Understanding the impact of climate change on pavements with CMIP5, system dynamics and simulation

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1 International Journal of Pavement Engineering ISSN: (Print) X (Online) Journal homepage: Understanding the impact of climate change on pavements with CMIP5, system dynamics and simulation Rajib B. Mallick, Jennifer M. Jacobs, Benjamin J. Miller, Jo Sias Daniel & Paul Kirshen To cite this article: Rajib B. Mallick, Jennifer M. Jacobs, Benjamin J. Miller, Jo Sias Daniel & Paul Kirshen (2016): Understanding the impact of climate change on pavements with CMIP5, system dynamics and simulation, International Journal of Pavement Engineering, DOI: / To link to this article: Published online: 27 Jun Submit your article to this journal Article views: 69 View related articles View Crossmark data Full Terms & Conditions of access and use can be found at Download by: [University of New Hampshire] Date: 18 January 2017, At: 12:39

2 International Journal of Pavement Engineering, Understanding the impact of climate change on pavements with CMIP5, system dynamics and simulation Rajib B. Mallick a, Jennifer M. Jacobs b, Benjamin J. Miller b, Jo Sias Daniel b and Paul Kirshen c a Civil and Environmental Engineering, Worcester Polytechnic Institute (WPI), Worcester, MA, USA; b Department of Civil and Environmental Engineering, University of New Hampshire, Durham, NH, USA; c Civil and Environmental Engineering Department, Institute for the Study of Earth, Oceans and Space, University of New Hampshire, Durham, NH, USA ABSTRACT There is an increasing awareness regarding the impact of climate change on performance and durability of pavements. The objective of this paper is to present a framework of using the global climate forecasts, system dynamics and Monte Carlo analyses to evaluate the rate of change in climate change parameters and understand the impact of climate change on pavements. Changes in maximum air temperature and annual precipitation levels were determined for seven cities across the US, for RCP 4.5 and RCP 8.5 scenarios, using downscaled CMIP5 model output. A system dynamics model was utilised to link the changes in climate change-related parameters to deterioration and life of pavements. Percent of roads that need rehabilitation at different times, up to a time span of 100 years were predicted with simulations of the system dynamics model. Using regression equations developed on the basis of the output data, Monte Carlo analyses were then conducted to obtain distribution and 90% confidence intervals for percent of roads requiring rehabilitation at 50 and 100 years, for no climate change, climate change and climate change with different mitigation scenarios. The results clearly show the significant increase in deterioration of roads as a result of predicted climate change, compared to a no climate change scenario. The conclusions are that the CMIP5 model output can be utilised successfully to obtain statistical data regarding climate change parameters that are relevant for pavement design and that a sequential use of the tool, system dynamic and Monte Carlo simulation can be utilised by public agencies to estimate climate change-related parameters for different scenarios, the risk of negative impact of such change on pavement lives and evaluate the effectiveness of various mitigation approaches. This can help them in making justifiable decisions regarding the consideration of climate changes in design of pavements. ARTICLE HISTORY Received 12 March 2016 Accepted 24 May 2016 KEYWORDS Climate-change; CMIP5; Monte Carlo; system dynamics; pavement 1. Introduction A significant amount of research has been conducted in the recent past on assessing the impact of climate change on the transportation infrastructure (Mills et al. 2007a, 2007b, Transportation Research Board Special Report , Truax et al. 2011). Researchers have clearly shown the prospect of climate change, and its impact on design and life of pavements. There is adequate proof of climate change-related shortening of pavement life, for convincing engineers regarding the need for considering climate change in their regular designs (Meagher et al. 2012, Mallick et al. 2014), and for considering the reduced lifespan of structures (for example, due to an increase in carbonation-induced corrosion of concrete structures, (Talukdar et al. 2012). Although there is convincing proof of climate change, there are a significant amount of uncertainties in climate change projections. In the face of uncertainty, a risk analysis-based approach is the most suitable one for public agencies for making judicious decisions. The following are the two critical questions that need to be answered for the successful implementation of incorporating climate change factors in regular design. (1) What are the expected changes in the most relevant climate change parameters that influence the deterioration of roads? (2) What is the probable increase of deterioration of roads designed on the basis of current standards as a result of climate change? 1.1. Objective The objective of this paper is to propose and demonstrate the use of CMIP5 model output, system dynamics and Monte Carlo analysis to provide a framework to evaluate the rate of change in climate change parameters and quantify the impact of climate change on pavements. 2. Proposed approach Climate change predictions are stochastic in nature, and no single value of high-temperature increase or rainfall increase can be predicted with confidence for a specific region. As a result, CONTACT Rajib B. Mallick rajib@wpi.edu 2016 Informa UK Limited, trading as Taylor & Francis Group

