estimated

using climate models developed by National Oceanic and Atmospheric Administration (NOAA),

United States Geological Survey (USGS), Geophysical Fluid Dynamics Laboratory (GFDL), etc.

However, the data available through the climate models are not readily useful to highway

agencies. It is essential to provide information at the regional and local levels because the

pavement design software needs regions specific climate information at a higher fidelity since

every region has specific climate and pavements design and materials will vary accordingly.

The changing hydrologic patterns, higher frequency of extreme weather events, storm

surge, a shift in temperature pattern, etc. are impacting the resiliency of the transportation

system. A change in precipitation pattern influences slope stability of infrastructure, reduces the

bearing capacity of subgrade due to saturation, damage of asphalt concrete layer due to stripping,

among others. The increase in precipitation also results in runoff which leads to high stream flow

impacting the dimension requirements for bridges and gutters (Warren et al., 2004). Similarly,

extremely hot and cold days’ affects pavements with softening and rutting, buckling in concrete

2

pavements and bleeding of the asphalt surface. These, unexpected levels of change in climate

adversely jeopardize the service life of transportation infrastructure.

Although studies have been conducted presenting the principal problems associated with

climate change, there is a need for project-specific information for highway officials, especially

regarding pavement infrastructure. Presently, historical weather data is used while designing the

pavement sections and analyzing its performance. To further complicate the issue, the designers

and decision-makers have to justify additional expenses needed for making transportation

structures resilient to the climate change.

Once the climate change impact is determined, the next step is how to incorporate the

changes into pavement design such that they can withstand the unexpected adversities. The

modification of existing infrastructure to survive future climatic events can have vast economic

implications. Overestimating climate events suggested by climate models can result in costly

overdesigning of infrastructure while underestimating will leave infrastructure vulnerable.

Therefore, there is a need for studying the risk and reliability of using climate change data. Also;

there is a need for assessing the cost-effectiveness of adaptation to climate change.

1.2 Research Objectives

The primary emphasis of this study is to identify the influence of climate change on the

performance of pavements and develop strategies to mitigate the same. Therefore, the null and

the alternative hypothesis are:

H0: Climate change does not significantly influence the performance of the pavement

infrastructure.

Ha: Climate change does significantly influence the performance of the pavement

infrastructure.

The following approach is adapted to verify the hypotheses:

3

1. This study is for regional and local level, so it is essential for the climate projections

to be downscaled and corrected for biases of projected climate to obtain the

meaningful information and reduce uncertainty in climate projection.

2. Identify climate change parameters that shall impact the pavement infrastructure with

an associated degree of uncertainty. These include extreme weather events, the rate of

climate change events over time and level of confidence of projections for each

weather element.

3. Develop a framework for evaluating pavement infrastructure for future evaluation.

4. Conduct a climate vulnerability assessment of roadways using the developed

framework, based on:

a. Current and projected variability of climate change.

b. Evaluating effects of climatic change on the performance of pavement

structures.

c. The degree of exposure of roadways to climate change parameters.

d. Nature and severity of each parameter.

e. The sensitivity of pavement performance to climate change.

5. Adaptation methods for climate change.

6. Conducting a probabilistic analysis.

7. Developing a system dynamics model.

8. Develop a cost-effective adaptation solution and climate change safety factor

depending on the region and design life and a guide for different adaptation

techniques.

1.3 Organization

This dissertation consists of eight chapters. Chapter 1 is the introductory chapter that

includes the problem statement and objectives of this study. Chapter 2 gives a brief background

about the studies conducted on climate change and climate models used in the studies. This

4

chapter also provides a brief idea about the pavement design software’s, and also explain the

gaps in the studies conducted.

Chapter 3 explains the research methodology adopted along with the frameworks used for

evaluating the performance of the pavements. Chapter 4 describes the climate data requirements

and how to extract the climate data from the available climate models and study its variations

based on the regions. The summarized results from all the selected climate models are explained

in this chapter.

Chapter 5 focuses on the climate change vulnerability assessment of the pavement

sections. The adaptation methods for climate changes are also explained in this chapter. Chapter

6 explains the cost-effectiveness of adaptation to climate change

In Chapter 7, the behavior of the pavement sections with climate change using system

dynamics model is observed. Finally, Chapter 8 summarized the results obtained by conducting

this study.

5

Chapter 2 : Literature Review

2.1 Background

The climate of the earth is predicted to change in the future, mainly because of human

activities influencing the composition of the atmosphere through the accumulation of GHG. As

per U.S. Global Change Research Program, the annual average temperature has increased by 1.8

°F from 1901 to 2016 over contiguous United States (CONUS) and is expected to rise by 2.5 °F

over the next few decades (2021-2050) under all possible climate scenarios (4th National

Climate Assessment, 2017). Even if drastic measures are taken to stabilize the GHG emissions,

still climate change will be experienced, and transportation professionals must take remedial

measures to alleviate the consequences (National Research Council, 2008).

A range of likely future scenarios is available for projecting the future climate conditions.

These scenarios are driven by socio-economic storylines about future demographic and

economic development, energy production and use, technology, land use, etc. (IPCC, 2000). The

revised scenarios were developed for the IPCC 5th Assessment Report (IPCC AR5, 2014) that

are based on representative concentration pathways (RCPs) that specifies the concentrations and

corresponding emissions. Various national and international agencies {like USGS, NOAA,

Geophysical Fluid Dynamics Laboratory (GFDL) to name the few} have spent significant

resources on predicting future climate simulations using different emission scenarios.

Climate change affects the transportation infrastructure system through several types of

climate and weather extremes, such as an increase in cumulative hot days, intense precipitation

events, hurricanes, droughts, rising sea level, storm surges, among others. The climate is one of

the critical inputs for the design of pavements, and its variability has a significant impact on the

pavement performance. Climate affects the selection of pavement types, materials, and

thicknesses of layers so that pavement can withstand the environmental burden and provide

intended service life (Li et al., 2011). However, the change in climate is inconsistent meaning the

level and magnitude of change is regional. Thus, the change in the planning, design,

6

construction, etc. needs to be incorporated based on local changes rather than global changes.

Additionally, transportation officials need to understand the consequences of climate change on

pavements performance and modify current design practices to mitigate the impact of the climate

change.

2.2 Review of Literature

To predict future climate simulations, various climate agencies have been developing

climate models over several decades. These agencies have used various future scenarios based on

GHG emission for forecasting the future climate. Hence, the output from each model is

anticipated to be different. Designers should understand the variability in predicted climate from

various models before implementing the design change or remedial actions.

2.2.1 Climate Scenarios and Models

Although most of the climate models have different approaches, the most common

difference can be attributed to emission scenarios. IPCC published a Special Report on

Emissions Scenarios (SRES) in 2000 (Nakicenovic and Swart, 2000). The emission scenarios

mentioned in SRES are used by the climate models for projection of future climate. The

projections are based on emission storyline and are summarized in Table 2-1.

7

Table 2-1 SRES Based Emission Scenario (IPCC, 2000).

Storyline Emission Scenario Description

A1 storyline Rapid economic growth, global population that peaks in mid-century and

then declines, and the early introduction of new and more efficient

technologies. A1F1, A1T, and A1B

A2 storyline A heterogeneous world with the continuously increasing global population.

Economic development is primarily regionally oriented, and per capita,

economic growth and technological change are more fragmented and slower

than in other storylines.

B1 storyline A convergent world with the same global population as in A1 storyline, but

with rapid changes in economic structures and the introduction of clean and

resource-efficient technologies.

B2 storyline Emphasis is on local solutions to economic, social, and environmental

sustainability with continuously increasing global population at a rate lower

than A2, intermediate levels of economic development, and less rapid and

more diverse technological change.

In 2014, IPCC in its Fifth Assessment Report (AR5) proposed Representative

Concentration Pathways (RCP) as an approach to predict future climate conditions. The

innovative approach uses GHG concentration trajectories instead of emissions to predict climate

change (Stocker et al., 2014). The RCP based on radiative forcing utilized for the projection in

climate models are summarized in Table 2-2.

Table 2-2 RCPs as per AR5, 2014.

RCP Radiative Forcing Levels

RCP2.6 Minimal GHG concentration levels. It is radiative forcing level first reaches a value

around 3.1 W/m2 mid-century, returning to 2.6 W/m2 by 2,100. GHG is reduced

substantially over time to achieve such radiative forcing levels.

RCP4.5 It is a stabilization scenario where total radiative forcing is stabilized before 2,100 by

the employment of a range of technologies and strategies for reducing GHG emissions.

RCP6.0 It is a stabilization scenario where total radiative forcing is stabilized after 2,100

without overshoot by the employment of a range of technologies and strategies for

reducing GHG emissions.

RCP8.5 The RCP 8.5 is characterized by increasing GHG emissions over time representative

for scenarios leading to high GHG concentration levels.

8

GCMs and RCMs use these different emission scenarios for projecting future climate.

GCMs, provide climate data for a large geographical area while pavement design needs climate

information at a higher resolution. For local climate changes, RCMs have been developed to

provide climate data at a higher fidelity than GCMs representing regional areas. In Table 2-3, the

different climate data sources, available based on emission scenarios, downscaling methods,

GCMs, RCMs, and spatial and temporal coverage are summarized.

Although AR5 is the latest report by IPCC, the use of SRES A2 is better suited for this

study due to fidelity and availability of simulated RCMs. Thus, further discussion is focused on

the same. This storyline uses a dynamic downscaling method to provide data at the local

resolution of 31 x 31 miles (50 x 50 km). NARCCAP database includes six RCMs and four

GCMs. The RCM models are: 1) Hadley Regional Climate Model Version 3 (HRM3), 2)

Regional Climate Model 3.0 (RCM3), 3) the Canadian Regional Climate Model (CRCM), 4)

Experimental Climate Prediction Center Regional Spectral Model (ECPC), 5) Mesoscale

Meteorological Model Version 5.0 (MM5I), and 6) the Weather Research and Forecasting Model

(WRFG). While the GCM models are: 1) the Hadley Centre Climate Model (HadCM3), 2)

Community Climate System Model (CCSM), 3) the Canadian Global Climate Model (CGCM3),

and 4) the GFDL model (Mearns et al., 2007, 2009).

9

Table 2-3 Climate Data Sources Based on Different Emission Scenario and Downscaling Methods.

Climate Source/ Downscaling

Method/ Emission Scenario

Parameters/

Spatial Resolution Climate Models Coverage

North American Regional Climate

Change Assessment Program

(NARCCAP) uses Dynamic

downscaling and is based on SRES A2.

(Mearns et al., 2007 and 2009)

Daily: (table 1) maximum and

minimum daily surface air

temperature

3-hourly: (table 2)

precipitation, surface air

temperature, cloud fraction,

wind speed, relative humidity.

Spatial resolution: 50x50 km

4 GCMs and 6

RCMs

Phase I, wherein six RCMs use boundary conditions from the

NCEP–DOE Reanalysis II (R2) for a 25-yr period (1980–2004),

and phase II, wherein the boundary conditions are provided by four

AOGCMs for 30 years of current climate (1971–2000) and 30

years of a future climate (2041–70)

Downscaled CMIP3 and CMIP5

Climate and Hydrology Projections

uses statistical downscaling. CMIP3

uses SRES B1, A1B, A2 while CMIP5

uses RCP2.6, RCP4.5, RCP6.0, RCP8.5

(Brekke et al., 2013)

(a) Monthly projections of

total precipitation and

monthly-mean daily average

temperature; and

(b) daily projections of the

precipitation, daily minimum

temperature, and daily

maximum temperature.

Spatial resolution: 1/8°

(12x12km)

CMIP3 - 16 climate

models

CMIP5 - 23 climate

models

BCSD: Coverage: 1950-2099 resolution: monthly

BCCA: CMIP3 coverage: 1961-2000, 2046-2065, 2081-2100

CMIP5 coverage: 1950-2099 resolution: daily

MACA CMIP5 Archive uses empirical

downscaling and has RCP4.5 and

RCP8.5 projections.

1) Maximum and Minimum

daily temperature near the

surface. 2)

Maximum and Minimum daily

relative humidity

3) Average daily precipitation

amount at the surface

Spatial resolution: 4-6km

20 global climate

models of the

Coupled Inter-

Comparison Project

5 (CMIP5)

Historical: 1950-2005 Future: 2006-2100

NASA NEX DCP30 National Climate

Change Viewer (NCCV) uses empirical

downscaling and has RCP4.5 and

RCP8.5 projections.

Maximum and Minimum

Temperature, Precipitation,

Runoff Spatial

resolution: 800-m grid over

the CONUS

30 of the Coupled

Inter-Comparison

Project 5 (CMIP5)

Dataset coverage: 1950-2005, 2025-2049, 2050-2074, and 2075-

2099

Program for Climate Model Diagnosis

and Intercomparison (PCMDI) CMIP5

Archive. Uses: RCP 2.6,4.5, 6.0 and 8.5

20 Climate modeling

groups

provide projections of climate change on two time scales, near-term

(out to about 2035) and long-term (out to 2100 and beyond)

10

2.2.2 Pavement Design Software’s

Although various pavement design software’s have been developed over the years, only

two pavement design software were selected in this study namely, Pavement ME Design

software and TxDOT Flexible Pavement System (FPS) 21. The selection of the first software

was based on the use of climate data while the second software selection was based on routine

use by the TxDOT for designing. A brief description of each software is included in the

following sections.

2.2.2.1 Pavement ME Design

Mechanistic-Empirical Pavement Design Guide (MEPDG) software now known as

Pavement ME Design is proposed for pavement analysis by AASHTO. The main reason for

selection of this software is the use of Integrated Climate Model (ICM) that considers climate

and moisture profiles in the pavement structure and subgrade over the design life of the

pavement (MEPDG Guide, 2004). Various researchers utilized MEPDG (now known as

Pavement ME) for predicting the performance of pavements with changing the climate (Meagher

et al., 2012, Mallick et al., 2014). An overview of inputs required, the analysis performed, and

design selection process is included in Figure 2-1, and a detailed discussion on the software can

be found in the 2004 design guide.

11

Figure 2-1 Pavement ME Design Process (MEPDG Guide, 2004).

2.2.2.2 Flexible Pavement System (FPS) 21

Flexible Pavement System (FPS) is a mechanistic-empirical (ME) design software used

by Texas Department of Transportation (TxDOT) for:

a. Pavement design (thickness)

b. Overlay design

c. Stress-strain response design

d. Rutting and Cracking (Pavement life prediction)

FPS design approach is based on a linear-elastic analysis system. FPS 21 is the most

recent version of the software which includes the following new features:

i. Pavement can be designed consisting of up to six layers on top of the subgrade

ii. The user-defined structure can be generated

iii. Provides extended stress analysis capabilities.

12

iv. Includes additional procedures for getting Texas Triaxial Class values for

subgrade.

Even though software has predetermined climate regions, it does not allow the inclusion

of future climate conditions in the design analysis. Since it is most commonly used for pavement

design in Texas, the software was used on a limited basis for comparison of the outputs from

Pavement ME software. Recently, TxDOT has developed Texas ME software for inclusion of

climate change. However, it was not used in this study because of time constrained.

2.2.3 Developed Frameworks

In this section, various climate studies, guidelines, models, and frameworks that have

been proposed for adaptation and planning, using various climate models, are discussed. The

Table 2-4 shows the climate change studies performed by USDOT’s and FHWA evaluating the

impact of climate change on US transportation infrastructure. USDOT Gulf Coast study (Phase I

and II) produced tools to assess vulnerability and build resilience to climate change. The recently

completed 2015 Phase II study proposed four useful assessment tools and the proposed tools are

as follows (Savonis et al., 2008, Hayhoe and Stoner, 2012):

i. Sensitivity matrix: This is a spreadsheet tool that has the sensitive nature of roads,

bridges, airports, ports, pipelines, and rail to 11 climate impacts.

ii. Guide to Assessing Criticality in Transportation Adaptation Planning: This guide

discusses everyday challenges associated with determining criticality, options for

defining criticality and identifying scope, and the process of applying criteria and grading

assets.

iii. CMIP climate data processing tool: A spreadsheet tool that transforms raw climate model

outputs at the local level from the World Climate Research Programme’s Coupled Model

Intercomparison Project into relevant statistics for transportation planners, including

changes in the frequency of scorching days and extreme precipitation events.

13

iv. Vulnerability assessment scoring tool (VAST): This tool provides user to decide what

assets to evaluate, an indicator to use and how to interpret the data, depending on these

inputs VAST will calculate vulnerability score for each asset.

FHWA developed a climate change, and extreme weather vulnerability assessment

framework (Figure 2-2) that was used for conducting the pilot studies and results are summarized

in Table 2-4.

2.2.4 Climate Adaptation Studies

One of the primary objectives of climate change vulnerability and risk assessment study

is to assess the influence of climate change on transportation assets and come up with a cost-

optimized solution. A well-planned adaptation strategy provides a sound and robust base for

decision-making when met with climate uncertainties. For instance, rerouting, mode change, and

modified designs are some of the approaches that can alleviate the impact of climate change to a

certain extent (Melillo et al., 2014). Countries like Canada, Denmark, the Netherlands, New

Zealand, Norway, and the United Kingdom have developed climate adaptation strategies and

policies. Some of the adopted policies are (Filosa and Oster, 2015):

1. The Danish Road Directorate follows the Blue Spot method to determine the roadway

locations where the likelihood of floods is high, and the consequence of flooding is

significant.

14

Figure 2-2 Climate Change and Extreme Weather Vulnerability Assessment Framework (Hayhoe

and Stoner, 2012).

15

Table 2-4 Climate Change Studies Conducted in the US.

No. Climate Studies Region covered Key findings/Approach Summary

1. Gulf Coast study

(Phase 1) U.S.

Climate Change

Science Program,

2008

U.S. central Gulf Coast

between Galveston,

Texas and Mobile,

Alabama

- for next 50-100 years, there will be a change in climate,

which includes warming temperature, change in precipitation

patterns and increased storm intensity.

- 27 percent of the principal roads, 9 percent of the rail lines,

and 72 percent of the ports are built on land which is below

122 cm (4 feet) in elevation and is more prone to frequent or

permanent inundation due to change in precipitation.

- More than half of the area’s major highways, rail miles, 29

airports, and the ports are below 7 m (23 feet) in elevation

and subject to flooding and possible damage due to hurricane

storm surge.

This study provides the impact of the climate

change on the transportation system. It showed

the vulnerability of the transportation

infrastructure to the increase of temperature,

inundation due to increase sea level rise and

precipitation patterns. Risk assessment

approach should be considered for

incorporating climate factors.

Future Work: More integrated climate data and

projections, a risk analysis tool and region

based analysis to be done.

2. Gulf Coast Study

Phase 2, October

2014

Mobile, Alabama - Determined the criticality of the infrastructure. The team

developed a scoring system ranked as high, medium and low,

which was based on socioeconomic importance, use and

operational characteristics and health and safety.

- Gathered region-specific climate information.

- Screening the assets for vulnerability, which was done

based on exposure, sensitivity and adaptive capacity of the

assets

- A detailed engineering assessment was done for vulnerable

assets to the climate stressor.

This study developed tools and approaches for

assessing criticality in transportation adaptation

planning and how best to adapt infrastructure to

the potential impacts of climate change. The

tools developed are as follows:

-Sensitivity Matrix Tool to identify potential

climate stressors to transportation assets

-Coupled Model Intercomparison Project

(CMIP) Climate Data Processing Tool to

projected changes in local temperature and

precipitation.

-Vulnerability Assessment Scoring Tool

(VAST)

3. Climate change

resilience pilots.

(FHWA) (2010-

2011)

Metropolitan

Transportation

Commission: San

Francisco Bay

- The inundation maps reveal nearly all the shoreline assets

would be inundated with the 55 inches of sea level rise

scenario.

- Limited flooding of the asset is expected to occur under the

midcentury scenario, and minor modifications can be made

during scheduled maintenance.

- For end-of-century scenario, more drastic adaptation

measures are required such as raising the road surface and

building a causeway.

These pilot studies are based on FHWA’s

Climate Change & Extreme Weather

Vulnerability Assessment Framework model.

This model is used in conducting vulnerability

and risk assessments of infrastructure to the

projected impacts of global climate change.

North Jersey (NJ)

Transportation Planning

-NJ TRANSIT indicated that temperatures higher than 95°F

would increase the risk of rail kinks.

