understanding the consequences and costs of climate change
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University of Texas at El PasoDigitalCommons@UTEP
Open Access Theses & Dissertations
2018-01-01
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
Table 5-5 Change in Maintenance Years for Selected Pavement Sections with IRI as
Maintenance Criteria ......................................................................................................................79
xii
Table 5-6 Change in Maintenance Years for Selected Pavement Sections with IRI as
Maintenance Criteria ......................................................................................................................97
Table 5-7 Change in Maintenance Years for Selected Pavement Sections with IRI as
Maintenance Criteria ......................................................................................................................98
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)
Pavement Section. ......................................................................................................................... 76
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)
Pavement Section. ......................................................................................................................... 78
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-37 IRI of the Pavement Section over the Years for Houston, Texas. .......................... 188
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-40 IRI 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-43 IRI of the Pavement Section over the Years for Paris, Texas. ............................... 191
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-46 IRI 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
Figure B-51 Base + Subgrade Rutting of the Pavement Section over the Years for Amarillo,
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
Figure B-54 Base + Subgrade Rutting of the Pavement Section over the Years for Austin, Texas.
..................................................................................................................................................... 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
Figure B-57 Base + Subgrade Rutting of the Pavement Section over the Years for Corpus
Christi, Texas. ............................................................................................................................. 198
Figure B-58 IRI of the Pavement Section over the Years for Corpus Christi, Texas. ................ 198
Figure B-59 Subgrade Modulus of the Pavement Section over the Years for Corpus Christi,
Texas. .......................................................................................................................................... 199
Figure B-60 Base + Subgrade Rutting of the Pavement Section over the Years for Dallas, Texas.
..................................................................................................................................................... 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
Figure B-63 Base + Subgrade Rutting of the Pavement Section over the Years for El Paso,
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
Figure B-66 Base + Subgrade Rutting of the Pavement Section over the Years for Houston,
Texas. .......................................................................................................................................... 202
Figure B-67 IRI of the Pavement Section over the Years for Houston, Texas. .......................... 203
Figure B-68 Subgrade Modulus of the Pavement Section over the Years for Houston, Texas. . 203
Figure B-69 Base + Subgrade Rutting of the Pavement Section over the Years for McAllen,
Texas. .......................................................................................................................................... 204
Figure B-70 IRI of the Pavement Section over the Years for McAllen, Texas. ......................... 204
Figure B-71 Subgrade Modulus of the Pavement Section over the Years for McAllen, Texas. 205
Figure B-72 Base + Subgrade Rutting of the Pavement Section over the Years for Paris, Texas.
..................................................................................................................................................... 205
xxiii
Figure B-73 IRI of the Pavement Section over the Years for Paris, Texas. ............................... 206
Figure B-74 Subgrade Modulus of the Pavement Section over the Years for Paris, Texas. ...... 206
Figure B-75 Base + Subgrade Rutting of the Pavement Section over the Years for San Antonio,
Texas. .......................................................................................................................................... 207
Figure B-76 IRI of the Pavement Section over the Years for San Antonio, Texas. ................... 207
Figure B-77 Subgrade Modulus of the Pavement Section over the Years for San Antonio, Texas.
..................................................................................................................................................... 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
Type D, PG 70-22 Initial two-way AADTT: 1000 (1)
40 ksi
8 ksi
(b) SH 197, Houston
(c) IH 10 Frontage Road,
El Paso
Type D, PG 70-22 Initial two-way AADTT: 1000 (1)
40 ksi
8 ksi
30
Figure 3-2 Pavement Sections Used for Analysis.
(d) US83 Uvalde County,
San Antonio
Material Properties Traffic (Lane in Design Direction)
(Modulus)
Type D, PG 64-22 Initial two-way AADTT: 650 (1)
65 ksi
35 ksi
14.9 ksi
(e) US 62, El Paso
Type D, PG 64-22 Initial two-way AADTT: 532 (1)
30 ksi
18 ksi
Type D, PG 64-22 Initial two-way AADTT: 612 (1)
Type C, PG 64-22
50 ksi
35 ksi
8 ksi
(f) SH 19, Paris
31
Figure 3-2 Pavement Sections Used for Analysis.
Material Properties Traffic (Lane in Design Direction)
(Modulus)
Type D, PG 64-22 Initial two-way AADTT: 200 (2)
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
Figure 3-2 Pavement Sections Used for Analysis.
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
Material Properties Traffic (Lane in Design Direction)
(Modulus)
Type C, PG 70-22 Initial two-way AADTT: 1500 (2)
Type D, PG 64-22
40 ksi
8 ksi
(j) S-44, Corpus Christi
Type C, PG 70-22 Initial two-way AADTT: 700 (2)
Type D, PG 64-22
30 ksi
16 ksi
(k) N_Mopac Expressway, Austin
Type D, PG 70-22 Initial two-way AADTT: 3000 (2)
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
IH 30 Frontage Road, Fort
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.
Table 5-4 Change in Maintenance Years for Selected Pavement Sections with IRI as
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
IH 30 Frontage Road, Fort
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
Table 5-5 Change in Maintenance Years for Selected Pavement Sections with IRI as
Maintenance Criteria
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
IH 30 Frontage Road, Fort
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).
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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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Table 5-6 Change in Maintenance Years for Selected Pavement Sections with IRI as
Maintenance Criteria
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.
Table 5-7 Change in Maintenance Years for Selected Pavement Sections with IRI as
Maintenance Criteria
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.
113
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
117
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
118
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.
120
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.
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.