16

No. Climate Studies Region covered Key findings/Approach Summary

Authority -Overhead wires may sag or experience pulley failures

during extreme heat.

-Enhancing shoreline infrastructure protection for managing

sea level rise.

Washington State

Department of

Transportation

-Seismic retrofits may boost a bridge’s ability to withstand

high winds, and widen or replace culverts and may reduce

exposure to flooding.

-Areas above or below steep slopes have high impact to

climate change.

-Low lying areas subject to flooding from sea level rise

Oahu Metropolitan

Planning Organization

-This study identified Honolulu Harbor, Honolulu

International Airport, and Farrington Highway on the

Waianae Coast has the highest risk due to climate change

(precipitation, sea level rise).

-The project team found that by the end of the century, the

Honolulu Harbor's low-lying areas are likely to flood due to

sea level rise.

4. Climate change

resilience pilots.

(FHWA) (2013-

2015)

Arizona Department of

Transportation

-Covered 300-mile stretch, screen vulnerable areas

-Temperature increase- reduce winter maintenance

-Precipitation and wildfire trends pose a threat to ADOT

assets

California Department

of Transportation

(District 1)

-Sea Level Rise (SLR) and increased coastal erosion affects

roads

-Vulnerable to slope instability, drainage, and erosion

Iowa Department of

Transportation

-Used downscaled climate projection for simulating peak

flow statistics

-Used to confirm resilience of new bridge project on I-35

Maine Department of

Transportation

-Majority of damage due to storms surge not SLR

-Identified design option and Life cost for each SLR scenario

North Central Texas

Council of

Governments

(NCTCOG)

-Vulnerability assessment- 636 miles of roads inundated by a

100-year flood

-Increase in temperature may reduce soil moisture- pavement

cracking and stresses on bridges and culverts

17

2. A joint research project by European countries called as ROADAPT research project

developed a preliminary risk assessment method to identify vulnerable locations,

understand the probabilities and consequences of future events on the transportation sites,

and provide options for adaptation actions.

3. The United Kingdom requires considering effects of climate change in planning, design,

construction, and operation stage for Nationally Significant Infrastructure Projects

approval.

4. Similarly, the New Zealand government provides information and guidance to the local

authorities on integrating climate change considerations into decision-making processes.

5. The Korea Expressway Corporation’s bridge design adopted new countermeasures

against climate change by including requirements of designing new bridges with 200-

year return period (instead of a 100-year return period) and having 2 m of minimum

freeboard. It has also added measures for retrofitting the existing bridges (for instance

bridges at low elevation should either be reconstructed or their heights be raised by 1to 3

m).

6. The Norwegian Public Roads Administration also included adaptation measures in its

planning, design, and construction manuals.

7. To focus on climate change adaptation, the European Road Association conducted two

research programs. The first program was designed to provide European road authorities

with the knowledge and tools necessary to "get to grips" with climate change and its

impacts on all elements of road management by adopting design rules, updating and

improving data collection, and developing risk management methods. The second

program focused on providing owners with adaptation technologies, and the models and

tools to support decision making concerning adaptation measures for pavement

infrastructure.

18

Table 2-5 Studies Conducted on Climate Change Adaptation.

No. Name of the Study Summary

1. Development of a Methodology for the

Assessment and Mitigation of Sea Level

Rise Impacts on Florida's Transportation

Modes and Infrastructures

-Inventory of the research and studies analyzing projected sea level rise

in Florida.

-Analyzing the data sources and methods for forecasting statewide sea

level.

-Recommendations to integrate these data with FDOT information

systems for identifying infrastructure at risk from sea level rise.

2. Climate Change Adaptation Guide: For

Transportation Systems Management,

Operations, And Maintenance (FHWA,

2015)

-Assessing the downscaled projected change in temperature.

-Increasing inspection of highway conditions.

-Considering cooler pavements for reducing surface temperature.

-Expanding the use of pavements operating better under high

temperatures.

-Developing a plan for maintenance of traffic during extreme weather

events.

-Training individuals on the impacts of climate change and how it will

affect their roles and responsibilities.

3. Progress Report of the Interagency Climate

Change Adaptation Task Force:

Recommended Actions in Support of a

National Climate Change Adaptation

Strategy (October 2010)

-Ensuring the Federal Government can execute its mission and services

in case climate change is vital

-Integrating the critical impact of climate change into development,

implementation and evaluation strategies

-Improve water resource management in a changing climate

-Addressing climate change in public health activities to protect human

health

-Building resilience to climate change

-DOTs should develop methods to include climate risk analysis in

transit policies, promoting vulnerability assessments, and assessing and

updating transit design standards.

19

2.2.5 System Dynamics

System Dynamics (SD) is the science of representing problems of systems through

appropriate computer models and simulating them for learning about the effect of

interdependencies of various components of systems and understanding the behavior of the

system over time (Forrester, 1968). The equations are developed based on experimental or

computational studies, or by answers to survey questionnaires. Most problems consist of systems

with many interrelated and dynamic (change over time) factors and feedback.

SD can be computer coded for policy analysis and design, which helps in adjusting

design based on climate change. SD approach models the relationships among all parts of a

system and estimates how those relationships influence the behavior of the system over time.

The developed relationships and connections between the components of the system are called

the structure of the system.

SD approach involves (System Thinking and Modeling for Planners):

Defining the problem graphically.

Thinking of all concepts in the real system as continuous quantities

interconnected in loops of information feedback and circular causality.

Identifying independent stocks or accumulations (levels) in the system and their

inflows and outflows (rates).

Formulating a behavioral model capable of reproducing, by itself, the dynamic

problem of concern. The model is usually a computer simulation model expressed

in nonlinear equations but is occasionally left unquantified as a diagram capturing

the stock-and-flow/causal feedback structure of the system.

Deriving understandings and relevant policy insights from the resulting model.

Implementing changes resulting from model-based understandings and insights.

SD structure (as per US Department of Energy’s Introduction to System Dynamics) has

closed boundary structure consisting of feedback loops. The feedback loop includes levels and

20

rates. Levels or stocks represent the quantities that accumulate or depletes. Stocks are changed

over time by inflows and outflows. Stocks usually represent the current state of the system while

Flow represents the rate of change of stocks. The parameters that can affect flows, and in most

cases, that can be controlled are designated as ‘converters.' Converters are used to convert input

data into some output signal. The links between these components are called connectors, which

allows information to pass between “converters and converters,” “stocks and converters,”

“stocks and flows,” and “converters and flows.” There are two types of the feedback loop (as per

MIT’s System Dynamics Open courseware):

1. Positive Feedback Loop: Positive feedback occurs when a change produces more

change in the same direction. Positive loops are compounding, reinforcing, or

amplifying systems that produce exponential behavior. It drives growth and change.

Positive feedback cannot continue forever. There is always a factor(s) in a system that

limits the growth of its elements. This limiting factor in a system with positive

feedback is known as limits to growth. Figure 2-3 shows the example of the positive

feedback loop. As the rate of increase in temperature increases, the maximum

pavement temperature will also increase.

Figure 2-3 Positive Feedback Loop.

2. Negative Feedback Loop: Negative feedback occurs when a change in a system

produces less and less change in the same direction until a goal is reached. The

21

negative feedback loop is a balancing or stabilizing system that produces asymptotic

or oscillatory behavior, or in simpler terms, Negative feedback negates change and

maintains systems. Figure 2-4 shows an example of the Negative Feedback Loop. As

the rate of deterioration decreases, the average life of pavements will increase. Thus,

the newly constructed pavements will last longer and vice versa.

Figure 2-4 Negative Feedback Loop.

To provide an efficient linkage between climate change factors and the

performance/durability of the components of the pavement infrastructure, an SD-based

framework can be developed. This structure can simulate dynamic interactions between different

factors. SD is useful for evaluating long-term effects of climate change on pavement

performance. (Mallick et al., 2014). The simulation tools developed by Mallick et al. (2015) are

useful in the selection of the most appropriate level of response needed to evaluate flooded

pavement. The model is used to evaluate pavements with regards to flooding damage and also to

take the necessary preventive actions. It was observed that even short-term inundations could

compromise the HMA strength. Thus, roads in coastal regions or river floodplains are

particularly vulnerable to impacts from future flooding (Mallick et al., 2015).

The pavement infrastructure deteriorates over time due to continuous exposure to heavy

traffic as well as climate. Additionally, pavement structure is a multilayered system; therefore,

the level of deterioration is different in individual layers (Mallick et al., 2015). The loss in

strength and functionality of pavement structure over time makes the pavement structure a

22

dynamic system. The climate factors influencing the performance of the pavements are air

temperature, sea level rise, annual rainfall, flooding, etc. To understand the full impact of these

climate factors or changes, a system level analysis approach is required. In other words, an

analysis that is holistic includes all the relevant factors and their interdependencies and considers

the time-dependent aspect of the problem is needed and SD approach provides an ideal system

for the same (Mallick et al., 2014, 2015, 2016).

2.2.6 Relevant Climate Change Research

Various studies have been carried out to understand the effect of climate change on the

transportation network. As per the US DOT Gulf Coast Study (Phase 1), the rise in sea levels

will impact the interstates and arterials, 75 % of the port facilities, etc. The probability of

infrastructure failures (Savonis et al., 2008) is expected to increase due to climate change.

Alteration in temperature and precipitation pattern will lead to fluctuation in river levels that may

lead to an increase in inland shipping cost and safety (Koetse et al., 2009). To assess risks, the

transportation vulnerability to climate changes (temperature, precipitation and sea level rise)

needs to be examined. It assists planners in prioritizing the assets that need proactive action to

mitigate the influence of climate change (Wu et al., 2013). These studies provided a generalized

impact on highways due to climate change, but the actual effects and their consequences depend

on the geographical location of the project.

The climate data currently used in the designs is historical weather data, and with climate

change, the pavement designs may not be able to withstand adverse weather conditions. Meagher

et al. (2012) conducted a study to evaluate the influence of climate change on pavement

performance (Pavement ME) using future climate data (NARCCAP climate database and only

temperature). The result of the study suggested that the future climate impacted Asphalt Concrete

(AC) rutting while the effect on alligator cracking was negligible. Another study (Sultana et al.,

2014) evaluating deterioration of pavements identified that the pavement strength would reduce

23

by 1.5-50% due to flooding. Melillo et al. (2014) stated that climate change causes additional

expenses to the transportation systems and their users.

Mills et al. (2009) used MEPDG (Pavement ME) to estimate climate change effects on 17

southern Canadian highways by using two climate models CGCM2 (A2 Scenario) and HadCM2

(B2 Scenario). They identified that the rutting potential of these highways would increase due to

changing the climate, and premature maintenance will be required. They concluded that agencies

benefit by incorporating climate change climate in their designs. However, this study did not

perform any cost-benefit analysis. Qiao (2015) also used Pavement ME Design to analyze the

effects of climate change on pavement performance. This study developed a decision framework

for considering the change in future climate for highway maintenance operations and for

estimating life-cycle costs. They demonstrated the process of including framework in the design

through six pavement sections. Carrillo et al. (2013) estimated the climate change adaptation

costs on Netherlands highway network using eight different climate models and concluded that

the proactive adaptation is beneficial that annually saves millions of dollars.

Atkins Ltd. studied economics and risks associated with climate change adaptation and

focus of their study was to identify the impact of hotter and drier summers in the United

Kingdom (UK). They found that if no preventive action (design change) is taken there will be a

burden on user’s costs due to frequent maintenance operations. They stated that in the worst-case

scenario (maximum impact of climate), the benefits of adaptation outweigh the costs. They also

stressed the importance of timing as the key driver for yielding higher benefits.

Daniel et al. (2014) stated that the climate adaptation should also consider - risk

assessment and risk management. A climate-related risk on an asset is not only the failure of the

asset (or incapacitated functionality) but also includes failure consequences (Meyer, 2014). Risk

assessment is the critical step in adaptation planning of climate change. Most of the researchers

focused on assessing the economic impact on highway agencies due to premature failure of

pavement. Few researchers incorporated the influence on pavement users. Risk management

focuses on alleviating effects on an asset (pavement) by using high-performance materials,

24

change in design, or increased frequency of maintenance operations, etc. The resultant benefits

(lessened consequences) due to the additional costs expended in risk management is estimated

(usually, as the benefit to cost ratio). The transportation officials make use of these estimates for

decision-making.

The impacts of an asset failure or lowered performance encompass not only economic

losses but also social and environmental losses. There are no studies on accounting the benefits

(or burdens) on the environment due to climate adaptation. Since the future climate estimates

rely on some hypothetical emissions scenarios; it is paramount to account for environmental

impacts due to the adaptation of predicted climate. There is also no guidance on which model’s

(predicted future climate) data to use in evaluating pavement design.

Another area of concern is that the highway agencies modify their design, but there is no

significant shift in the climate in the future due to unforeseen changes. In this scenario, agencies

spend significant resources (economic as well as environmental) and expected benefits would not

be recuperated. Thus, the designers and planners need to consider these situations in decision-

making as well. The studies conducted on the climate change and its impacts on transportation

infrastructures are summarized in Table 2-6.

25

Table 2-6 Studies Documenting Influence of Climate Change on Transportation.

Topic Objective Summary What is Missing?

Method for Evaluating

Implications of Climate

Change for Design and

Performance of Flexible

Pavements (Meagher et al.,

2012)

Develop a method to

include climate model

forecast in the MEPDG and

assess the implication of

climate and climate change

on pavement deterioration

processes.

The regional climate model temperature data was transformed

to be compatible with the MEPDG climate database, and then

the MEPDG model was used to predict stresses. The impact of

future temperature on AC rutting was significant while the

effect on alligator cracking was negligible.

Only temperature data was

modified to identifying the impact

of future climate change.

A review of the structural

performance of Flooded

Pavements (Sultana et al.,

2014)

This paper analyzed the

impact of flooding on

pavement deterioration.

Pavement deflection, obtained Falling Weight Deflectometer,

were obtained and analyzed before and after the flood events.

A reduction (1.5-50% change) in the pavement strength was

observed after flooding.

Roughness, cracking and rutting

also needed to be included.

The impact of climate

change and weather on

transport: An overview of

empirical findings. (Koetse

and Reitveld, 2008)

This paper presents an

overview of empirical

findings on the impact

climate change and weather

on transportation.

- Changes in temperature and precipitation will alter water

levels in rivers that will in turn influence inland shipping cost.

- Wind, poor visibility and rain storms will affect the aviation

sector.

- Fog, heat, snow, and precipitation changes will alter accident

frequency and severity, congestion pattern, and travel time

increase.

The study shows the impact of

climate change on the different

transport network. However, there

is also a need to consider future

climate data during the design and

planning phases to mitigate the

impact of climate change.

Deterioration Modeling for

condition Assessment of

flexible pavements

considering extreme weather

events. (Tari et al., 2015)

This paper proposes a

pavement deterioration

model which considers the

effect of traffic loads,

climate condition, and

extreme weather events.

Pavement deterioration model was developed using data from

eight states over eighteen years. Regression approach was

carried out to quantify the effect of the extreme weather events

on the change in IRI. There was a 90% correlation between the

model and the actual conditions surveyed after extreme events.

More data from different regions

can be added to improve the

correlation. Impact of two or more

extreme events needs to be

evaluated.

Climate change effects on

Transportation

Infrastructure. (Wu et al.,

2013)

The potential effect of

climate change on

infrastructure is

investigated using

geographic information

systems (GIS) risk analysis

tool for Hampton Roads,

Virginia.

A scenario-based climate change is adopted for a combination

of different climate events. Sea level rise, precipitation intensity

and storm surges (forecasted using Sea, Lake, and Overland

Surges from Hurricanes (SLOSH) model) were the climate data

examined. A GIS-based evaluation of transportation

vulnerability to these climate changes was considered. Three

different risk scenarios were considered while generating the

GIS-based risk map and the results indicated that the city is at

risk if all three scenarios coincide. This risk model can assist the

decision makers and planners in prioritizing the assets.

Temperature change is also one of

the major climate factors, which

was not considered in the scenario-

based climate change.

26

Table 2-6 (continued) Studies Documenting Influence of Climate Change on Transportation

Topic Objective Summary What is Missing?

Pavement Structures damage

caused by Hurricane Katrina

flooding. (Zhang et al., 2008)

The pavement structures were

analyzed for structural damage

due to Hurricane Katrina in New

Orleans

Since pre-flooding data was unavailable, an indirect approach

was used to conduct the analysis. This was done by utilizing

the areas with the same environmental conditions and

pavement structure which were not flooded. FWD, GPR, and

DCP test were conducted, and results indicated that AC

pavements with lower elevation and the thinner pavements

were affected more while PCC damage was limited as

compared to AC.

No conclusion was

drawn for the effect of

flood damage for

composite pavements.

Risk and economic viability of

housing climate adaptation

strategies for wind hazards in

southeast Australia. (Stewart,

2015)

This paper uses break-even

analysis to compare risks, costs,

and benefits of climate adaptation

strategies for new housing.

Break-even estimates of risk reduction and adaptation cost for

designing new housing were calculated for three scenarios: no

change, B1, and A1F1. It was found that the adaptation

strategy can lead to risk reductions of 50–80 % at the cost of

approximately 1 % of house replacement value.

Climate impact risks and

climate adaptation engineering

for built infrastructure. (Stewart

and Deng, 2014)

This paper describes how the risk-

based approaches best fit for

optimizing climate adaptation

strategies.

A risk-based decision-support framework was developed for

assessing the cost-effectiveness of adaptation measures.

Climate hazard model, vulnerability model, no climate

adaptation risk, climate adaptation measures, risk assessment

and cost and benefits were considered while performing risk-

based decision analysis.

27

2.3 Summary

In recent years, flooding, droughts, wildfire, hurricanes and other natural climatic events

have impacted the infrastructures causing disruptions to the community. Many transportation

agencies are incorporating climate change into design and planning. FHWA along with the

DOTs have developed a vulnerability assessment tool that helps the agencies in understanding

how climate change is impacting the transportation infrastructures and its vulnerability. Various

studies have been conducted to study the influence of climate change, and not much work is

done to consider adaptation into designing. So, this study proposes to help the decision makers in

adapting to the climate change, rather than using current climate factors into design and

planning. This study provides a detailed overview of the changing climatic factors like

temperature and precipitation using climate prediction models and how to incorporate these

changes into designing of the pavement structures. The implications of these climate changes are

described for different pavement structures, mix types, and regional variations.

Recommendations to mitigate climate change and its impact on life-cycle costs are also

provided.

28

Chapter 3 : Research Methodology

The purpose of this study is to document anticipated change in climate and its impact on

the performance of the pavements for the state of Texas. Since variations in climate change differ

based on different geographical locations, eleven urban cities were selected within Texas to

cover climate variability. Through this study, the effects of change in temperature, precipitation,

and extreme events on pavement performance were explored. Although climate parameters like

wind speed and humidity are also covered, these parameters minimally impact the performance

of the pavement. The evaluation process developed in this study will help local, state highway

agencies, transportation professionals and decision-makers in conducting vulnerability research

and developing adaptation methods.

3.1 Geographical Location

The eleven selected cities (Figure 3-1) are Amarillo, Austin, Dallas, El Paso, Corpus

Christi, Lubbock, Paris, Fort Worth, San Antonio, McAllen, and Houston. The selection was

based on geographical locations, availability of pavement design data, traffic volume within

Texas.

Figure 3-1 Geographical Location of the Selected Texas Cities.

29

3.2 Pavement Sections

To study the influence of climate change, twelve different pavement designs were

selected for performance evaluation using future climate data in the Pavement ME software. The

pavement layer information along with traffic levels is summarized in Figure 3-2.

Figure 3-2 Pavement Sections Used for Analysis.

(a) IH30 Frontage Road, Tarrant

County, Fort Worth

Material Properties Traffic (Lane in Design Direction)

(Modulus)

Type D, PG 70-22 Initial two-way AADTT: 828 (2)

120 ksi

4.5 ksi


40 ksi

8 ksi

(b) SH 197, Houston

(c) IH 10 Frontage Road,

El Paso


40 ksi

8 ksi

30


(d) US83 Uvalde County,

San Antonio


(Modulus)


65 ksi

35 ksi

14.9 ksi

(e) US 62, El Paso


30 ksi

18 ksi


Type C, PG 64-22

50 ksi

35 ksi

8 ksi

(f) SH 19, Paris

31



(Modulus)


30 ksi

16 ksi

Modulus of Rupture: 620 psi,

Elastic Modulus: 5000 ksi Initial two-way AADTT: 625 (2)

Type D, PG 64-22

4.5 ksi

4.5 ksi

(g) FM 1926, McAllen

(h) Chapel Creek Blvd, Fort Worth

Type C, PG 70-22 Initial two-way AADTT: 778 (1)

Type D, PG 70-22

120 ksi

32 ksi

8 ksi

(i) US175, Dallas

32


The pavement sections are selected based on their traffic classification. Table 3-1 shows

the truck traffic for each road sections categorizing it as low volume or high-volume roads. For


(Modulus)


Type D, PG 64-22

40 ksi

8 ksi

(j) S-44, Corpus Christi


Type D, PG 64-22

30 ksi

16 ksi

(k) N_Mopac Expressway, Austin


50 ksi

15 ksi

8 ksi

(l) I40, Amarillo

33

each pavement analysis, a traffic growth rate of 0.75% is considered, and all pavements are

analyzed for 20 years of service life. Since the performance of pavement will be dependent on

truck traffic, the selection of pavement belonging to different traffic classification is appropriate.

Additionally, the modification of low volume roads to withstand climate change may not be cost-

effective. Each selected pavement design is existing pavement design currently in practice that

was designed using FPS 21. Since FPS 21 doesn’t allow weather data to be entered, the

pavement designs were analyzed using Pavement ME design software.

Table 3-1 Traffic Information Used in Pavement ME Design.

Sections Location Initial two-way Annual Average Daily Truck

Traffic (AADTT)

US83 San Antonio 650

IH 30 Frontage Road Fort Worth 828

SH 197 Houston 1000

US 60 El Paso 532

SH 19 Paris 612

FM 1926 McAllen 200

Chapel Creek Blvd Fort Worth 625

US 175 Dallas 778

S-44 Corpus Christi 1500

N_Mopac Expressway Austin 700

I 40 Amarillo 3000

The pavement layer properties of the selected pavement sections are summarized

in Table 3-2. The specifications of the layer materials are based on the Texas Department of

Transportation (TxDOT) “Standard Specifications for Construction and Maintenance of

Highways, Streets, and Bridges.”

34

Table 3-2 Pavement Layer Properties.

Material Type Specifications Design

Modulus

Poisson’s Ratio Layer Type

Dense Graded

HMA, thin

Item 340, 341 500 ksi 0.35 AC Layer

Dense-Graded

HMA, thick

Item 340, 341 650 ksi 0.35 AC Layer

Performance Mix Item344 650~950 ksi 0.35 AC Layer

Stone-Matrix

Asphalt

Item 346 650~850 ksi 0.35 AC Layer

Asphalt Treated

Base

Item 292 250~400 ksi 0.35 Base Layer

Flexible Base Item 247 40~70 ksi 0.35 Base Layer

Cement

Stabilized Base

Item 275, 276 80~150 ksi 0.30 Base Layer

Subgrade 8~16 ksi 0.40 Subgrade Layer

3.2.1 Traffic Conditions

The Pavement ME Design software requires traffic inputs which consist of the vehicle

class distribution of the AADTT and number of axles per truck for the vehicle class considered.

Figure 3-3 shows the distributions of vehicle Class 4 to Class13 used in this analysis. The same

classification is adopted for all the analysis done in this study. Similarly, the number of axles per

truck (single, tandem, tridem and quad axle) for each vehicle class is shown in Figure 3-4.

35

Figure 3-3 AADTT Distribution by Vehicle Class.

Figure 3-4 Axles per Truck by Vehicle Class.

36

3.2.2 Design Criteria

The flexible pavements are designed for 20 years. A threshold value is selected for the

pavement distresses, based on which the overlay is provided. As per Pavement ME Design, the

design criteria required as input are summarized in Table 3-3. In this study, a pavement requires

maintenance if measured IRI value is equal to or more than 100 in./mile and if measured AC

rutting is equal to or more than 0.4 inches.

Table 3-3 Pavement Section Distress Criteria.

Distress Type Distress @

Specified

Reliability

Terminal IRI (in/mile) 172.00

Permanent deformation - total pavement (in) 0.75

AC total fatigue cracking: bottom up + reflective (% lane

area)

25.00

AC total transverse cracking: thermal + reflective (ft/mile) 2500.00

AC bottom-up fatigue cracking (% lane area) 25.00

AC thermal cracking (ft/mile) 1000.00

AC top-down fatigue cracking (ft/mile) 2000.00

Permanent deformation - AC only (in) 0.40

Chemically stabilized layer - fatigue fracture (% lane area) 25.00

3.3 Scope of the Study

Pavement performance analysis is done for twelve climate models. Temperature,

precipitation, relative humidity, wind speed and percent sunshine are the climate parameters

required as an input in the design software. Since percent sunshine parameter was not available

in the climate model databases, it is not considered in this study. For percent sunshine, the

historical weather data provided in the Pavement ME Design software was used.

In this study, to include the effects of extreme events, the water table depth is changed.

The reason for doing so was due to the limitation of the design software. Pavement performance

is determined using pavement distresses as mentioned in Table 3-3. International Roughness

37

Index (IRI) and AC rutting are the main criteria for evaluating the performance of the pavements

using twelve climate models.

3.4 Framework Adopted

To perform pavement analysis using Pavement ME software, a framework was adopted

for this study and is included in Figure 3-5. The steps followed for performing this study are:

1. Select the Location (Latitude and Longitude) of the city.

2. Obtain the Future and Existing Climate database for the selected location.

a. Future Climate Database: Selection of the climate prediction model depends on

emission scenarios, downscaling methods, and resolution. For this study, the

NARCCAP climate data source that uses a projected coordinate system (xc and

yc) to store data was used (shown below).

If xc and yc co-ordinates are not available for corresponding latitude and longitude

than the grid is selected through surrounding stations. So, the average of climate

data at four grid-cells co-ordinates will provide the climate data for the required

location. Each RCMs will have different grid points for the same

latitude/longitude.

38

b. Existing Climate Database (Historical period): Pavement ME Climate Data.

3. Select the climate parameters: In this study, Temperature, Precipitation, Relative

Humidity and Wind Speed were selected.

4. Next step is to convert the climate data into a format used by Pavement ME software.

a. Chapter 4 discusses the conversion of raw climate data obtained from NARCCAP

to software ready ‘.hcd’ climate file.

b. A MATLAB code is written to expedite conversion.

5. Pavement Distress Analysis: In this step, the future climate files are used when analyzing

the pavements sections and are compared with the existing scenarios (Pavement ME

climate) to determine the vulnerability of the sections.

6. Developing adaptation methods to make the structure resilient.

a. Adaptation methods are proposed that can be implemented at the initial design

stage to mitigate the impacts of climate change.

7. Economic analysis: In this step, the extra cost incurred by modifying the design of the

pavement sections and the future benefits are compared.

39

Figure 3-5 Adopted Framework.

40

Chapter 4 : Climate Data Extraction

The focus of this chapter is to identify climate data requirement for evaluating pavement

design using AASHTO Pavement ME design software and evaluate climate models available

with the required information. This section provides an explanation of the selected climate data

and a procedure to extract the required climate data from the database.

4.1 Climate Data Requirements

Pavement design software (AASHTO Pavement ME) is used in this study to account for

climate change in the design and analysis of pavements. Integrated Climate Model (ICM)

embedded in the Pavement ME Design software uses climate file (‘.hcd’ format) for estimating

service life of the pavement structure. The climate file requires the following climate variables at

one-hourly time interval:

i. Temperature (°F or °C)

ii. Wind speed (mph or km/h)

iii. Relative Humidity (%)

iv. Sunshine (%)

v. Precipitation (in. or mm)

Therefore, the climate data obtained from the climate prediction models should be able to

provide the inputs mentioned above. Although various climate models have been developed, the

NARCCAP is the most suitable climate model for this study. NARCCAP climate data source

was selected because it consists of all the climate variables required for the climate files for the

software. Also, the future simulated climate data from the NARCCAP data source is at a 3-hr

interval. While, other data sources (MACA, CMIP5) have climate projections at daily

resolutions, and reducing it to hourly scale will further introduce uncertainty to the climate data.

Hence, NARCCAP is employed in this study. Some of the data parameters in NARCCAP need

to modify to suit the requirements of Pavement ME and discussed in later sections.

41

The data sources available were download based on the station of interest (latitude and

longitude values). Since this study is conducted for the state of Texas, several counties were

selected from the State of Texas, and their geographical location is shown in Figure 3-1. The

process of station selection and data reduction for use in Pavement ME is explained in detail in

the subsequent sections.

4.2 Climate Model Databases

NARCCAP is an international program used for creating high-resolution climate change

simulations. This program runs on A2 emission scenario from the SRES for 21st century

covering the Conterminous United States (CONUS) and most of Canada (22). In this program,

dynamic downscaling is done to produce the data at the local resolution of 31 x 31 miles (50 x 50

km). Dynamic downscaling uses high-resolution Regional Climate Models (RCMs), which are

driven by boundary conditions from GCMs (Trzaska et al., 2014).

4.2.1 Preparing Data for Pavement ME Software from NARCCAP Climate Data

Step 1: Climate Data Downloading and Conversion into Geographical Coordinate System

NARCCAP data sources are used to download the climate data for the station of interest

based on the latitude and longitude values (Geographical Coordinate System) (Mearns et al.,

2007). However, the climate variables available from various RCMs of NARCCAP use projected

coordinate systems (xc, yc) referred to as grid points, and data is stored in 'NetCDF’ format. Grid

cell maps are available for each RCMs on NARCCAP database. So, using the latitude and

longitude of the station and grid cell maps for the RCM, the grid points for each RCM can be

identified. These grid points are used to download the data for the following twelve climate

models: 1) CRCM-CCSM, 2) CRCM-CGCM3, 3) ECP2-GFDL, 4) ECP2-HadCM3, 5) HRM3-

HadCM3, 6) HRM3-GFDL, 7) MM5I-HadCM3, 8) MM5I-CCSM, 9) RCM3-CGCM3, 10)

RCM3-GFDL, 11) WRFG-CCSM, and 12) WRFG-CGCM3. The first term of the model

represents RCM while the second term represents GCM (Mearns et al., 2009). A MATLAB code

is developed to perform the above task. NARCCAP climate data simulations are available for a

42

current period of 1968-2000 and future simulation from 2038 to 2070. The downloaded climate

data needs to be converted into an acceptable climate file for the pavement software which is

explained in the following section.

Step 2: Developing Input Climate Data Required for Pavement ME Software

The data downloaded from NARCCAP is not readily useful for Pavement ME software.

Table 4-1 shows the characteristics of NARCCAP climate data and Pavement ME Design

software requirements. The next step is to convert the climate data to match the required ME

design software input variables. It is achieved in two stages: 1) Generation of Climate Variables,

2) Conversion from 3 hourly to 1 hourly Data.

Table 4-1 Characteristics of NARCCAP Climate Data and Requirements of Pavement ME.

NARCCAP Data Pavement ME

Data stored in ‘NetCDF’ format Data required in ‘.hcd.' format

3-hour interval 1-hour interval

Temperature (K) Temperature (°F or °C)

Zonal wind speed (uas, m/s-1) and Meridional wind

speed (vas, m/s-1) Wind Speed (mph)

Precipitation (kg m-2 s-1) Precipitation (in. or mm)

Specific humidity (kg kg-1), pressure (Pa) and

temperature (K) Relative Humidity (%)

Cloud Fraction (Not included in this study) Sunshine/Cloud Cover

Generation of Climate Variables: Precipitation and temperature required to be in

converted in the appropriate unit while the wind speed was estimated as the square root of

squares of zonal and meridional surface wind speed. The relative humidity (RH) is calculated

from specific humidity (q), surface atmospheric pressure (p) and air temperature (tas) as follows

(Meagher et al., 2012):

𝑅𝐻 = 100 × 𝑒

𝑒𝑠 (4-1)

e = q × p

0.622 (4-2)

43

es = 611 × e(

17.27×(tas−273.15)

237.3+(tas−273.15)) (4-3)

Where e is the vapor pressure and es is the saturation vapor pressure. Since the selected climate

models do not provide sunshine, the historical sunshine data of Pavement ME design software

was not modified.

Conversion from 3-Hourly to 1-Hourly Climate Data: Climate variables are reported

at the instantaneous time. For converting temperature from 3-hr to 1-hr, a linear trend is assumed

between two-time periods to get the temperatures at in between hours (Sharma et al., 2017). For

precipitation, relative humidity and wind speed, the values of these parameters are kept the same

for the three-time periods as suggested by Meagher et al. (2012).

After all the climate variables are converted to hourly scale, the existing hourly climate

database (.hcd) file is replaced with the new climate with the following format:

Date (YYYYMMDDHH), Temperature (F), Wind Speed (mph), % Sunshine, Precipitation

(inch), and Relative humidity (%).

4.2.2 Climate Data Plots

This section shows the climate parameters simulation for selected models and cities. The

contour plots shown in Figure 4-1 are the Mean Annual Temperature for Texas using ArcGIS

ArcMap. Figure 4-1a shows the temperature contours for Historical climate (Pavement ME

Design software) from 1979-2015. The climate model simulations from CRCM-CCSM and

CRCM-CGCM3 models are shown in Figure 4-1b and Figure 4-1c. The climate predictions with

CRCM-CCSM climate model (Figure 4-1b) show higher mean annual temperature for east of

Texas as compared with the historical trends (Figure 4-1a). The predicted Mean Annual

Temperatures depend on the climate model (CRCM-CCSM and CRCM-CGCM3) used for future

prediction.

44

Temperature

According to Schwartz et al. (2015), the most influential climate parameters that

influence pavement design is average annual temperature, while percent sunshine and wind

speed are moderately prominent. Figure 4.2 and 4.3 shows the variation in monthly mean

temperature and mean annual temperature for the selected cities in Texas corresponding to

eleven climate prediction models and Pavement ME historical climate. It is evident from the

plots that except for GCM- HADCM3 other climate models are predicting hotter summer and

colder winter for Texas. The temperature variation depends on the selected city in addition to the

climate model used. For El Paso, CRCM-CCSM climate model predicted a rise of 2.7 °F in the

month of April-June, whereas 13.5 °F rise in temperature is predicted for Corpus Christi.

Therefore, it can be stated that the temperature change is not uniform, and that is the reason

different regions were selected within the same state to perform the analysis. It is expected that

this change in climate parameters affects the pavement performance.

45

Figure 4-1 Mean Annual Temperature (a) Historical Climate (1979-2015) (b) CRCM-CCSM

Future (2038-2070) (c) CRCM-CGCM3 Future (2038-2070).

As shown in Table 4-2 HadCM3 climate model prediction for temperature are maximum

among all the selected models for all the cities except for Dallas where CRCM climate model

predictions are maximum. If we select the GFDL climate model for analysis, these climate model

predictions are minimum among all the climate models for all the selected cities.

Mean Annual Precipitation

Figure 4-4 shows the summary of mean precipitation of eleven climate prediction

models; every model predicts either an increase or decrease in precipitation for all the selected

cities in Texas. For Corpus Christi HADCM3 model precipitation prediction is very high when

(a) (b)

(c)

46

bias-correction is applied the precipitation prediction reduces significantly, and it is further

explained in subsequent sections.

As shown in Table 4-3, of all the selected climate models GFDL and CCSM predictions

are maximum and minimum among the selected climate models for Texas except for Corpus

Christi for which HADCM3 was predicting maximum, respectively.

Mean Annual Wind Speed and Relative Humidity

Similarly, Figure 4-5 and Figure 4-6 show the annual mean wind speed and relative

humidity, respectively. Future predicted mean annual wind speed is increased across all the

regions with ECP2-GFDL climate model predicting lower wind speed.

Wind speed climate predictions are maximum and minimum for CGCM3 and HadCM3

climate models among the selected models within Texas. Similarly, simulations for relative

humidity are maximum for HadCM3 and GFDL climate models while they are minimum for

CCSM climate models.

47

Table 4-2 Mean Annual Temperature.

Cities Minimum

Predictions

Maximum

Predictions

CRCM-CCSM Climate

Model for Analysis (°F)

Climat

e

Model

Temper

ature

(°F)

Climate

Model

Temper

ature

(°F)

Amarillo RCM3

-GFDL

53.4 HRM3-

HADCM3

64.1 61.1

Austin ECP2-

GFDL

60.1 MM5I-

HADCM3

70.6 70.4

Corpus

Christi

RCM3

-GFDL

64.9 HRM3-

HADCM3

75.2 72.7

Dallas ECP2-

GFDL

58.2 CRCM-

CCSM

70.4 70.4

El Paso ECP2-

GFDL

55.5 HRM3-

HADCM3

67.3 64.2

Fort

Worth

ECP2-

GFDL

58.4 MM5I-

HADCM3

70.1 69.1

Houston ECP2-

GFDL

61.8 MM5I-

HADCM3

72.1 71.7

Lubbock RCM3

-GFDL

55.3 MM5I-

HADCM3

65.6 63.3

Mc Allen RCM3

-GFDL

66.2 MM5I-

HADCM3

76.2 74.6

San

Antonio

ECP2-

GFDL

59.9 MM5I-

HADCM3

71.5 70.3

48

Table 4-3 Mean Annual Precipitation.

Cities Minimum

Predictions

Maximum

Predictions

CRCM-CCSM Climate

Model for Analysis (in.)

Climate

Model

Precipi

tation

(in,)

Climate

Model

Precipi

tation

(in.)

Amarill

o

WRFG-

CCSM

14.20 RCM3-

GFDL

29.30 17.67

Austin MM5I-

CCSM

17.33 RCM3-

GFDL

43.30 18.70

Corpus

Christi

MM5I-

CCSM

16.13 MM5I-

HADCM3

175.83 28.33

Dallas CRCM-

CCSM

20.90 RCM3-

GFDL

37.77 20.90

El Paso ECP2-

HADCM

3

8.20 RCM3-

GFDL

41.67 13.33

Fort

Worth

CRCM-

CCSM

19.90 HRM3-

GFDL

36.87 19.90

Houston MM5I-

CCSM

22.60 ECP2-

HADCM3

80.07 23.47

Lubboc

k

ECP2-

HADCM

3

15.17 HRM3-

GFDL

29.37 17.33

Mc

Allen

MM5I-

CCSM

10.67 RCM3-

GFDL

69.17 24.27

San

Antonio

MM5I-

CCSM

14.70 MM5I-

HADCM3

41.93 15.57

49

Figure 4-2 Monthly Mean Temperature of Some of the Texas Counties for all Climate Prediction Models (2030-2050).

50

Figure 4-2 (continued) Monthly Mean Temperature of Some of the Texas Counties for all Climate Prediction

Models (2030-2050).

51

Figure 4-2 (continued) Monthly Mean Temperature of Some of the Texas Counties for all Climate Prediction

Models (2030-2050).

52

Figure 4-3 Mean Annual Temperature of Cities of Texas for all Climate Prediction Models (2030-2050).

53

Figure 4-4 Mean Annual Precipitation of Cities of Texas for all Climate Prediction Models (2030-2050).

54

Figure 4-5 Mean Annual Wind Speed of Cities of Texas for all Climate Prediction Models (2030-2050).

55

Figure 4-6 Mean Annual Relative Humidity of Cities of Texas for all Climate Prediction Models (2030-2050).

56

4.3 Bias Correction Method

The model simulated climate data and the observed climate data for the historical period is

compared to evaluate the accuracy of the climate models. Figure 4-7 shows the temperature from

1996-1999 for CRCM-CCSM model-simulated and observed temperature (Pavement ME

Existing) for the same period. It is noticeable that there is an error (bias) within the model

simulation which should be considered for the future simulations too. Hence, the model

prediction should be corrected for the bias, and the same bias correction will be applied for the

future time period. The bias correction methods provide adjustment factors to minimize the

offset between the observed historical and climate prediction model data (Hempel et al., 2013).

This study uses the simple bias correction approach (Delta method) which corrects the projected

climate model data using the difference in the mean between observed, and climate predicted

data for the historical period. (Hawkins et al., 2013).