123
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
125
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.
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
128
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
Corpus Christi 28.99 4.60 12.50
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
Corpus Christi 70.58 3.32 97.10
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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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
Figure B-21 Base + Subgrade Rutting of the Pavement Section over the Years for Amarillo,
Texas.
Figure B-22 IRI of the Pavement Section over the Years for Amarillo, Texas.
181
Figure B-23 Subgrade Modulus of the Pavement Section over the Years for Amarillo, Texas.
Figure B-24 Base + Subgrade Rutting of the Pavement Section over the Years for Austin, Texas.
182
Figure B-25 IRI of the Pavement Section over the Years for Austin, Texas.
Figure B-26 Subgrade Modulus of the Pavement Section over the Years for Austin, Texas.
183
Figure B-27 Base + Subgrade Rutting of the Pavement Section over the Years for Corpus
Christi, Texas.
Figure B-28 IRI of the Pavement Section over the Years for Corpus Christi, Texas.
184
Figure B-29 Subgrade Modulus of the Pavement Section over the Years for Corpus Christi,
Texas.
Figure B-30 Base + Subgrade Rutting of the Pavement Section over the Years for Dallas, Texas.
185
Figure B-31 IRI of the Pavement Section over the Years for Dallas, Texas.
Figure B-32 Subgrade Modulus of the Pavement Section over the Years for Dallas, Texas.
186
Figure B-33 Base + Subgrade Rutting of the Pavement Section over the Years for El Paso,
Texas.
Figure B-34 IRI of the Pavement Section over the Years for El Paso, Texas.
187
Figure B-35 Subgrade Modulus of the Pavement Section over the Years for El Paso, Texas.
Figure B-36 Base + Subgrade Rutting of the Pavement Section over the Years for Houston,
Texas.
188
Figure B-37 IRI of the Pavement Section over the Years for Houston, Texas.
Figure B-38 Subgrade Modulus of the Pavement Section over the Years for Houston, Texas.
189
Figure B-39 Base + Subgrade Rutting of the Pavement Section over the Years for McAllen,
Texas.
Figure B-40 IRI of the Pavement Section over the Years for McAllen, Texas.
190
Figure B-41 Subgrade Modulus of the Pavement Section over the Years for McAllen, Texas.
Figure B-42 Base + Subgrade Rutting of the Pavement Section over the Years for Paris, Texas.
191
Figure B-43 IRI of the Pavement Section over the Years for Paris, Texas.
Figure B-44 Subgrade Modulus of the Pavement Section over the Years for Paris, Texas.
192
Figure B-45 Base + Subgrade Rutting of the Pavement Section over the Years for San Antonio,
Texas.
Figure B-46 IRI of the Pavement Section over the Years for San Antonio, Texas.
193
Figure B-47 Subgrade Modulus of the Pavement Section over the Years for San Antonio, Texas.
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
Figure B-51 Base + Subgrade Rutting of the Pavement Section over the Years for Amarillo,
Texas.
Figure B-52 IRI of the Pavement Section over the Years for Amarillo, Texas.
196
Figure B-53 Subgrade Modulus of the Pavement Section over the Years for Amarillo, Texas.
Figure B-54 Base + Subgrade Rutting of the Pavement Section over the Years for Austin, Texas.
197
Figure B-55 IRI of the Pavement Section over the Years for Austin, Texas.
Figure B-56 Subgrade Modulus of the Pavement Section over the Years for Austin, Texas.
198
Figure B-57 Base + Subgrade Rutting of the Pavement Section over the Years for Corpus
Christi, Texas.
Figure B-58 IRI of the Pavement Section over the Years for Corpus Christi, Texas.
199
Figure B-59 Subgrade Modulus of the Pavement Section over the Years for Corpus Christi,
Texas.
Figure B-60 Base + Subgrade Rutting of the Pavement Section over the Years for Dallas, Texas.
200
Figure B-61 IRI of the Pavement Section over the Years for Dallas, Texas.
Figure B-62 Subgrade Modulus of the Pavement Section over the Years for Dallas, Texas.
201
Figure B-63 Base + Subgrade Rutting of the Pavement Section over the Years for El Paso,
Texas.
Figure B-64 IRI of the Pavement Section over the Years for El Paso, Texas.
202
Figure B-65 Subgrade Modulus of the Pavement Section over the Years for El Paso, Texas.
Figure B-66 Base + Subgrade Rutting of the Pavement Section over the Years for Houston,
Texas.
203
Figure B-67 IRI of the Pavement Section over the Years for Houston, Texas.
Figure B-68 Subgrade Modulus of the Pavement Section over the Years for Houston, Texas.
204
Figure B-69 Base + Subgrade Rutting of the Pavement Section over the Years for McAllen,
Texas.
Figure B-70 IRI of the Pavement Section over the Years for McAllen, Texas.
205
Figure B-71 Subgrade Modulus of the Pavement Section over the Years for McAllen, Texas.
Figure B-72 Base + Subgrade Rutting of the Pavement Section over the Years for Paris, Texas.
206
Figure B-73 IRI of the Pavement Section over the Years for Paris, Texas.
Figure B-74 Subgrade Modulus of the Pavement Section over the Years for Paris, Texas.
207
Figure B-75 Base + Subgrade Rutting of the Pavement Section over the Years for San Antonio,
Texas.
Figure B-76 IRI of the Pavement Section over the Years for San Antonio, Texas.
208
Figure B-77 Subgrade Modulus of the Pavement Section over the Years for San Antonio, Texas.
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.