Statistical bias correction of simulation data is broadly applicable to the climate impacts

research since it has an advantage over raw climate model output while doing impact analysis

(Hempel et al., 2013):

i. Statistical bias correction methods help in comparing the observed and simulated effects

during the historical reference period and its transition into the future. Without such an

adjustment of the mean behavior in the historical period, future impacts cannot be

accurately described.

ii. Bias correction techniques are used for implicit downscaling of the simulated data to a

higher resolution of the observational data. While a simple interpolation to the finer grid

would not account for variability in higher resolution data, it can be achieved by a bias

correction method that adjusts the variance.

iii. Bias correction also serves as a way to improve the simulated climate data to the more

detailed information associated with observational data.

57

Figure 4-7 CRCM-CCSM Model Simulated and Observed Temperature (Pavement ME Existing)

Over the years.

4.3.1 Temperature Bias Correction

∆= 𝑻𝒐𝒃𝒔 − 𝑻𝒎𝒐𝒅 4-1

𝑻𝒃𝒊𝒂𝒔 = 𝑻𝒎𝒐𝒅 + ∆ 4-2

where:

𝑇𝑜𝑏𝑠 is the mean existing observed Temperature for the historical period.

𝑇𝑚𝑜𝑑 is the mean model simulated Temperature for the historical period.

𝑇𝑏𝑖𝑎𝑠 is bias corrected Temperature.

The same bias correction is applied to the future simulations.

4.3.2 Precipitation Bias Correction

∆= 𝑷𝒐𝒃𝒔/𝑷𝒎𝒐𝒅 4-3

𝑷𝒃𝒊𝒂𝒔 = 𝑷𝒎𝒐𝒅 × ∆ 4-4

where:

𝑃𝑜𝑏𝑠 is the annual mean existing observed Precipitation for the historical period,

𝑃𝑚𝑜𝑑 is the annual mean model simulated Precipitation for the historical period,

𝑃𝑏𝑖𝑎𝑠 is bias corrected Precipitation.

58

The Figures 4-8 and 4-9 show the adjusted mean temperature and precipitation for the

Fort Worth, TX. The data suggest that future models are predicting lower temperatures and

precipitations than the recorded temperature and precipitations. For all the selected cities

temperature prediction goes higher after bias-correction while precipitation they either go

high/low depending on the selected city and climate model. The bias-corrected plots for all other

cities are presented in Appendix A.

Figure 4-8 Bias Correction for Mean Annual Temperature Fort Worth, TX.

Figure 4-9 Bias Correction for Mean Annual Precipitation Fort Worth, TX.

59

4.4 Climate Change Parameters Summary

This section summarizes the detailed climate variations within the state of Texas. The

entire year is divided into four seasons to understand the changes in the temperature seasonally:

January to March (Jan-Mar), April to June (Apr-June), July to September (July-Sept) and

October to December (Oct-Dec). Figure 4-10 shows the probability of increase or decrease in

seasonal mean temperature. For Amarillo, out of eleven climate models, seven models predicted

an increase in temperature for Jan-Mar while four models predicted decrease compared to

existing temperature (Pavement ME climate), giving 64% chances of increase in Jan-Mar

temperature in Amarillo. Each scenario and climate model have equal chances of occurrence

(IPCC, 2012).

Figure 4-11 to 4-14 shows the quartile range plots for the mean annual temperature,

precipitation, wind speed and relative humidity, respectively. The boxplot for Amarillo, El Paso,

and Lubbock is comparatively short for mean annual temperature and precipitation which shows

that mean from twelve models are close to each other as compared to predictions from other

cities. Obvious difference between the boxplots for other cities is observed which suggests that

the climate predictions for cities vary. The four sections of the quartiles plots are uneven in size

which suggests that many variations occur within the climate model predictions for the same

city. The extremes of boxplot for Corpus Christi, Houston and McAllen are very high which

suggests the predictions varied among the same group. Boxplots for relative humidity are

comparatively short. Wind speed predictions for Corpus Christi and Houston are similar as their

boxplots are very small.

Figure 4-15 to 4-18 shows the range of mean annual temperature, precipitation, wind

speed and relative humidity, respectively. For McAllen, the predicted range of annual

temperature is more than the existing climate (Pavement ME Climate), while for El Paso and San

Antonio it is lower. For McAllen, the lower range of annual precipitation is higher than the

Pavement ME climate. The mid-range of annual wind speed prediction is close to the existing

60

climate for San Antonio, Houston, Lubbock and Fort Worth. Similarly, for relative humidity

Corpus Christi, Dallas and Houston are close to existing climate.

61

Figure 4-10 Probability of Increase or Decrease in Seasonal Mean Temperature.

62

Figure 4-11 Quartile Range Plot for Mean Annual Temperature.

63

Figure 4-12 Quartile Range Plot for Mean Annual Precipitation.

64

Figure 4-13 Quartile Range Plot for Mean Annual Wind Speed.

65

Figure 4-14 Quartile Range Plot for Mean Annual Relative Humidity.

66

Figure 4-15 Range of Mean Annual Temperature Compared with Pavement ME Climate.

67

Figure 4-16 Range of Mean Annual Precipitation Compared with Pavement ME Climate.

68

Figure 4-17 Range of Mean Annual Wind Speed Compared with Pavement ME Climate.

69

Figure 4-18 Range of Mean Annual Relative Humidity Compared with Pavement ME Climate.

70

Chapter 5 : Vulnerability Study of the Pavements

5.1 Pavement Performance Analysis

The pavement performance is evaluated for all the selected cities using the climate data

from twelve climate models. Although pavement design software predicts various performance

parameters, this study mainly focused on evaluating rutting potential and IRI. Based on the

literature review and discussions with TxDOT engineers, it was identified that maintenance

measures are taken when IRI exceeds 100 in./mile or rutting of more than 0.4 in. is observed or

both. Even though distress analysis of Fort Worth Pavement section is discussed here, a similar

analysis was performed for other cities and results are included in the appendices.

5.1.1 Influence of Climate Models on IH30 Frontage Road Pavement Performance

To evaluate the influence of climate change, the pavement design along with relevant

information included in Chapter 3 for IH30 frontage road (Fort Worth) was analyzed by

exposing pavement to future climate models. The Figure 3-2a pavement design was analyzed for

20 years of service life by subjecting the pavement to traffic conditions included in Table 5-1.

Table 5-1 Design Traffic for Pavement Section in Fort Worth, TX.

Highway Location IH 30 Frontage Road,

Tarrant County

Annual Average Daily Traffic (AADT)

Beginning

22,990

Annual Average Daily Truck Traffic (AADTT)/ Percentage of

Trucks

828/ 3.6%

Analysis Period 20 years

The results obtained from the ME Design software is shown in Figure 5-1 and Figure 5-2.

For comparison purposes, the results obtained from the historical climate data and various

climate models are included in the figures. Based on the criterion of 100 in./mile IRI, the

pavement will require maintenance after 9.5 years of service (Pavement ME Climate data) while

maintenance requirement will vary from 8.5 to 10 years of service depending on climate model

selected. If future climate follows CRCM-CCSM model, the pavement section will require

71

maintenance after 8.5 years of service. Regarding AC rutting (Figure 5.2), the various future

climate models predict the maintenance requirement after various 6 (CRCM-CCSM) to 20 years

(WRFG-CGCM3) of service based on a threshold value of 0.4 in. On the other hand, the

pavement requires maintenance after 15.8 years of service if Pavement ME climate data is used.

The analysis suggests that there is an influence of climate change and premature failure will lead

to maintenance earlier.

Table 5-2 and 5-3 show the summarized results from all the sections considered in this

study. When IRI is considered as the maintenance criteria, the performance of the pavement

section is reduced for any climate model selected for all the cities except San Antonio, Fort

Worth and Amarillo. However, with AC rutting as the maintenance criteria, the performance

reduces to only six years for Fort Worth, or it improves such that the pavements perform well for

the design service life of 20 years.

Figure 5-1 Influence of Climate Change on IRI (Fort Worth, Texas).

72

Figure 5-2 Influence of Climate Change on AC Rutting (Fort Worth, Texas).

Table 5-2 Range of Change in Maintenance Years for Selected Pavement Sections with IRI as

Maintenance Criteria.

Pavement Section, City Range of Maintenance year

with Climate Models (years)

Pavement ME

Climate (years)

FM 1926, McAllen 11.6-16.4 16.4

US 60, El Paso 7-12 12

SH 19, Paris 10-12 12

US 83, San Antonio 10.8-12.3 12.3

N_Mopac Expressway, Austin 8.8-11 11

US 175, Dallas 11-12.5 12.3

IH 30 Frontage Road, Fort

Worth

8.5-10 9.5

SH 197, Houston 6-8 8

SH 44, Corpus Christi 5.7-8 8

IH 40, Amarillo 4.7-7.4 6.8

73

Table 5-3 Range of Change in Maintenance Years for Selected Pavement Sections with AC

Rutting as Maintenance Criteria.

Pavement Section, City Range of Maintenance year

with Climate Models (years)

Pavement ME

Climate (years)

FM 1926, McAllen Beyond 20 years Beyond 20 years

US 60, El Paso Beyond 20 years Beyond 20 years

SH 19, Paris Beyond 20 years Beyond 20 years

US 83, San Antonio Beyond 20 years Beyond 20 years

N_Mopac Expressway, Austin 9.4- Beyond 20 years 14.9

US 175, Dallas Beyond 20 years Beyond 20 years


Worth

6- Beyond 20 years 15.8

SH 197, Houston 8-Beyond 20 years 16.9

SH 44, Corpus Christi 4.9-12 8.8

IH 40, Amarillo Beyond 20 years Beyond 20 years

5.1.2 Influence of Extreme Event on Pavement Performance

Although the majority of climate models predict enhanced frequency and intensity of

precipitation, the models are not capable of predicting extreme events like excessive rainfall

events of Texas (2015 and 2017, National Weather Service, NOAA). In the event of excessive

rainfall, the water table will rise and saturate the subgrade layer, thus, reducing the bearing

capacity of the subgrade layer and was also evaluated in this study. To identify the influence of

saturation, it is assumed that extreme rainfall will occur at an interval of 1, 2, 3, 5, 10, and 15

years of service life of the pavements. These extreme events will raise the water table to the top

of the subgrade layer, thus, saturating the subgrade layer. It was also assumed that the saturation

would last at a time for 7.5 days or 15 days or one month or two months after the occurrence of

these events. Since the focus was to evaluate the influence of saturation only, the historical

climate data files were used for the evaluation rather than future predictions and the water table

depth was raised for the specified period and duration. The influence of the rise in the water table

depth on the pavement distresses is summarized in Figures 5-3 through 5-5 for the Fort Worth

pavement section.

Since the Pavement ME provides AC rutting and total rutting of the pavement, the AC

rutting was subtracted from total rutting to obtain rutting of base and subgrade layers, which will

74

be expected due to saturation of subgrade layer. Even though saturation of subgrade is expected

to enhance pavement rutting, the combined base and subgrade rutting was minimally influenced

(Figure 5-3). Overall, the results suggest an increase in base and subgrade rutting (0.30 to 0.33

in.) due to a rise in water table. Similarly, the influence of water table rise on IRI is shown in

Figure 5-4. Although there is an influence of water table rise on service life pavement, the loss in

service life is nominal (less than 0.5 years).

The influence of saturation on the subgrade modulus is summarized in Figure 5-5. The

trend indicates that the modulus reduces with saturation. However, the subgrade modulus regains

some of the moduli after reduction in saturation (or drop in water table). The data suggest that

the subgrade modulus reduces from 4,000 psi to 3,000 psi at the end of the 20 years using

historical data. However, the modulus reduces to 2,500 psi when exposed to one extreme event,

which is significant. The drop-in modulus suggests that the bearing capacity of the subgrade

layer is reduced, however, its influence on IRI nominal. For the Fort Worth pavement section, it

can be concluded that the influence of saturation is minimal (less than eight months change in

maintenance). However, this was not true for other parts of the state like Corpus Christi, El Paso

where the soil is moisture dependent (Appendix B). The saturation of the subgrade layer reduces

the bearing capacity of soils, especially in Eastern Texas. The loss of modulus and recovery of

the modulus after reduction in saturation is as expected. However, permanent loss in the modulus

needs to be further evaluated to identify the reasons for the loss. If true, the level of reduction in

the modulus suggests that an increase in truck traffic will further enhance the damage of the

pavements. Although the analysis was performed for different saturation days (7.5 15, 30, 60

days), the results are only showed for 30 days saturation because the influence of saturation days

on performance was similar to the 30 days saturation. Table 5-4 shows the summarized results

for all the pavement sections selected for this study. For McAllen, Austin, Houston and El Paso,

the change in maintenance required are one year earlier with extreme events occurring, which

suggests that the effect of subgrade saturation on the performance of pavements is more for these

cities.

75

Figure 5-3 Influence of Extreme Events on Rutting (Base and Subgrade) of Fort Worth (Texas)

Pavement Section.

Figure 5-4 Influence of Extreme Events on IRI of Fort Worth (Texas) Pavement Section.

76

Figure 5-5 Influence of Extreme Events on Subgrade Modulus of the Fort Worth (Texas)

Pavement Section.


Maintenance Criteria

Pavement Section, City Maintenance year with

Extreme Event

Pavement ME

Climate (years)

FM 1926, McAllen 14.5 16.4

US 60, El Paso 7.8 12.0

SH 19, Paris 11.0 12.0

US 83, San Antonio 12.0 12.5

N. Mopac Expressway, Austin 8.8 11.0

US 175, Dallas 11.5 12.3


Worth

8.9 9.6

SH 197, Houston 6.5 8.0

SH 44, Corpus Christi 7.0 8.0

IH 40, Amarillo 5.5 6.8

77

5.1.3 Combined Influence of Extreme Event and Climate Change on Pavement

Performance

A worst-case scenario was also considered where the simulated future climate files were

used along with the extreme event (subgrade saturation due to rise in the depth of the water

table). The results of the analysis are shown in Figures 5-6 through 5-8. In the worst-case

scenario, the combined base and subgrade rutting is minimally influenced (0.33 to 0.335) while

IRI slightly changes requiring maintenance after 8.5 years of service in comparison 9.6 years

required with historical climate data. Figure 5-8 shows the change in the subgrade modulus, with

different scenarios. The combined effect of CRCM-CCSM climate model and change in water

table depth reduces the subgrade modulus significantly (from 4,000 to 2,000 psi after 20 years).

It is worth mentioning that the worst-case scenario may or may not happen in the future;

however, recent extreme events indicate the chances or worst case scenario are quite likely in the

future. The summarized results for all the pavement sections selected for this study are shown in

Table 5-5. Combined effects of climate change and extreme events is more on the pavement

performance than only one event occurring individually.

Figure 5-6 Influence of Extreme Events and Climate Change on Rutting (Base and Subgrade) of

Fort Worth (Texas) Pavement Section.

78

Figure 5-7 Influence of Extreme Events and Climate Change on IRI of Fort Worth (Texas)

Pavement Section.

Figure 5-8 Influence of Extreme Events and Climate Change on Subgrade Modulus of Fort

Worth (Texas) Pavement Section.

79



Pavement Section, City Maintenance year with

Extreme Event and Climate

Change

Pavement ME

Climate (years)

FM 1926, McAllen 11.0 16.4

US 60, El Paso 7.5 12.0

SH 19, Paris 10.5 12.0

US 83, San Antonio 11.5 12.5

N_Mopac Expressway, Austin 8.5 11.0

US 175, Dallas 11.0 12.3


Worth

8.5 9.6

SH 197, Houston 5.8 8.0

SH 44, Corpus Christi 5.8 8.0

IH 40, Amarillo 5.5 6.8

5.1.4 Influence of Climate Change on Selected Cities and Pavement Sections

In the end, the data obtained from performing analysis of all the pavement sections

(Chapter 3) is summarized in Figures 5-9 through 5-11. The data shows the IRI (top) and AC

rutting (bottom) variation with the use of various future climate models. The negative percentage

change indicates values lower than the Pavement ME climate data and vice versa. Similarly, the

average, high, low values indicate maximum, average, and minimum values obtained from all

the climate model predictions.

The IRI and AC rutting obtained for all the pavement sections is summarized in Figure 5-

9. The data suggest that maximum percentage change in IRI can be observed in the El Paso

pavement section (33.1%) while minimal percentage change in IRI can be observed in Paris

pavement section (6.5%). Although some of the pavement sections had negative percentage

change (indicating IRI change due to climate change is less than obtained from Pavement ME

climate), the negative percentage change was minimal (less than 2.5%). If average change is

taken into consideration, the average change is from 2.6% (San Antonio) to 17.3% (El Paso).

80

The figure also suggests that the influence of a change in climate is minimal in Paris and

maximum in El Paso because of the variation in the range of predicted IRI.

The data suggest that maximum percentage change in AC rutting can be observed in the

Paris and Fort Worth sections (around 42%) while minimal percentage change in IRI can be

observed in El Paso pavement section (6.5%). Similarly, some of the pavement sections had

negative percentage change (indicating AC rutting change due to climate change is less than

obtained from Pavement ME climate), the negative percentage change was maximum for El Paso

pavement section (-42.3%), and minimal was for Corpus Christi and Dallas pavement sections

(8.8%). If average change is taken into consideration, the average change is from -0.5% (Austin)

to 16% (Fort Worth). The figure also suggests that the influence of a change in climate is

minimal in Amarillo and maximum in Paris because of the variation in the range of predicted AC

rutting.

One of the observations from the analysis is an inconsistency between IRI and AC rutting

predictions. For instance, the IRI change is higher, and change is positive while AC rutting

change is negative indicating that AC rutting is not the factor contributing an increase in IRI.

Since IRI includes other factors like cracking, the contribution is from other sources rather than

AC rutting.

The influence of extreme events on pavement sections is included in Figure 5-10, and the

results are like the ones summarized in Figure 5-9. The change in IRI is maximum for El Paso

while the influence of extreme event is minimal for Paris. Regarding rutting, the change in

combined base and subgrade rutting is minimal for Paris and maximum for El Paso indicating

that the influence of extreme event is significantly higher for El Paso and minimal for Paris.

Similar trends are observed when combined with the influence of extreme event and climate

change on pavement sections is analyzed (Figure 5-11). The results indicate that the influence of

climate and extreme events is localized.

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Figure 5-9 Observed IRI and AC Rutting Variation (Various Cities in Texas).

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Figure 5-10 Observed IRI and Base + Subgrade Rutting Variation (Various Cities in Texas).

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Figure 5-11 Observed IRI and Base + Subgrade Rutting Variation (Various Cities in Texas).

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5.1.5 Influence of Geographical Location on Pavement Performance

To document variation in pavement performance under different climate conditions, the

El Paso pavement section (IH 10 Frontage Road) as shown in Figure 3-2c was subjected to

climate conditions of different geographical locations within Texas and the influence of climate

change on performance is discussed below. Four pavement distresses (IRI, Fatigue Cracking, AC

rutting, and Base and Subgrade rutting) are compared. All the distresses are compared with the

performance of pavements projected with historical climate data (Pavement ME Climate).

Dallas

The predicted pavement distresses for the city of Dallas are summarized in Figure 5-12.

Most of the climate models enhanced AC layer rutting, fatigue cracking, leading to higher IRI.

However, the influence of climate change on base and subgrade rutting seems to be minimal.

The average increase in IRI, AC layer rutting, fatigue cracking, and combined base and subgrade

rutting is 5.5%, 6.8%, 29.4%, and 3.2%, respectively. The data seems to indicate that Dallas is

expected to see a higher increase in temperature as compared to historically recorded.

Amarillo

The percent change in distresses for the pavement section subjected to Amarillo climate

condition is shown in Figure 5-13. The pavement analysis runs with RCM3 model show an

increase in IRI, deformation in combined base and subgrade and top-down fatigue cracking

except AC rutting (which decreases). Runs with the other eight models show an increase in

pavement distresses. The average change in distresses is 4.8%, 6.6%, 32%, and 2.9% for IRI, AC

permanent deformation, fatigue cracking, and base and subgrade deformation, respectively.

Corpus Christi

The distresses observed when the pavement section exposed to Corpus Christi climate is

shown in Figure 5-14. The average increase in distresses is 4%, 12%, 25%, 5% for IRI, AC

rutting, fatigue cracking, and combined base and subgrade rutting, respectively. Even though the

trends show an increase in all distresses but there is a considerable variation in the projection of

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fatigue cracking. If exposed to the HRM3-GFDL climate model, there is no change in fatigue

cracking whereas the influence of MM5I-HADCM3 model is higher (65% increase). Designers

should consider this variability before considering any adaptation technique to mitigate the

influence of climate change.

Figure 5-12 Influence of Dallas Climate on Performance of El Paso Pavement Section.

El Paso

The observed for El Paso are summarized in Figure 5-15. Compared to other cities, the

IRI is increasing in El Paso by more than 5%. Some models predicted a decrease in AC rutting

and fatigue cracking and an opposite observation in other models. Due to this variation, the

average values are showing no significant difference from historical performance.

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Figure 5-13 Influence of Amarillo Climate on Performance of El Paso Pavement Section.

Figure 5-14 Influence of Corpus Christi Climate on Performance of El Paso Pavement Section.

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Figure 5-15 Influence of El Paso Climate on Performance of El Paso Pavement Section.

Similarly, Figure 5-16 to Figure 5-21 shows the analysis for San Antonio, Austin, Fort

Worth, Houston, Lubbock and McAllen, respectively. For McAllen and Fort Worth, all the

models show an increase in distresses. It can be concluded that same pavement section which

works well with climate change for El Paso might not perform at the same level as that at

McAllen. As the climate prediction depends on geographical location, the influence of climate

on pavement will also depend on the geographical location.

The range of percentage change in distresses is shown in Figure 5-22 that suggests that

the predicted stresses on an average are higher with a change in climate. The distresses can be as

high as +60% (AC rutting as per McAllen) and are as low as -30% (AC rutting as per Houston).

Therefore, both expert opinion and engineering judgment are required in choosing the

appropriate climate model and mitigation strategies in pavement design. Since the variation is

significant, there is a need for inclusion of probability in decision making and will be discussed

in later chapters.

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Figure 5-16 Influence of San Antonio Climate on Performance of El Paso Pavement Section.

Figure 5-17 Influence of Austin Climate on Performance of El Paso Pavement Section.

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Figure 5-18 Influence of Fort Worth Climate on Performance of El Paso Pavement Section.

Figure 5-19 Influence of Houston Climate on Performance of El Paso Pavement Section.

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Figure 5-20 Influence of Lubbock Climate on Performance of El Paso Pavement Section.

Figure 5-21 Influence of McAllen Climate on Performance of El Paso Pavement Section.

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Figure 5-22 Influence of Geographical Location on Performance of El Paso Pavement Section.

(a) IRI (b) Permanent Deformation AC

(c) Fatigue Cracking (d) Base+Subgrade Deformation

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5.2 FPS-21 Analysis

Even though Pavement ME design software was used for climate analysis, the TxDOT

uses FPS 21 design software for pavement design. The software has divided Texas, based on

historical climate data, in different climate region and performs pavement design based on

climate region, traffic, material properties, among others. To compare the influence of climate

change on pavement designed, a pavement (Figure 5-23) was designed for Fort Worth, TX using

FPS 21 and the same design was analyzed using Pavement ME Design software. FPS 21 uses

Pavement Serviceability Index (PSI) for maintenance while Pavement ME Design uses IRI as

one of the failure criteria. As per W.D.O. Paterson, the PSI of flexible pavements can be

estimated from IRI (m/km) using equation 5-1:

𝑷𝑺𝑰 = 𝟓 × 𝒆−𝑰𝑹𝑰

𝟓.𝟓 5-1

Using PSI = 2.5 in FPS as the criteria for the pavement maintenance, the pavement

section (Figure 5-23) will last for 33.7 years. Similarly, with Pavement ME, the failure criterion

is IRI = 172 in./mile, it can be seen that the pavement will last beyond 20 years (Figure 5-24).

Since IRI = 100 in./mile (PSI = 3.8) is considered as the maintenance criteria, the

pavement will last for 20.7 years with an overlay of 2 in. provided after 13.5 years (Figure 5-23)

of service. Similar design evaluated using Pavement ME Design Software identified that the

pavement is required to have maintenance after 9.1 years of service when existing climate

condition (Figure 5-24) data is used. This comparison suggests that there is a difference of 4.4

years between the two design methods and can be attributed to models of Pavement ME Design

that may not be calibrated for Texas materials and climate conditions. The difference can also be

due to the approximation of conversion from IRI to PSI. To better compare the results obtained

from two design methods, it was decided to use IRI obtained from maintenance years and then

compare the results. A similar approach will be used for comparing AC rutting results as well.

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Figure 5-23 Pavement Section using FPS 21.

Figure 5-24 FPS Pavement Design with Maintenance at IRI of 100 in./mile.

The performance of the pavement section estimated by Pavement ME is shown in Figure

5-25 for historical climate data as well as future climate (CRCM-CCSM) data. As per FPS 21,

the maintenance is required after 13.5 years of service, which is corresponding to IRI of 120

in./mile for existing climatic conditions (Pavement ME Climate) while for future climate

(CRCM-CCSM) it will change to 12.3 years for an IRI of 120 in./mile. If IRI of 100 in./mile

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(Pavement ME Design) is considered for preventive maintenance, the maintenance of the

pavement is required after 8.3 years of service with a change in climate in comparison to 9.1

years with historical data. Even though the predicted service life and maintenance requirements

are different among evaluated design methods, the change in climate reduces the service life

leading to earlier maintenance requirement.

The AC rutting obtained from Pavement MD design is shown in Figure 5-26. A threshold

value of 0.4 in. is observed after 20 years of service (historical climate data) and a similar level

of rutting is observed after 10.8 years of service if CRCM-CCSM climate data is used. Since FPS

21 predicted maintenance after 13.5 years of service, the rutting level of 0.32 is observed after

13.5 years of service if historical climate data is used. A similar level of rutting is observed

within 6.75 years of service if CRCM-CCSM climate data is used.

The analysis suggests that the Pavement ME Design method predicts less service life in

comparison to FPS 21. However, the FPS 21 doesn’t take into consideration change in climate,

and the pavement designed using FPS 21 may require maintenance earlier than anticipated.

Figure 5-25 IRI of the Pavement Section over the Years.

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Figure 5-26 AC Rutting of the Pavement Section over the Years.

5.3 Adaptation Methods

One of the objectives of this study is to propose adaptation strategies for mitigating the

influence of climate. Since climate is regional, the climate adaptation strategies will be different

for different geographical locations even in places few hundred miles apart. To exemplify, the

pavement designs used in previous sections were modified and evaluated to estimate resilience

enhancement. To withstand changing climate either pavements need to use high-performance

materials or enhance layer thicknesses or both. The following strategies to minimize the

influence of climate change have been proposed that can enhance the resiliency of the

pavements.

5.3.1 Adaptation Methods for Climate Change

5.3.1.1 Increasing the Thickness of AC Layer

Adaptation approach is required to enhance resiliency by mitigating the influence of

future climate. One of the adaptation strategies is to increase the thickness of the AC layer. For

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the pavement section in Fort Worth, Texas the thickness of the AC layer is increased by 1 in. As

it can be seen in Figure 5-27, the increase in the thickness of the AC layer improves the

performance of pavement section (IRI). In the plot, CC refers to climate change for CRCM-

CCSM climate prediction models. With the historical climate and design, the section was

required to have maintenance after 9.1 years while if the climate change occurs the performance

of the section will decrease, and maintenance is required after 8.3 years. If the climate change is

considered during design and construction, the pavement section will require maintenance after

9.4 years similar to the one observed with historical climate data.

Figure 5-27 Change in Performance of Pavements with Increase in AC Thickness.

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Pavement Section,

City

Maintenance year with

Climate Models (years)

Maintenance Year with

Adaptation (years)

Pavement

ME Climate

(years)

FM 1926, McAllen 11.6 14.0 16.4

US 60, El Paso 7.0 9.0 12.0

SH 19, Paris 10.8 11.9 12.0

US 83, San Antonio 10.8 12.0 12.5

N_Mopac Expressway,

Austin

10.0 11.0 11.0

US 175, Dallas 11.5 12.3 12.3

IH 30 Frontage Road,

Fort Worth

8.3 9.4 9.1

SH 197, Houston 6.8 7.9 8.0

SH 44, Corpus Christi 6.8 8.0 8.0

IH 40, Amarillo 5.0 5.0 6.8

5.3.1.2 Binder Change

Changing the Performance Grade (PG) of the binder also enhances the performance of

the pavement sections. The change in IRI of the pavement sections for different binder types is

shown in Figure 5-28. If the binder grade is changed from PG 70-22 to PG 76-22 for CRCM-

CCSM climate model, the maintenance year changes from 8.3 to 8.8 years.

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Figure 5-28 Change in Performance of Pavements with Changing Binder Type.



Pavement Section,

City

Maintenance year

with Climate Models

(years)

Maintenance Year

with Adaptation

(years)

Pavement ME

Climate (years)

FM 1926, McAllen 11.6 13.4 16.4

US 60, El Paso 10.0 10.2 12.0

SH 19, Paris 10.8 10.8 12.0

US 83, San Antonio 10.8 10.8 12.5

N_Mopac

Expressway, Austin

10.0 10.0 11.0

US 175, Dallas 11.5 11.5 12.3

IH 30 Frontage Road,

Fort Worth

8.3 8.8 9.1

SH 197, Houston 6.8 7.5 8.0

SH 44, Corpus Christi 6.8 6.8 8.0

IH 40, Amarillo 5.0 5.0 6.8

5.3.1.3 Changing Mix Type

Similarly, the third adaptation option is to change the mix-type of the AC layer. Table 5-2

shows the AC mix types typically used along with reference to the mix specifications used by

TxDOT. The AC Rutting and IRI distress of the selected pavement section over the years with

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changing the material types for the AC layer are shown in Figure 5-29 and Figure 5-30,

respectively. Type C and Type D (Item 340,341) behave similarly while using SP mix

(performance mix) will improve the performance of the pavement section.

Table 5-8 Asphalt Mix Type Specifications.

Item Type Specifications

Dense-graded hot mix asphalt (Item 340, 341)

Type C, Type D Texas Department of Transportation

(TxDOT) “ Standard Specifications

for Construction and Maintenance of

Highways, Streets, and Bridges”

Stone Matrix Asphalt (SMA) (Item 346)

Superpave Mix- Coarse (SP-C) or Performance Mix

(Item 344)

Figure 5-29 AC Rutting of the Pavement Section for Different Material Types.

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Figure 5-30 IRI of the Pavement Section for Different Material Types.

Changing the material type also changes the performance of the pavements in FPS 21.

The performance is measured in a number of years pavements will perform well for PSI of 2.5 as

shown in Figure 5-31.

Figure 5-31 Change in Design Life of the Pavement Section with Changing Asphalt Material.

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As per FPS, Item 340, 341 (Type D or Type C) will give similar design, similarly using

properties for Type C or Type D in Pavement ME Design, the similar behavior is observed.

Performance mix (SP mix/ Item 344) in FPS, increases the design life by five years, similarly, in

Pavement ME, SP mix improves the performance as well. The SMA mix (Item 346) design

improves the design life in FPS, while the opposite similar behavior was observed with

Pavement ME predictions. This may be due to the dynamic properties of the SMA mix

considered in Pavement ME Design software.

5.3.1.4 Increasing the Thickness of AC Layer and Changing Binder Grade

Changing the Performance Grade (PG) along with layer thickness further improves the

performance of the pavement sections as well. If the binder grade is changed from PG 70-22 to

PG 76-22 and the increase in thickness by one inch for CRCM-CCSM climate model, the

maintenance year change from 8.3 to 9.8 years (Figure 5-32).

Figure 5-32 Change in Performance of Pavements with Binder Grade Change and Increase in AC

Thickness

5.3.2 Adaptation Methods for Extreme Event

The extreme rainfall raises the water table depth that saturates the subgrade. One of the

adaptation methods to improve the pavements subgrade against the extreme event is to improve

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the quality of the subgrade. In this study, it can be observed from Figure 5-33, by enhancing

subgrade quality (higher modulus) from 4.5 ksi to 6 ksi, the service life improves.

Figure 5-33 Adaptation against Extreme Event in the Subgrade.

5.3.3 Adaptation to Extreme Events and Climate Change

In the case of extreme events (considering changing climate along with moisture damage)

the adaptation is shown in Figure 5-34. The increase in the thickness of the AC layer along with

improving the subgrade quality improves the performance of the pavement sections leading to

resiliency in the event of a change in climate.

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Figure 5-34 Mitigation Approach to Minimize Premature Pavement Failure (due to change in

temperature and precipitation).

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Chapter 6 : Economic and Environmental Analysis

This chapter discusses the economic and environmental benefits of developed resilient

pavement structure design to withstand the climate change. The analysis is demonstrated using a

section for Fort Worth (Figure 3-2). The following are the steps performed:

Pavement performance is analyzed using the future climate data.

If the performance deteriorates an alternate design is developed using future climate

Evaluating the benefit (both costs and emissions) due to a modified design

Also assessing the pavements if pavement design is modified and climate change did

not occur

The future climate is estimated using CRCM-CCSM climate model. As explained in

Chapter 5 the pavement failure is evident using future climate scenarios based on pavement

distresses like IRI, rutting in the pavement. Cost-benefit analysis is done for the following

scenario:

(i) ‘Design A’: Existing design using Pavement ME Design Software with existing

historical climate. (Figure 6-1a)

(ii) Existing design analyzed using future climate data: Reduced life of ‘Design A’

due to climate change. (Figure 6-1b)

(iii) An alternate design is developed using the future climate data (Design B), such

that the design is more resilient to potential climate change. (Figure 6-1c)

(iv) An adapted design against climate change but the performance is analyzed using

historical climate data considering the fact that climate change does not occur.

(Figure 6-1d)

Due to an increased rate of deterioration, the pavement needs (Design A) early

maintenance. Reduced Life of ‘Design A’ Due to Climate Change (Figure 6-1b) displays the

maintenance requirements of the design developed with historical weather data and performance

is according to the changing climate. Now the highway requires early maintenance at the 8th

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year instead of a 9th year. An alternate ‘Design B’ was developed that takes into account new

climate data (CRCM-CCSM) during initial construction. Except for the climate data, all other

inputs remained the same as the design in ‘Design A.' The significant difference between the two

designs is the layer thicknesses. Since the primary climate stressor is temperature rise which

softens the asphalt and leads to accelerated failure, the thickness increase in asphalt layer is

required to improve the performance of the design.

Constructing a highway according to the new design comes at a higher price (considering

thickness change as adaptation method). It is logical to adopt the new design (Figure 6-1c Design

B and Design C) over the traditional design (Figure 6-1a Design A) if the new design outsmarts

the other in performance and yields greater benefits. The cost-benefit analysis is done by

studying the mean savings in costs and reduction in emissions. Estimating the expenses and

emissions incurred due to the pavements in their lifetime, we can compare both the designs.

Figure 6-1 Design for Cost-Benefit Analysis (a) Design A (ME Design) (b) Reduced Life of

Design A (ME Design) due to Climate Change (c) Climate Adopted Design (Design B) to meet

Design Life (d) Design B with Historical Existing Climate Data.

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In this study, Life Cycle Cost Analysis (LCCA) tool for appraising the costs and Life

Cycle Assessment (LCA) tool for estimating emissions for the life of the pavements are used.

Figure 6-2 shows the various phases of pavements and how costs and emissions were evaluated.

Figure 6-2 Phases in Life Cycle of Pavements, Models used for LCCA and LCA.

6.1 Cost Analysis

Primarily there are two costs associated with highways, 1) agency costs (costs incurred

by a highway agency for constructing and maintaining a highway), 2) user costs (costs incurred

by users of the road). It is a common practice in the US to perform LCCA for selection of

alternative designs. FHWA developed an LCCA tool named “Realcost2.5” (FHWA’s Life Cycle

Cost Analysis software). This tool considers the overall costs of the pavement and transforms

them into net present worth (NPW).

In this study, agency costs for construction and maintenance of the designs shown in

Figure 6-1 were calculated and transformed to NPW. The future costs are considered as constant

dollars (inflation not considered) as per the recommendation of LCCA Primer FHWA. The

future expenses are converted to present worth at a discount rate of 4.0% the widely used rate in

the US (Rangaraju et al., 2008).

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The unit costs for materials and construction were taken from Low Bid Prices of El Paso

(TxDOT’s Average Low Bid Unit Prices) and RS Means (2012). If the design life is beyond 20

years, then remaining service life is converted into salvage value and considered as a negative

cost. The primary concentration of LCCA (Realcost2.5) of highways is towards agency costs,

and there is an option to calculate additional costs (due to traffic delays) incurred to road users

during maintenance operations. TxDOT (Texas Department of Transportation) releases unit user

delay costs annually, and costs as per 2014 are: for a car are $21.73($/Vehicle hr) and for a truck

are $31.71($/Vehicle hr). These prices are modified to 2017 using consumer price index (CPI) of

2014 and 2017.

The duration for initial construction is considered as zero in RealCost2.5 such that user

costs during initial construction are not accounted. A similar assumption was made by Wimsatt

et al. (2009). There are other user costs which are dependent on pavement performance that

Realcost2.5 ignores like vehicle operating costs (fuel consumption, tire wear, maintenance, and

repair, etc.), accident costs, etc. Chatti.K and Zaabar.I (2012) presented the list of highway user

costs.

It is during the use phase where the users expend more since this phase is the longest

phase in a pavement life cycle. A small deviation in the performance of the pavement will cause

a significant impact on user costs. In simple terms, a well-performing pavement causes less user

costs than the poor performing one. Since each pavement design has different performance

characteristics, the impact on the road users is different.

In this study, the three user costs (fuel consumption, tire wear, and maintenance and

repair (M&R) costs) were estimated. These three costs are the function of rolling resistance of

pavement. According to FHWA's Towards Sustainable Pavement Systems reference document,

the rolling resistance is a result of pavement roughness, macro-texture, and structure

responsiveness. However, there are no available validated models for predicting structure

responsiveness and its exact role in rolling resistance. Thus, it was ignored in this study.

Similarly, the impact of macro-texture is statistically insignificant for all vehicle class except for

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trucks traveling at lower speeds (35 mph) (Van Dam, 2015). In general, vehicles on highway

travel at higher speeds and one can ignore the impact of macro-texture too.

The tremendous research effort has been focused on estimating the impact of roughness

on user costs. In this study, the Highway Development and Management System (HDM) models

developed by the World Bank were utilized. The reasons for choosing them are that these models

are the latest ones which are available, they are calibrated to the US pavement conditions, and

these models can include different classes of vehicles. The complete details of these models were

available in NCHRP 720 (Chatti and Zabbar, 2012). The unit costs (fuel, tires, and maintenance

costs) mentioned in the NCHRP 720 were for 2011, the prices were updated to 2017 using CPI.

Table 6-1 shows the estimated costs for all the design shown in Figure 6-1. The current

design and assessment are performed for an AADT of 22,990. In this assessment, each car is

assumed to travel 5 miles per day like trucks. However, the average miles traveled by a

passenger car in the US is 11,240 miles per year (US Department of Energy, Alternative Fuels

Data Center) and average miles travel per day is around 35 miles in a personal vehicle (Bureau

of Transportation Statistics. US States Department of Transportation).

Comparing ‘Design A’ (designed and performance predicted using historical climate data)

with Reduced life of ‘Design A’ (performance predicted using CRCM-CCSM climate data)

The user and agency cost for ‘Design A’ (Designed and performance predicted using

historical climate data) are $9.52 and $758.08 million. While, for Reduced Life Design A using

CRCM-CCSM climate model it is $9.71 and $ 760.04, respectively. So, if climate change occurs

and the design is not modified the agency will spend $190,000, and users will spend $1,960,000.

Comparing ‘Design A’ (designed and performance predicted using historical climate data)

with ‘Design B’ (designed and performance predicted using CRCM-CCSM climate data)

The first row of Table 6-1 shows the agency and user cost for ‘Design A’ as $9.52 and

$758.08 million. The third row of Table 6-1 shows the costs for ‘Design B’ which is the CRCM-

CCSM climate model adapted design and its agency and user cost are $9.79 and $758.83 million,

respectively. By comparing both the design the agency spends $270,000 (cost) more for

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constructing the road and the user’s cost is reduced by $750,000 (benefit). Even if climate

change didn’t occur and adopted design (fourth row in Table 6-1) is considered agency is

spending only $10,000, and this is because the adopted design performs well and last for longer

time. But, the users will also have a benefit of $3,270,000 because of the improved performance

of the climate change adopted design.

Table 6-1 Life Cycle Costs for Agency and User.

Design Units Agency

Costs

Fuel

Consumption

Costs

Tire

Wear

Costs

M&R

Costs

Total

User

Costs

Design A (Designed

and performance

predicted using

historical climate

data)

Costs

in

million

dollars

$9.52 $569.98 $46.35 $141.75 $758.08

Reduced Life Design

A (ME Design) using

CRCM-CCSM

climate model

$9.71 $571.09 $46.40 $142.55 $760.04

CRCM-CCSM

climate model

adopted design

(Design B)

$9.79 $570.52 $46.37 $141.94 $758.83

Design B with

historical, existing

climate data

$9.53 $567.35 $46.32 $141.14 $754.81

The impact of an increase in traffic on the benefits is illustrated by considering AADT

from 20,000 to 100,000 is shown in Figure 6-3. Climate change adaptation has benefits even at

lower traffic (20,000), but the magnitude of benefits is higher for heavier traffic (100,000).

Some earlier studies (Buttlar et al., 2015) considered user costs for passenger cars for

12,000 miles traveled per year, while estimated agency costs for one-mile length. This means

each passenger car is going 33 trips per day on one mile. This approach yields a very high user

cost benefit. Therefore, agency cost is estimated for one-mile of stretch and variation of the

benefit-cost ratio is demonstrated in Figure 6-4 with changing the miles traveled by the

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passenger cars per day for one- mile length. The AADT of 22,990 and miles traveled by trucks

as 5 miles are kept constant.

Figure 6-3 Range of Benefit with Increasing Traffic.

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Figure 6-4 Range of Benefit with Increasing Traffic.

6.2 Emission Estimation

In this study, the life cycle assessment (LCA) tool for estimating the emissions (Global

Warming Potential) throughout the life of a highway was utilized. LCA was performed

according to the guidance of International Organization of Standardization (ISO) documents

14040 (2006) and 14044 (2006). LCA is emissions accounting method which needs various

models and inputs from various sources. In this study, the models which are available to highway

officials at no cost were selected. The details of the models and data sources are portrayed in

Table 6-2.

The estimated greenhouse gases across all phases were considered in detail and converted

into GWP using the characterization factors provided by the US EPA (Global Emissions, 2014).

Even though there are other emissions apart from GHGs, this study only presented GWP due to

the fact emissions scenarios (SRES) developed for future climate models are based on GHGs.

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Comparing GWP ‘Design A’ (designed and performance predicted using historical climate

data) with Reduced life of ‘Design A’ (performance predicted using CRCM-CCSM climate

data)

The user emissions for ‘Design A’ (Designed and performance predicted using historical

climate data) are 2,286,641 tons of CO2e. While for Reduced Life Design A using CRCM-

CCSM climate model it is 2,291,347 tons of CO2e. So, if climate change occurs and the design is

not modified the emissions will go up by 4,506 tons of CO2e.

Comparing GWP Reduced life of ‘Design A’ (performance predicted using CRCM-CCSM

climate data) with ‘Design B’ (designed and performance predicted using CRCM-CCSM

climate data)

The second row of Table 6-1 shows the emissions for ‘Design A’ are 2,291,347 tons of

CO2e. The third row of Table 6-1 shows the emissions for ‘Design B’ which is the CRCM-

CCSM climate model adapted design as 2,288,702 tons of CO2e. By comparing both the design’s

the emissions, savings are 2,645 tons of CO2e. There will be emissions savings of 3,417 tons of

CO2e even if climate change doesn’t occur and design is modified because of the improved

performance of the climate change adopted design.

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Table 6-2 Models and Sources for LCA.

Phase Model Used Sources Remarks

Material

Production

Customized emissions model is

developed using Greenhouse Gases,

Regulated Emissions, and Energy Use in

Transportation Model (GREET)

Asphalt & Emulsion: Life Cycle Inventory Bitumen (Blomberg

et al., 2011)

Aggregate: Greenhouse Gas Emissions Inventory CEMEX

Jesse Morrow Mountain Plant (Downey 2009)

Asphalt Plant Operations: Hot Mix Asphalt Plants Emission

Assessment Report (US EPA 2000) (Myers et al., 2000)

Fuel Combustion factors’. E. Office of Air Quality Planning

and Standards, Emissions Factors & AP 42

The mix designs for materials like asphalt

concrete; lime stabilized base was

considered as per Texas commonly used

proportions. The portions of materials

remained the same for all the designs

Transportation Customized emissions model is

developed using Greenhouse Gases,

Regulated Emissions, and Energy Use in

Transportation Model (GREET)

A 20-ton capacity truck with full front haul and empty back haul

is assumed with fuel (diesel) consumption of 5.3 miles per

gallon. Diesel is considered the fuel used in trucks for

transporting materials

Transportation distance of raw materials

like (asphalt, lime, etc.,) to a plant or

construction site 50 miles and 12 miles

considered as the distance between asphalt

plants to the construction site

Construction The emissions during construction from

equipment and machinery were

estimated using the NONROAD 2008

database

The type of construction equipment and working durations are

estimated by using RSMEANS 2012. The equipment details

were taken from common construction equipment manufactures

like Caterpillar, Dynapac, Bomag, Roadtech, Wirtgen, etc. The

emissions data for equipment (machinery) available in

NONROAD 2008 is matched with construction equipment on

the basis of horse power

After estimating the emissions from each

equipment per hour, the total impacts is

calculated by multiplying machinery hours

required for activity (i.e., asphalt concrete)

and time efficiency

Use Models reported in NCHRP 720 for

estimating vehicle operating costs were

used to calculate the gasoline and diesel

consumption, and tire wear.

Tire: Typical tire manufacturing LCA is developed by using

Life Cycle Assessment of a Car Tire Continental (Kromer et al.),

Emission Factor Documentation for AP-42 Manufacture of

Rubber Products, Tyre LCCO2 Calculation Guidelines.

Emissions from Cars: Greenhouse Gas Emissions from a

Typical Passenger Vehicle

Emissions from Trucks: Developed emissions from a truck

with varying mileage using GREET

The emissions for manufacturing a

passenger car is estimated first, and later

the emissions for trucks are estimated

based on the proportions of materials used

in manufacturing truck and a passenger car

tire

Maintenance For materials production and

transportation, the methods explained

above are followed. For estimating the

emissions due to traffic delays during

maintenance is estimated using Motor

Vehicle Emission Simulator

For traffic delay emissions method proposed by Inti et al., 2016

is employed which used MOVES

Traffic delay emissions depend on the

timing of maintenance and in this study we

considered maintenance from 9AM-5PM

by closing one lane because this working

time has a maximum impact on the

environment

114

Table 6-3 User Emissions.

Design Units Fuel Consumption

Emissions

Tire Wear

Emissions

Emissions during

Use Phase

Design A

(Designed and

performance

using historical

climate data)

Global

Warming

Potential

(GWP)

CO2e in

tons

2,258,485 28,356 2,286,841

Reduced Life

Design A (ME

Design) using

CRCM-CCSM

climate model

2,262,960 28,387 2,291,347

CRCM-CCSM

climate model

adopted design

(Design B)

2,260,333 28,369 2,288,702

Design B with

historical,

existing climate

data

2,255,091 28,333 2,283,424

The impact of an increase in traffic on the benefits is illustrated by considering AADT

from 20,000 to 100,000. Figure 6-5 shows the emission savings of climate change adaptation on

traffic. Similarly, the effect of passenger car travel per day on emission savings is shown in

Figure 6-6. AADT of 22,990 and miles traveled by trucks as 5 miles is constant. If the passenger

car travel is more distance in one the saving in GWP is going up from 1000 to 7000 CO2e of tons

for one mile in length.

115

Figure 6-5 Range of Emission Savings with Increasing Traffic.

Figure 6-6 Range of Emission Savings with Increasing Traffic.

116

Chapter 7 : System Dynamics and Probabilistic Analysis

7.1 Introduction

The conditions of the pavement structures decline with time due to continuous exposure

to heavy traffic and weather conditions. The loss in strength and functionality of pavement

structure over time makes the pavement structure a dynamic system (Mallick et al., 2015). A

system level model is required to understand the impact of climate factors on the pavements over

the time. System dynamics (SD) approach provides an ideal system for considering the time

factor in the analysis and will be used in this study (Mallick et al., 2015, 2016).

The SD approach involves:

Developing a mental model of the problem.

Outlining the problem dynamically, considering time depending factor.

Transforming the mental model into a system with interconnected loops.

Identifying independent stocks and flows in the system.

Develop a framework for the mental model that is capable of imitating the dynamic

problem of concern. This is done by giving the relationship between the components

of the model.

Analyzing and understanding the resulting model.

7.2 SD Model

STELLA software was used in this study to develop SD model because it has an intuitive

interface that allows to formulate the idea into a model and analyze the results with ease. Also, it

is simple and effective in identifying the variables affecting the system, and better weaves the

model dynamics. The steps used to create the model are explained in the following sections.

7.2.1 Climate Parameters

The initial step is to determine the climate factors affecting the performance of the

pavements. In this study, the temperature and precipitation future simulations for Fort Worth

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temperature were selected because these are the two most influencing factors that are used in the

SD model. The summary of mean annual temperature for Fort Worth, TX for all of the climate

models (future climate from 2040 to 2070, historical predictions from future climate models

from 1968 to 2007, and bias-corrected as described in Chapter Four) is provided in Table 7-1.

The analyses of historical data of Pavement ME for the Fort Worth identified that mean annual

temperature is 66.48 °F. The bias-corrected mean annual temperature is calculated based on this

difference. Similarly, the Table 7-2 summarizes mean annual precipitation for all of the climate

models. Again, the bias-correction is based on historical data (Pavement ME) of 34.16 in. The

rate of increase in mean annual temperature and precipitation over the period of 2040-2070 is

shown in Table 7-3.

Table 7-1 Mean Annual Temperature Summary for Fort Worth, TX.

Climate Models

Future

Simulation

2040-2070

(°F)

Historical

Simulation

1968-2007

(°F)

Pavement ME

Temperature

(°F)

Bias-

Corrected

(°F)

CRCM-CCSM 69.33 64.38

66.48

75.01

CRCM-CGCM3 66.04 61.60 71.73

ECP2-GFDL 58.36 54.92 64.05

ECP2-HADCM3 59.37 55.62 65.05

HRM3-GFDL 67.94 63.03 73.62

HRM3-HADCM3 69.16 65.23 74.85

MM5I-CCSM 69.09 65.06 74.77

MM5I-HADCM3 66.70 62.30 72.39

RCM3-CGCM3 64.38 60.35 70.06

RCM3-GFDL 60.56 57.04 66.24

WRFG-CCSM 63.95 59.02 69.63

WRFG-CGCM3 63.87 61.04 69.55

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Table 7-2 Mean Annual Precipitation Summary for Fort Worth, TX.

Climate Models

Future

Simulation

2040-2070

(in.)

Historical

Simulation

1968-2007

(in.)

Pavement ME

Precipitation

(in.)

Bias-

Corrected

(in.)

CRCM-CCSM 19.76 20.57

34.16

23.71

CRCM-CGCM3 24.80 24.40 29.76

ECP2-GFDL 30.98 36.12 37.16

ECP2-HADCM3 31.02 31.58 37.22

HRM3-GFDL 35.44 37.95 42.52

HRM3-HADCM3 34.18 33.67 41.00

MM5I-CCSM 21.05 21.86 25.25

MM5I-HADCM3 28.22 27.10 33.86

RCM3-CGCM3 28.98 30.08 34.77

RCM3-GFDL 35.96 36.10 43.14

WRFG-CCSM 22.58 20.62 27.09

WRFG-CGCM3 22.02 21.61 26.42

Table 7-3 Rate of Increase in Mean Annual Temperature and Precipitation.

Climate Models

Rate of Change in Mean

Annual Temperature (%)

Rate of Change in Mean Annual

Precipitation (%)

Model

Simulated

Bias

Corrected

Model Simulated Bias Corrected

CRCM-CCSM 4.29% 7.45% 24.52% -3.93%

CRCM-CGCM3 -0.66% 6.68% 39.28% 11.79%

ECP2-GFDL -12.21% 5.19% 57.35% -14.24%

ECP2-HADCM3 -10.70% 5.64% 57.48% -1.78%

HRM3-GFDL 2.19% 7.39% 70.43% -6.60%

HRM3-HADCM3 4.03% 5.92% 66.72% 1.52%

MM5I-CCSM 3.92% 6.05% 28.29% -3.69%

MM5I-HADCM3 0.33% 6.62% 49.29% 4.15%

RCM3-CGCM3 -3.16% 6.06% 51.51% -3.65%

RCM3-GFDL -8.91% 5.29% 71.93% -0.39%

WRFG-CCSM -3.81% 7.41% 32.78% 9.51%

WRFG-CGCM3 -3.93% 4.25% 31.13% 1.88%

7.2.2 Sensitivity Analysis

Sensitivity analysis of the climate parameters was conducted to determine the

relationship between the climate factors and pavement distresses. NARCCAP database consists

119

of simulations from twelve climate models. Each climate model has different annual mean

temperature and precipitation. These different simulations will affect the pavement performance

differently. The sensitivity of climate parameters is included to generate system dynamics model

to examine the overall effect of climate change on the performance of pavements.

To study exclusively the impact of climate change, the pavement design for Fort Worth is

discussed in this section. Pavement section, material properties, and traffic conditions are

maintained same as Chapter 4. Only temperature and precipitation are changed to determine its

influence on the pavement distresses. Figure 7-1 to 7-3 shows the relationship between climate

factors and distresses. The cost for paving the overlay is including material, labor and equipment

cost (RSMeans) corrected to 2017 prices, and it was discussed in Chapter 6.

7.2.3 System Dynamics Model

The SD models are created by developing the relationship between parameters

(temperature and precipitation) and responses (pavement distresses). In this study, an initial

simple SD model (SD Model 1) was developed to identify the feasibility of using minimal

parameters. Unfortunately, the responses observed suggested that a comprehensive model needs

to be developed. Therefore, a slightly complex model was developed and is discussed as SD

Model 2. Both models are discussed in the following sections.

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Figure 7-1 Influence of Mean Annual Temperature on AC Rutting.

Figure 7-2 Influence of Mean Annual Precipitation on IRI.

121

Figure 7-3 Influence of Rehabilitation Thickness on Cost Incurred.

SD Model 1:

The developed SD model is shown in Figure 7-4 while the equations developed for

performing SD analysis are included in Table 7-4. In this model, a simple approach is

considered. The IRI is affected by the change in average annual precipitation and AC rutting

only by average annual temperature. The rate of change in mean annual temperature and

precipitation are obtained from the climate data extracted from NARCCAP. The average value of

all the models is used in the SD model. For each simulation, the mean temperature and

precipitation will increase by rate considered. Using results from the senisitivity analysis, the

relationship between “mean annual temperature and AC rut depth” and “mean annual

precipitation and IRI” are used in the model. This relationship helps in determining how the

distresses change with a change in the climate parameters. The PSI and IRI relationship is shown

in Equation 5-1. The link for the SD model for climate change influence on performance life of

pavements:

https://exchange.iseesystems.com/public/system_dynamics_model/system-dynamics-

model-for-climate-change/index.html#page1

while simulations of the SD model are summarized in Figures 7-5 through 7-6.

https://exchange.iseesystems.com/public/system_dynamics_model/system-dynamics-model-for-climate-change/index.html

https://exchange.iseesystems.com/public/system_dynamics_model/system-dynamics-model-for-climate-change/index.html

122

Figure 7-5 shows the influence of mean annual precipitation on IRI and rut depth. The Y-

axis represents two parameters: 1) mean annual precipitation (represented by blue) and the

precipitation changes from 30 to 80 in. and 2) IRI (represented by black) and the range of change

is from 100 to 103 in./mile. The X-axis represents IRI change for 50 simulations while the rate of

increase is maintained at 0.1129 in. So, from the graph, if mean annual precipitation increase

from 30 to 80 inches, IRI will increases from 100 to 103 in./mile. Similarly, in Figure 7-6 the

variation in the AC rut depth is shown with an increase in mean annual temperature. The

increase in temperature from 65 to 85 °F results in AC rut depth increase from 0.35 to 0.65

inches. The results suggest that there is no significant influence of precipitation while the

increase in temperature increases the rut depth and will exceed the target AC rut depth of 0.4 in.

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Figure 7-4 SD Model 1 for Evaluating Influence of Climate Change on Performance Life of Pavements.

124

Table 7-4 Equations for SD Model 1.

Mean_Annual_Precipitation(t) = Mean_Annual_Precipitation(t - dt) +

(Precipitation_Increase) * dt INIT Mean_Annual_Precipitation = 35

INFLOWS:

Precipitation_Increase = 7.34*Rate_of_Increase_in_Precipitation

Mean_Annual_Temperature(t) = Mean_Annual_Temperature(t - dt) +

(Temperature_Increase) * dt INIT Mean_Annual_Temperature = 66

INFLOWS:

Temperature_Increase = 3.807*Rate_of_increase_in_temperature

AC_Thickness = IF(Maintenance_Criteria=1) THEN (1) ELSE (0)

IRI = 0.0575*Mean_Annual_Precipitation+98.029

Maintenance_Criteria = IF(Rut_Depth>0.4) OR (PSR<3.6) THEN (1) ELSE (0)

Peformance_Life = -65.273*Rut_Depth+41.189

PSI = 5*EXP(-(IRI*0.0157828)/5.5)

PSR_after_Rehabilitation = IF(AC_Thickness>0) THEN (4) ELSE (PSR)

Rate_of_Increase_in_Precipitation = 0.1129

Rate_of_increase_in_temperature = 0.0543

Rehabilitation_Cost = (3.7176*(AC_Thickness) +0.248)

Rut_Depth = 0.0228*Mean_Annual_Temperature-1.1248

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Figure 7-5 IRI and Rut Depth vs. Mean Annual Precipitation for SD Model 1.

Figure 7-6 IRI and Rut Depth vs. Mean Annual Temperature for SD Model 1.

126

SD Model 2:

A complex model was developed to evaluate the influence of climate parameters, and the

developed model is shown in Figure 7-7. Here is the link for the SD model:

https://exchange.iseesystems.com/public/system_dynamics_model/sd-model-for-

pavements-with-climate-change

The equations used for the SD Model 2 are included in Table 7-5. In this model, the

combined effect of a change in average annual precipitation and temperature is considered on the

pavement distresses IRI and total rut depth of the pavements. The simulation results of the SD

model are shown in Figures 7-8 through 7-11. The explanation for the X and Y axis is the same

as for SD Model 1.

The results summarized in Figure 7-8 suggest that there is an influence of precipitation as

the IRI increased from 143 to 150 while precipitation increased from 38 to 44 in. The SD model

1 predicted the minimal influence of precipitation on IRI even though the precipitation increased

from 30 to 80 in. The influence of mean annual temperature is also more in comparison to the

SD Model 1 (SD Model 1 temperature increase was from 65 to 85 °F while SD Model 2

temperature increase is from 66 to 75 °F) Thus, increase in annual precipitation of 6 in. and

temperature by 9 °F results in IRI increase of 7 in./mile and total rut depth by 0.3 inches. In

Figure 7-10, the Y-axis is represented by AC rut depth (red), IRI (black) and performance life

(blue). So, in Figure 7-10 it is illustrated that as the AC rut depth and IRI of the pavement section

increases with increase in climate parameters, the maintenance years of the pavements moves

from 10 to 8 years suggesting an early maintenance requirement due to change in climate. The

comparison between PSI and IRI is shown in Figure 7-11 suggesting that if TxDOT is interested

in using PSI as a criterion for maintenance, then a similar approach can be developed.

https://exchange.iseesystems.com/public/system_dynamics_model/sd-model-for-pavements-with-climate-change

https://exchange.iseesystems.com/public/system_dynamics_model/sd-model-for-pavements-with-climate-change

127

Table 7-5 Equations for SD Model 2.

Mean Annual Precipitation(t) = Mean Annual Precipitation (t - dt) +

(Precipitation Increase) * dt INIT Mean Annual Precipitation = 45

INFLOWS:

Precipitation Increase = 1.247*Rate of Increase in Precipitation

Mean Annual Temperature(t) = Mean Annual Temperature (t - dt) +

(Temperature Increase) * dt INIT Mean Annual Temperature = 70

INFLOWS:

Temperature Increase = 2.282*Rate of increase in temperature

Pavement Performance(t) = Pavement Performance (t - dt) + (Maintenance

Pulse – Deterioration) * dt INIT Pavement Performance = 4.5

INFLOWS:

Maintenance Pulse = PULSE ((((PSI after Rehabilitation – 2.5)/Maintenance

Years) *10), 10, 10) INFLOWS:

Deterioration = (PSI after Rehabilitation – 2.5)/ Maintenance Years

AC Thickness = IF (Maintenance Criteria=1) OR (Rehabilitation Condition <

3.6) THEN (1) ELSE (0)

IRI = 93.92 + 0.7088*Mean Annual Temperature + 0.0558*Mean Annual

Precipitation

Maintenance Criteria = IF (Rut Depth>0.75) OR (PSI<3.6) THEN (1) ELSE (0)

Rehabilitation Years = 33.933-0.253*Rut Depth-0.16948*IRI

PSI = 5*EXP(-(IRI*0.0157828)/5.5)

PSI after Rehabilitation = IF (AC Thickness>0) THEN (5) ELSE (PSI)

Rate of Increase in Precipitation = 0.0951

Rate of Increase in Temperature = 0.0745

Rehabilitation Cost = (3.7176*(AC Thickness) +0.248)

Rut Depth = -0.9315 + 0.025269*Mean Annual Temperature + 0.000717*Mean

Annual Precipitation

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Figure 7-7 SD Model 2 for Evaluating Influence of Climate Change on Performance Life of Pavements.

129

Figure 7-8 IRI and Rut Depth vs Mean Annual Precipitation for SD Model 2.

Figure 7-9 IRI and Rut Depth vs. Mean Annual Temperature for SD Model 2.

130

\

Figure 7-10 Impact of IRI and Rut Depth on Performance Life for SD Model 2.

Figure 7-11 Impact of IRI and Rut Depth on Performance Life for SD Model 2.

131

7.3 Probabilistic Analysis

Monte Carlo Simulation approach was used in conducting the probabilistic analysis. It

provides with a range of possible outcomes and probability they will occur for the scenario. In

Monte Carlo simulation, the range of values are substituted with a probability distribution and

analyzed over a different set of random values from the probability function. Tens of thousands

of simulations can be quickly performed for the analysis.

In this study, a relationship between “Precipitation and IRI” and “Temperature and AC

Rutting” is determined using sensitivity analysis. Both precipitation and temperature are

assumed to follow a normal distribution since it is the simplest and symmetric distribution. This

distribution requires only mean and standard deviation as inputs that were available from the

climate database. The Monte Carlo-simulation was performed to determine the chances of

premature failure for the pavements due to temperature or precipitation. Since the analysis of

climate data suggested that future climate models are under-predicting lower temperatures than

recorded while predicting higher rainfall than recorded, it was decided to perform Monte Carlo-

simulations for both scenarios.

With Bias-Correction:

The mean annual temperature and precipitation predicted by each climate model was

averaged, and the standard deviation was calculated for performing Monte Carlo-simulations,

and the results are summarized in Tables 7-6 and 7-7, respectively. The use of averaged mean

annual temperature of 71.15 °F, the standard deviation of 3.54 °F, and normal distribution

resulted in 96.6% chances of premature failure (Figure 7-12) for the Fort Worth, TX. This means

that the pavement will require maintenance earlier than planned for based on AC rutting criteria

of 0.4 in. Similarly, averaged mean annual precipitation of 33.5 in. and a standard deviation of

6.9 in. resulted in a 48.7% chance of premature failure for Fort Worth (Figure 7-13). This means

that the pavement may not require early maintenance because there are less than 50% chances

that premature failure will occur based on the IRI of 100 in./mile as maintenance criteria. The

132

results for all the analyzed cities are summarized in Tables 7-6 and 7-7. The results summarized

in Table 7-6 suggest that most of the cities, except Amarillo, will require earlier maintenance

because AC rutting will reach the threshold of 0.4 in. due to a rise in temperature. Similarly, the

averaged mean annual precipitation data summarized in Table 7-7 suggests that chances are

minimal for Amarillo, El Paso, and McAllen while chances are maximum for Houston (more

than 99%) for IRI threshold of 100 in./mile. In addition to Houston, there are more than 50%

chances of premature failure in Austin and Dallas based on IRI threshold of 100 in./mile.

Table 7-6 Chances of Early Failure with AC Rutting as Maintenance Criteria (with Bias-

Correction).

Cities

Average Annual

Mean

Temperature

(°F)

Standard

Deviation

(°F)

Chances of increase

in AC rutting>0.4

inches (%)

Amarillo 64.23 3.41 49.1

Austin 72.87 3.74 98.9

Corpus Christi 76.94 3.38 100.0

Dallas 71.72 3.91 97.1

El Paso 71.13 3.05 98.7

Fort Worth 71.15 3.74 96.6

Houston 73.84 3.56 99.6

McAllen 78.73 3.66 100.0

San Antonio 73.83 3.82 99.4

133

Table 7-7 Chances of Early Failure with IRI as Maintenance Criteria (with Bias-Correction).

Cities

Average Annual

Mean

Precipitation (in.)

Standard

Deviation (in.)

Chances of increase

in IRI>100 in./mile

(%)

Amarillo 17.59 1.31 0.00

Austin 36.84 9.16 61.00


Dallas 34.46 2.05 53.50

El Paso 8.65 0.93 0.00

Fort Worth 33.50 6.90 48.70

Houston 42.74 3.28 99.50

McAllen 17.69 3.12 0.00

San Antonio 30.15 3.04 8.70

Figure 7-12 Monte Carlo Simulation for AC Rutting (with Bias-Correction).

134

Figure 7-13 Monte Carlo Simulation for IRI (with Bias-Correction).

Without Bias-Correction:

The mean annual temperature predicted by each climate model was averaged, and the

standard deviation was calculated for performing Monte Carlo-simulations, and the results are

summarized in Tables 7-8. The use of a mean annual temperature of 64.9 °F, the standard

deviation of 3.86 °F, and normal distribution resulted in 56.1% chances of premature failure

(Figure 7-14) for the Fort Worth, TX. This means that the pavement will require maintenance

earlier than planned for based on AC rutting criteria of 0.4 in. The results summarized in Table

7-8 suggest that most of the cities, except Amarillo and El Paso, will require earlier maintenance

because AC rutting will reach the threshold of 0.4 in. due to a rise in temperature.

Based on the analysis, the bias-correction needs to be performed for precipitation data

and and temperature data obtained from the climate models. Monte Carlo simulations for all the

selected pavement sections are presented in Appendix C.

135

Table 7-8 Chances of Failure of Pavements due to AC Rutting (without Bias-Correction).

Cities

Average Mean

Annual

Temperature

(°F)

Standard

Deviation

(°F)

Chances of increase

in AC rutting>0.4

inches (%)

Amarillo 59.59 4.17 12.90

Austin 65.78 3.77 65.20


Dallas 65.66 4.38 65.20

El Paso 62.40 3.25 27.90

Fort Worth 64.90 3.86 56.10

Houston 67.28 3.66 79.20

McAllen 71.49 3.58 97.80

San Antonio 66.71 3.70 74.20

Figure 7-14 Monte Carlo Simulation for AC Rutting (without Bias-Correction).

136

Chapter 8 : Closure

8.1 Summary

Over the several centuries, the global temperatures have been rising, and the rate of rising

in temperature has increased significantly in the last century. The increase in temperature and

precipitation can significantly influence the performance life of pavements because of their

continuous exposure to climate. To take into the influence of climate, the climate parameters like

temperature and precipitation are considered in the pavement design. Currently, designers use the

historical weather patterns while designing pavements. Since the climate is changing, the

estimated service life of the pavement will be reduced significantly if the historical data is used

in designs. The primary purpose of this study was to identify the influence of climate change on

the service life of pavements and identify the levels of loss in service life in the event the climate

change influences service life. To understand how the climate change will affect the pavement

performance, a thorough review of information was conducted to identify the current state of

practice and research gaps. Based on the review, the impact of climate change on the

performance of pavements was performed using future climate models and Pavement ME design

software. The study used twelve future climate prediction models from NARCCAP databases to

develop an understanding of climatic factors like on the performance of the pavement structures.

The pavement structure evaluation was performed (using Pavement Mechanistic-

Empirical (ME) Design software and future climate model predictions) to evaluate the influence

of climate on the performance of different pavement structures, mix types, and regional

variations, among others. The results of the influence were incorporated in SD Model for

decision makers. Various approaches were evaluated to mitigate the influence of climate change

like changing the thickness of the AC layer, using high-quality material, etc. An economic

analysis approach was also developed to help decision-makers in selecting pavement designs that

can withstand and resist climate change.

137

8.2 Findings

The null hypothesis was that climate change would not influence the performance of the

pavements. But the vulnerability study suggested that with climate change the performance of

the pavement sections was declining. Therefore the alternate hypothesis is correct. The findings

from climate data projection, climate change and extreme events vulnerability study of the

pavement sections, adaptation methods and economic and environmental assessment of the

mitigation options are summarized in this section.

Change in climate predictions from various models:

i. Climate model projections vary geographically indicating that the projected climate

change is dissimilar for different cities within Texas

ii. The predictions from different climate models varies significantly for the same

geographical location.

iii. HadCM3 climate model prediction for temperature is maximum among all the

selected models for all the cities except for Dallas where CRCM climate model

predictions are maximum. If the GFDL climate model is selected for the analysis, the

estimations are lowest among all of the climate models evaluated.

iv. Mean annual precipitation predictions from GFDL and CCSM models are maximum

and minimum among the selected climate models for Texas, respectively.

v. Four models predicted cooler winters and hotter summers while two climate models

with HADCM3 driving models/GCMs suggested opposite trends

vi. Future predicted mean annual wind speed increases across all the regions with ECP2-

GFDL climate model predicting lowest wind speeds. Wind speed climate predictions

are maximum and minimum for CGCM3 and HadCM3 climate models among the

selected models within Texas. For Amarillo and Corpus Christi, all climate models

are predicting higher annual mean wind speed.

138

vii. Relative humidity is maximum for HadCM3 and GFDL climate models while they

are minimum for CCSM climate models. The predictions for Houston, Corpus

Christi, and McAllen, are higher.

viii. For Amarillo, out of eleven climate models, seven models predicted an increase in

temperature for Jan-Mar while four models predicted a decrease, giving 64% chances

of increase in Jan-Mar temperature in Amarillo. Chances of increase in mean

temperature for all four seasons are more than 80% for McAllen. Except for McAllen,

all the other cities have higher chances of a decrease in winter temperature (Oct-Dec).

Influence of climate change and extreme events on the vulnerability of the pavement

sections:

i. Changing climate adversely impacts the pavements functionality by reducing the

service life of pavements.

ii. When IRI is considered as the maintenance criteria, the performance of the pavement

section is reduced for any climate model selected for all the cities except San

Antonio, Fort Worth and Amarillo. However, with AC rutting as the maintenance

criteria, the performance reduces to only six years for Fort Worth, or it improves such

that the pavements perform well for the design service life of 20 years.

iii. For McAllen, Austin, Houston and El Paso, the change in maintenance required are

greater than one year with extreme events occurring, which suggests that the effect of

subgrade saturation on the performance of pavements is more for these cities.

Influence of extreme event is significantly higher for El Paso and minimal for Paris.

iv. Combined effects of climate change and extreme events is more on the pavement

performance rather than an individual event.

v. The range of percentage change of the pavement distresses with respect to historical

climate vary geographically. The distresses can be as high as +60% (AC rutting as per

McAllen) and are as low as -30% (AC rutting as per Houston).

139

vi. The Pavement ME analysis was compared with FPS software design. The comparison

evaluation suggested that the Pavement ME Design method predicts less service life

in comparison to FPS 21. However, the FPS 21 doesn’t take into consideration

change in climate, and the pavement designed using FPS 21 may require maintenance

earlier than anticipated.

Influence of pavement design modification to mitigate influence of climate change:

To withstand changing climate either pavements need to use high-performance materials

or enhance layer thicknesses or both. Adaptation methods considered in this study are:

a. Increasing the AC layer thickness.

b. Changing the binder grade.

c. Changing the mix type of the AC layer.

d. Enhancing the subgrade quality.

Considering any of the adaptation strategies improves the performance of the pavement

sections, thus, mitigating the impacts of the climate change.

Economic and environmental analysis results: The cost and emission analysis show that

early consideration of future weather changes into design yields long-term benefits regarding

savings in user costs and emissions.

8.3 Recommendations and Limitations

The recommendation and limitations of this study are as follows:

• When choosing the climate prediction model for considering changes in pavement

designs do not rely on one climate model. Consider analyzing the sections for a range

of climate models predictions and choosing the climate model which has an average

impact.

• A detailed procedure for downloading the climate data from data sources and

converting it to format needed for pavement design software is presented for use by

future researchers and highway engineers.

140

• Climate data has uncertainty and including bias-correction might increase the

uncertainty so use the climate data which is already corrected for bias to reduce any

more uncertainty.

• This study suggests recommended use of future climate data inputs that can be used

in enhancing the resiliency of pavements.

• Changing the material type or selecting the binder grade based on future climate will

improve the performance of the pavements.

• A decision-making tool needs to be developed for decision makers regarding

selecting pavement designs that mitigate the influence of climate change.

• The software has limitations of considering extreme weather events like drought

model and flooding so for future study proper analysis should be done to include

these events in the analysis.

• During the overlay design (Maintenance) the ME software needs the modulus of

existing layer which is estimated using Falling Weight Deflectometer (FWD) data.

Since there is no FWD data available, we considered level 3 input for design, which

considers the recommended modulus and information from general pavements.

141

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151

Appendix A : Bias Correction Plots

Bias Correction Plots for Selected Cities

The following plots from Figure A-1 to Figure A-6 shows the bias-corrected mean annual

temperature and precipitation. All the models have predicted a rise in precipitation and by

applying bias correction to precipitation the mean annual precipitation decreases compared to the

model predicted values.

Figure A-1 Bias Correction for Mean Annual Temperature for Amarillo, TX.

152

Figure A-2 Bias Correction for Mean Annual Precipitation for Amarillo, TX.

Figure A-3 Bias Correction for Mean Annual Temperature for Austin, TX.

153

Figure A-4 Bias Correction for Mean Annual Precipitation for Austin, TX.

Figure A-5 Bias Correction for Mean Annual Temperature for Corpus Christi, TX.

154

Figure A-6 Bias Correction for Mean Annual Precipitation for Corpus Christi, TX.

Figure A-7 Bias Correction for Mean Annual Temperature for Dallas, TX.

155

Figure A-8 Bias Correction for Mean Annual Precipitation for Dallas, TX.

Figure A-9 Bias Correction for Mean Annual Temperature for El Paso, TX.

156

Figure A-10 Bias Correction for Mean Annual Precipitation for El Paso, TX.

Figure A-11 Bias Correction for Mean Annual Temperature for Houston, TX.

157

Figure A-12 Bias Correction for Mean Annual Precipitation for Houston, TX.

Figure A-13 Bias Correction for Mean Annual Temperature for McAllen, TX.

158

Figure A-14 Bias Correction for Mean Annual Precipitation for McAllen, TX.

Figure A-15 Bias Correction for Mean Annual Temperature for San Antonio, TX.

159

Figure A-16 Bias Correction for Mean Annual Precipitation for San Antonio, TX.

160

Appendix B : Pavement Analysis Data

Distress Analysis Plots

Figure B-1 IRI of the Pavement Section over the Years for Amarillo, Texas.

161

Figure B-2 AC Rutting of the Pavement Section over the Years for Amarillo, Texas.

162

Figure B-3 IRI of the Pavement Section over the Years for Austin, Texas.

163

Figure B-4 AC Rutting of the Pavement Section over the Years for Austin, Texas.

164

Figure B-5 IRI of the Pavement Section over the Years for Corpus Christi, Texas.

165

Figure B-6 AC Rutting of the Pavement Section over the Years for Corpus Christi, Texas.

166

Figure B-7 IRI of the Pavement Section over the Years for Dallas, Texas.

167

Figure B-8 AC Rutting of the Pavement Section over the Years for Dallas, Texas.

168

Figure B-9 IRI of the Pavement Section over the Years for El Paso, Texas.

169

Figure B-10 AC Rutting of the Pavement Section over the Years for El Paso, Texas.

170

Figure B-11 IRI of the Pavement Section over the Years for Houston, Texas.

171

Figure B-12 AC Rutting of the Pavement Section over the Years for Houston, Texas.

172

Figure B-13 IRI of the Pavement Section over the Years for McAllen, Texas.

173

Figure B-14 AC Rutting of the Pavement Section over the Years for McAllen, Texas.

174

Figure B-15 IRI of the Pavement Section over the Years for Paris, Texas.

175

Figure B-16 AC Rutting of the Pavement Section over the Years for Paris, Texas.

176

Figure B-17 IRI of the Pavement Section over the Years for San Antonio, Texas.

177

Figure B-18 AC Rutting of the Pavement Section over the Years for San Antonio, Texas.

178

Figure B-19 IRI of the Concrete Pavement Section over the Years for Fort Worth, Texas.

179

Figure B-20 CRCP Punch-out of the Pavement Section over the Years for Fort Worth, Texas.

180

Moisture Damage Plots


Texas.


181

Figure B-23 Subgrade Modulus of the Pavement Section over the Years for Amarillo, Texas.


182


Figure B-26 Subgrade Modulus of the Pavement Section over the Years for Austin, Texas.

183


Christi, Texas.


184


Texas.


185


Figure B-32 Subgrade Modulus of the Pavement Section over the Years for Dallas, Texas.

186


Texas.


187

Figure B-35 Subgrade Modulus of the Pavement Section over the Years for El Paso, Texas.


Texas.

188


Figure B-38 Subgrade Modulus of the Pavement Section over the Years for Houston, Texas.

189


Texas.


190

Figure B-41 Subgrade Modulus of the Pavement Section over the Years for McAllen, Texas.


191


Figure B-44 Subgrade Modulus of the Pavement Section over the Years for Paris, Texas.

192


Texas.


193


Figure B-48 Base + Subgrade Rutting of the Concrete Pavement Section over the Years for Fort

Worth, Texas.

194

Figure B-49 IRI of the Concrete Pavement Section over the Years for Fort Worth, Texas.

Figure B-50 Subgrade Modulus of the Pavement Section over the Years for Fort Worth, Texas.

195

Moisture and Climate Change Plots


Texas.


196

Figure B-53 Subgrade Modulus of the Pavement Section over the Years for Amarillo, Texas.


197


Figure B-56 Subgrade Modulus of the Pavement Section over the Years for Austin, Texas.

198


Christi, Texas.


199


Texas.


200


Figure B-62 Subgrade Modulus of the Pavement Section over the Years for Dallas, Texas.

201


Texas.


202

Figure B-65 Subgrade Modulus of the Pavement Section over the Years for El Paso, Texas.


Texas.

203


Figure B-68 Subgrade Modulus of the Pavement Section over the Years for Houston, Texas.

204


Texas.


205

Figure B-71 Subgrade Modulus of the Pavement Section over the Years for McAllen, Texas.


206


Figure B-74 Subgrade Modulus of the Pavement Section over the Years for Paris, Texas.

207


Texas.


208


Binder Change Variations Plots

Figure B-78 Change in Performance of Pavements with Changing Binder Type for Amarillo,

TX.

209

Figure B-79 Change in Performance of Pavements with Changing Binder Type for Austin, TX.

Figure B-80 Change in Performance of Pavements with Changing Binder Type for Corpus

Christi, TX.

210

Figure B-81 Change in Performance of Pavements with Changing Binder Type for Dallas, TX.

Figure B-82 Change in Performance of Pavements with Changing Binder Type for El Paso, TX.

211

Figure B-83 Change in Performance of Pavements with Changing Binder Type for Houston, TX.

Figure B-84 Change in Performance of Pavements with Changing Binder Type for McAllen, TX.

212

Figure B-85 Change in Performance of Pavements with Changing Binder Type for Paris, TX.

Figure B-86 Change in Performance of Pavements with Changing Binder Type for San Antonio,

TX.

213

AC Thickness Change Variations Plots

Figure B-87 Change in Performance of Pavements with Increase in AC Thickness for Amarillo,

TX.

Figure B-88 Change in Performance of Pavements with Increase in AC Thickness for Austin,

TX.

214

Figure B-89 Change in Performance of Pavements with Increase in AC Thickness for Corpus

Christi, TX.

Figure B-90 Change in Performance of Pavements with Increase in AC Thickness for Dallas,

TX.

215

Figure B-91 Change in Performance of Pavements with Increase in AC Thickness for El Paso,

TX.

Figure B-92 Change in Performance of Pavements with Increase in AC Thickness for Houston,

TX.

216

Figure B-93 Change in Performance of Pavements with Increase in AC Thickness for McAllen,

TX.

Figure B-94 Change in Performance of Pavements with Increase in AC Thickness for Paris, TX.

217

Figure B-95 Change in Performance of Pavements with Increase in AC Thickness for San

Antonio, TX.

218

Appendix C: Probabilistic Analysis

This section shows the Monte-Carlo simulation for the pavement section selected in this

analysis. The summarized results are shown in Chapter 7. The summary of mean annual

temperature and precipitation for selected cities in TX for all of the climate models (future

climate from 2040 to 2070, historical predictions from future climate models from 1968 to 2007,

and bias-corrected as described in Chapter Four) is provided in Table C-1 to C-16.

Table C-1 Annual Average Mean Temperature Summary for Amarillo, TX.

Climate Models

Future

Simulation

2040-2070

(°F)

Historical

Simulation

1968-2007

(°F)

Pavement

ME

Temperature

(°F)

Bias-

Corrected

(°F)

CRCM-CCSM 61.1 56.3

59.7

66.2

CRCM-CGCM3 58.1 53.6 63.2

ECP2-GFDL 53.7 50.3 58.8

ECP2-HADCM3 56.3 52.6 61.4

HRM3-GFDL 60.7 55.0 65.8

HRM3-HADCM3 64.1 58.8 69.3

MM5I-CCSM 63.4 58.9 68.5

MM5I-HADCM3 58.9 55.2 64.0

RCM3-CGCM3 57.6 53.3 62.7

RCM3-GFDL 53.4 49.9 58.5

WRFG-CCSM 60.1 55.9 64.3

WRFG-CGCM3 59.2 55.7 65.2

219

Table C-2 Annual Average Mean Precipitation Summary for Amarillo, TX.

Climate Models

Future

Simulation

2040-2070

(in.)

Historical

Simulation

1968-2007

(in.)

Pavement

ME

Precipitation

(in.)

Bias-

Corrected

(in.)

CRCM-CCSM 17.87 18.17

19.2

18.00

CRCM-CGCM3 19.69 18.87 19.10

ECP2-GFDL 21.38 24.46 16.00

ECP2-HADCM3 15.27 17.63 15.86

HRM3-GFDL 26.31 29.71 16.21

HRM3-HADCM3 21.49 23.08 17.05

MM5I-CCSM 17.08 17.86 17.51

MM5I-HADCM3 21.28 21.73 17.93

RCM3-CGCM3 20.04 21.75 16.87

RCM3-GFDL 28.98 29.67 17.88

WRFG-CCSM 14.51 14.49 18.34

WRFG-CGCM3 15.06 13.59 20.29

Table C-3 Annual Average Mean Temperature Summary for Austin, TX.

Climate Models

Future

Simulation

2040-2070

(°F)

Historical

Simulation

1968-2007

(°F)

Pavement ME

Temperature

(°F)

Bias-

Corrected

(°F)

CRCM-CCSM 70.4 65.9

68.7

77.1

CRCM-CGCM3 67.3 63.2 74.0

ECP2-GFDL 60.1 56.9 66.8

ECP2-HADCM3 60.8 57.6 67.5

HRM3-GFDL 69.4 64.5 76.1

HRM3-HADCM3 67.3 66.3 74.0

MM5I-CCSM 69.5 65.7 76.2

MM5I-HADCM3 70.6 63.2 77.3

RCM3-CGCM3 64.1 60.8 70.8

RCM3-GFDL 60.9 57.8 67.6

WRFG-CCSM 63.9 60.0 70.6

WRFG-CGCM3 64.1 61.5 70.8

220

Table C-4 Annual Average Mean Precipitation Summary for Austin, TX.

Climate Models

Future

Simulation

2040-2070

(in.)

Historical

Simulation

1968-2007

(in.)

Pavement

ME

Precipitation

(in.)

Bias-

Corrected

(in.)

CRCM-CCSM 18.61 20.73

31.5

28.33

CRCM-CGCM3 27.70 27.66 31.59

ECP2-GFDL 33.35 24.46 43.02

ECP2-HADCM3 39.85 37.45 33.56

HRM3-GFDL 33.74 29.71 35.82

HRM3-HADCM3 41.13 23.08 56.21

MM5I-CCSM 17.15 19.23 28.13

MM5I-HADCM3 37.52 32.85 36.03

RCM3-CGCM3 35.25 34.53 32.20

RCM3-GFDL 43.21 43.42 31.39

WRFG-CCSM 24.26 14.49 52.83

WRFG-CGCM3 28.24 27.04 32.95

Table C-5 Annual Average Mean Temperature Summary for Corpus Christi, TX.

Climate Models

Future

Simulation

2040-2070

(°F)

Historical

Simulation

1968-2007

(°F)

Pavement ME

Temperature

(°F)

Bias-

Corrected

(°F)

CRCM-CCSM 72.7 68.9

73.3

79.0

CRCM-CGCM3 70.9 67.5 77.2

ECP2-GFDL 66.0 72.3 72.3

ECP2-HADCM3 67.9 64.6 74.2

HRM3-GFDL 73.1 69.2 79.3

HRM3-HADCM3 75.2 70.9 81.5

MM5I-CCSM 73.7 70.2 79.9

MM5I-HADCM3 73.4 69.6 79.7

RCM3-CGCM3 67.4 64.2 73.7

RCM3-GFDL 64.9 61.9 71.2

WRFG-CCSM 69.5 67.9 75.8

WRFG-CGCM3 70.5 66.4 76.8

221

Table C-6 Annual Average Mean Precipitation Summary for Corpus Christi, TX.

Climate Models

Future

Simulation

2040-2070

(in.)

Historical

Simulation

1968-2007

(in.)

Pavement ME

Precipitation

(in.)

Bias-

Corrected

(in.)

CRCM-CCSM 28.57 32.87

27.2

24.62

CRCM-CGCM3 53.32 51.59 29.27

ECP2-GFDL 42.74 45.49 26.61

ECP2-HADCM3 168.64 135.71 35.19

HRM3-GFDL 24.29 27.63 24.89

HRM3-HADCM3 38.23 35.37 30.61

MM5I-CCSM 16.65 19.94 23.65

MM5I-HADCM3 167.25 118.91 39.83

RCM3-CGCM3 47.06 45.29 29.43

RCM3-GFDL 54.94 54.74 28.42

WRFG-CCSM 33.54 34.43 27.59

WRFG-CGCM3 39.30 40.09 27.77

Table C-7 Annual Average Mean Temperature Summary for Dallas, TX.

Climate Models

Future

Simulation

2040-2070

(°F)

Historical

Simulation

1968-2007

(°F)

Pavement ME

Temperature

(°F)

Bias-

Corrected

(°F)

CRCM-CCSM 70.4 65.5

67.0

75.9

CRCM-CGCM3 67.0 62.6 72.5

ECP2-GFDL 58.2 63.7 63.7

ECP2-HADCM3 59.0 55.2 64.5

HRM3-GFDL 68.9 63.5 74.4

HRM3-HADCM3 70.2 65.5 75.7

MM5I-CCSM 69.7 65.7 75.2

MM5I-HADCM3 66.2 62.9 71.7

RCM3-CGCM3 64.3 60.4 69.8

RCM3-GFDL 60.6 57.2 66.1

WRFG-CCSM 66.4 63.7 71.9

WRFG-CGCM3 66.5 61.5 72.0

222

Table C-8 Annual Average Mean Precipitation Summary for Dallas, TX.

Climate Models

Future

Simulation

2040-2070

(in.)

Historical

Simulation

1968-2007

(in.)

Pavement ME

Precipitation

(in.)

Bias-

Corrected (in.)

CRCM-CCSM 21.03 22.05

31.8

33.16

CRCM-CGCM3 27.58 26.79 35.79

ECP2-GFDL 32.22 37.07 30.22

ECP2-HADCM3 32.22 33.21 33.74

HRM3-GFDL 35.89 37.10 33.64

HRM3-HADCM3 35.21 34.68 35.30

MM5I-CCSM 21.93 23.17 32.91

MM5I-HADCM3 30.96 29.48 36.52

RCM3-CGCM3 30.93 31.76 33.86

RCM3-GFDL 37.79 37.24 35.28

WRFG-CCSM 25.00 22.67 38.35

WRFG-CGCM3 25.26 25.27 34.77

Table C-9 Annual Average Mean Temperature Summary for El Paso, TX.

Climate Models

Future

Simulation

2040-2070

(°F)

Historical

Simulation

1968-2007

(°F)

Pavement ME

Temperature

(°F)

Bias-

Corrected

(°F)

CRCM-CCSM 64.2 59.3

66.6

72.6

CRCM-CGCM3 62.1 57.1 70.5

ECP2-GFDL 55.5 63.9 63.9

ECP2-HADCM3 57.1 54.0 65.6

HRM3-GFDL 63.6 58.4 72.1

HRM3-HADCM3 67.3 61.8 75.7

MM5I-CCSM 65.2 60.8 73.6

MM5I-HADCM3 63.1 59.6 71.5

RCM3-CGCM3 64.6 60.8 73.0

RCM3-GFDL 61.1 57.8 69.5

WRFG-CCSM 62.1 57.4 70.5

WRFG-CGCM3 60.7 58.2 69.1

223

Table C-10 Annual Average Mean Precipitation Summary for El Paso, TX.

Climate Models

Future

Simulation

2040-2070

(in.)

Historical

Simulation

1968-2007

(in.)

Pavement

ME

Precipitation

(in.)

Bias-

Corrected

(in.)

CRCM-CCSM 13.19 15.55

7.5

7.89

CRCM-CGCM3 11.56 12.47 8.62

ECP2-GFDL 16.70 16.85 9.22

ECP2-HADCM3 8.20 8.97 8.50

HRM3-GFDL 21.32 23.84 8.32

HRM3-HADCM3 11.88 15.09 7.33

MM5I-CCSM 9.30 11.73 7.37

MM5I-HADCM3 12.72 11.11 10.66

RCM3-CGCM3 34.32 35.28 9.05

RCM3-GFDL 41.59 42.26 9.16

WRFG-CCSM 9.75 10.88 8.34

WRFG-CGCM3 7.12 7.07 9.36

Table C-11 Annual Average Mean Temperature Summary for Houston, TX.

Climate Models

Future

Simulation

2040-2070

(°F)

Historical

Simulation

1968-2007

(°F)

Pavement ME

Temperature

(°F)

Bias-

Corrected (°F)

CRCM-CCSM 71.7 67.4

69.7

77.9

CRCM-CGCM3 68.5 64.7 74.7

ECP2-GFDL 61.8 68.0 68.0

ECP2-HADCM3 62.0 58.8 68.2

HRM3-GFDL 71.1 66.7 77.3

HRM3-HADCM3 68.7 67.8 74.9

MM5I-CCSM 70.5 67.0 76.7

MM5I-HADCM3 72.1 64.5 78.3

RCM3-CGCM3 65.3 61.9 71.5

RCM3-GFDL 62.9 59.8 69.1

WRFG-CCSM 65.7 63.5 71.9

WRFG-CGCM3 66.1 62.2 72.3

224

Table C-12 Annual Average Mean Precipitation Summary for Houston, TX.

Climate Models

Future

Simulation

2040-2070

(in.)

Historical

Simulation

1968-2007

(in.)

Pavement ME

Precipitation

(in.)

Bias-

Corrected (in.)

CRCM-CCSM 23.23 25.84

43.6

39.16

CRCM-CGCM3 39.40 40.20 42.69

ECP2-GFDL 49.88 56.82 38.24

ECP2-HADCM3 80.05 77.09 45.23

HRM3-GFDL 33.37 37.32 38.95

HRM3-HADCM3 47.21 45.16 45.53

MM5I-CCSM 22.59 25.59 38.45

MM5I-HADCM3 61.86 57.08 47.21

RCM3-CGCM3 48.11 47.12 44.48

RCM3-GFDL 53.59 50.13 46.57

WRFG-CCSM 29.00 28.92 43.67

WRFG-CGCM3 38.84 39.61 42.71

Table C-13 Annual Average Mean Temperature Summary for McAllen, TX.

Climate Models

Future

Simulation

2040-2070

(°F)

Historical

Simulation

1968-2007

(°F)

Pavement ME

Temperature

(°F)

Bias-

Corrected (°F)

CRCM-CCSM 74.6 70.1

74.7

81.6

CRCM-CGCM3 71.3 67.5 78.3

ECP2-GFDL 66.5 73.5 73.5

ECP2-HADCM3 68.3 64.4 75.3

HRM3-GFDL 74.8 70.3 81.8

HRM3-HADCM3 74.5 71.7 81.5

MM5I-CCSM 75.8 71.9 82.8

MM5I-HADCM3 76.2 70.2 83.2

RCM3-CGCM3 68.6 65.4 75.6

RCM3-GFDL 66.2 63.1 73.2

WRFG-CCSM 70.2 67.7 77.2

WRFG-CGCM3 70.4 66.6 77.4

225

Table C-14 Annual Average Mean Precipitation Summary for McAllen, TX.

Climate Models

Future

Simulation

2040-2070

(in.)

Historical

Simulation

1968-2007

(in.)

Pavement

ME

Precipitation

(in.)

Bias-

Corrected

(in.)

CRCM-CCSM 24.02 29.24

18.2

14.86

CRCM-CGCM3 42.47 44.07 17.43

ECP2-GFDL 49.88 38.54 23.41

ECP2-HADCM3 43.77 35.54 22.28

HRM3-GFDL 23.53 27.44 15.51

HRM3-HADCM3 38.44 34.74 20.02

MM5I-CCSM 10.72 15.63 12.41

MM5I-HADCM3 44.51 46.27 17.40

RCM3-CGCM3 51.11 49.73 18.59

RCM3-GFDL 69.68 69.44 18.15

WRFG-CCSM 21.38 24.99 15.48

WRFG-CGCM3 32.03 34.66 16.72

Table C-15 Annual Average Mean Temperature Summary for San Antonio, TX.

Climate Models

Future

Simulation

2040-2070

(°F)

Historical

Simulation

1968-2007

(°F)

Pavement ME

Temperature

(°F)

Bias-

Corrected

(°F)

CRCM-CCSM 70.3 66.1

70.2

77.5

CRCM-CGCM3 66.7 63.6 73.9

ECP2-GFDL 59.9 67.1 67.1

ECP2-HADCM3 62.1 59.1 69.3

HRM3-GFDL 70.6 65.8 77.8

HRM3-HADCM3 67.3 67.5 74.5

MM5I-CCSM 70.2 66.7 77.4

MM5I-HADCM3 71.5 64.8 78.7

RCM3-CGCM3 64.5 61.6 71.7

RCM3-GFDL 61.7 58.6 68.9

WRFG-CCSM 64.9 62.5 72.1

WRFG-CGCM3 65.1 61.1 72.3

226

Table C-16 Annual Average Mean Precipitation Summary for San Antonio, TX.

Climate Models

Future

Simulation

2040-2070

(in.)

Historical

Simulation

1968-2007

(in.)

Pavement ME

Precipitation

(in.)

Bias-

Corrected

(in.)

CRCM-CCSM 17.08 19.51

30.3

26.49

CRCM-CGCM3 27.01 27.03 30.22

ECP2-GFDL 29.41 34.59 25.72

ECP2-HADCM3 37.93 34.26 33.48

HRM3-GFDL 29.42 32.60 27.30

HRM3-HADCM3 37.94 32.94 34.84

MM5I-CCSM 14.67 16.14 27.51

MM5I-HADCM3 34.37 30.15 34.48

RCM3-CGCM3 33.33 32.49 31.04

RCM3-GFDL 41.67 41.96 30.04

WRFG-CCSM 24.00 24.41 29.75

WRFG-CGCM3 25.37 24.82 30.92

With Bias Correction

The mean annual temperature and precipitation predicted by each climate model were

averaged, and the standard deviation was calculated for performing Monte Carlo-simulations,

and the results are summarized in Tables 7-6 and 7-7. The plots corresponding to these

simulations are presented in this Appendix for all the selected cities in Texas from Figure C-1 to

C-13.

Figure C-1 Monte Carlo Simulation for AC Rutting for Amarillo (with Bias-Correction).

227

Figure C-2 Monte Carlo Simulation for AC Rutting for Austin (with Bias-Correction).

Figure C-3 Monte Carlo Simulation for IRI for Austin (with Bias-Correction).

228

Figure C-4 Monte Carlo Simulation for AC Rutting for Corpus Christi (with Bias-Correction).

Figure C-5 Monte Carlo Simulation for IRI for Corpus Christi (with Bias-Correction).

229

Figure C-6 Monte Carlo Simulation for AC Rutting for Dallas (with Bias-Correction).

Figure C-7 Monte Carlo Simulation for IRI for Dallas (with Bias-Correction).

230

Figure C-8 Monte Carlo Simulation for AC Rutting for El Paso (with Bias-Correction).

Figure C-9 Monte Carlo Simulation for AC Rutting for Houston (with Bias-Correction).

231

Figure C-10 Monte Carlo Simulation for IRI for Houston (with Bias-Correction).

Figure C-11 Monte Carlo Simulation for AC Rutting for McAllen (with Bias-Correction).

232

Figure C-12 Monte Carlo Simulation for AC Rutting for San Antonio (with Bias-Correction).

Figure C-13 Monte Carlo Simulation for IRI for San Antonio (with Bias-Correction).

Without Bias Correction

In this section Monte Carlo simulation without considering bias correction are presented

in Figure C-14 to C-21.

233

Figure C-14 Monte Carlo Simulation for AC Rutting for Amarillo (without Bias-Correction).

Figure C-15 Monte Carlo Simulation for AC Rutting for Austin (without Bias-Correction).

234

Figure C-16 Monte Carlo Simulation for AC Rutting for Corpus Christi (without Bias-

Correction).

Figure C-17 Monte Carlo Simulation for AC Rutting for Dallas (without Bias-Correction).

235

Figure C-18 Monte Carlo Simulation for AC Rutting for El Paso (without Bias-Correction).

Figure C-19 Monte Carlo Simulation for AC Rutting for Houston (without Bias-Correction).

236

Figure C-20 Monte Carlo Simulation for AC Rutting for McAllen (without Bias-Correction).

Figure C-21 Monte Carlo Simulation for AC Rutting for San Antonio (without Bias-Correction).

237

Curriculum Vita

Megha Sharma earned her Bachelor of Technology degree in Civil Engineering from

G.B. Pant University of Agriculture and Technology, Pantnagar-India in 2010. Soon after that,

she joined Indian Institute of Technology, Kharagpur for her Master’s in Transportation

Engineering. She joined The University of Texas at El Paso for the doctoral program in Civil

Engineering in 2013.

Dr. Sharma has received numerous scholarships during her time as a graduate student at

UTEP. She was the recipient of UTEP’s Graduate School Kalpana Chawla Memorial

Scholarship. She also received Women in Transportation Seminar’s Scholarship for Graduate

students from 2014-2015 and 2017-2018. While pursuing her degree, Dr. Sharma worked as a

Research Associate and Teaching Assistant for the Department of Civil Engineering. She was

also a lecturer for the Civil Engineering Department teaching undergraduate students.

Dr. Sharma has presented her work at various conferences including Transportation

Research Board (TRB) and American Society for Civil Engineers (ASCE) and her work also

appeared the proceedings of these conferences. Dr. Sharma is currently seeking job opportunities

in the field of Civil Engineering.

Dr. Sharma’s dissertation “Understanding the Consequences and Cost of Climate Change

on Texas Pavements” was supervised by Dr. Vivek Tandon.

Contact Information: [email protected]

This dissertation was typed by Megha Sharma.

mailto:[email protected]

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