3 2 R. B. Mallick et al. the impact of climate change on the performance of pavements, specifically on deterioration of pavements needs to be ascertained probabilistically. The results of such an analysis can then be utilised to understand the risks of loss as a result of climate change, benefits for different mitigation actions and also to develop budgets to address climate change-related damages. To tackle this problem, an approach consisting of the use of a climate change prediction system, systems analysis and Monte Carlo analysis has to be adopted. The steps are described briefly in the following paragraphs Climate change prediction Civil infrastructure is exposed to the elements day in and day out and often takes the brunt of impacts when extreme weather strikes. Because much of our transportation infrastructure that will be built or maintained is expected to be in place for 50 to 100 years, a changing climate is consistently considered to be a very real threat to infrastructure. The International Panel on Climate Change and the National Climate Assessment (Wilbanks et al. 2014) are the key source of climate change information for many transportation agencies (Meyer et al. 2013, U.S. Government Accountability Office 2013, Baglin 2014). Based on the scientific projections from these types of organisations, infrastructure analysts are able to estimate how climatic conditions will affect physical infrastructure components. In the future, it is likely that there will be higher temperatures and changes in mean precipitation but those changes will differ by region (Program 2014). Future temperature and precipitation depend on how far into the future a projection is made and future global emissions of greenhouse gases. There are also many different global models of climate. No model is perfect, thus the approach traditionally preferred by climate modellers is to analyse a number of models and consider the overall signal from the ensemble of models. Thus, a clear signal is likely to emerge from the ensemble of model forecasts. In the longer term, there is more variability of potential futures and developing specific numbers for likely changes is more challenging. There are a number of approaches being developed and tested for aggregating output from multiple models and future emission as well as developing plausible scenarios of the future climate Systems analysis A roadway (or an airport) pavement is not an isolated structure it is part of a system that consists of the multilayer structure and the environment that affects it. Furthermore, it is a dynamic system, as it is affected continuously by environmental factors, and it deteriorates over time. The environmental factors specifically that are most likely to be affected by climate change consist of maximum air temperature, sea water level rise, flood causing hurricanes and amount of annual rainfall. Changes in each of these factors affect the performance of the pavement and hence its rate of deterioration and life. To understand the full impact of all of these factors, a systems level approach for analysis is required an analysis that is holistic and includes all of the relevant factors and their interdependencies, and considers the time-dependent aspect of the problem. System Dynamics (SD) provides an ideal systems analysis approach that has been utilised by many researchers in different fields, including transportation and pavement engineering (Forrester 1971, Sterman 2000, Mallick et al. 2014). System dynamics 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 behaviour of the system over time. The behaviours of systems are simulated over time, and the quantities (x) that can decrease or increase in value are represented by stocks, the rate of increase or decrease (dx/ dt) is represented by flow and parameters that can affect flows, and in most cases, that can be controlled, are represented as converters. The links between these components are provided through connectors, which are essentially the equations that relate one parameter to other(s). The equations are developed on the basis of experimental or computational studies, or on the basis of answers to survey questionnaires. Most problems consist of systems with many interrelated and dynamic (change over time) factors and feedback Monte Carlo simulation The impact of climate change on the relevant pavement design factors is expressed in terms of distributions that consider the uncertainty in climate change projections. Coming from a combination of variability of different climate change-impacted factors, the overall variability in terms of pavement life or condition is always a concern for the pavement agencies because of its significance related to budget development and allocation. If the effects are properly understood then a rational and practical approach could be taken to reduce the risks associated with the impact of climate change. Such an understanding can be achieved through the use of probabilistic analysis, whereby a large number of samples can be taken consecutively and the overall impact on the distribution of the parameter of interest (such as pavement condition or life) can be calculated. The reasons for selecting a probabilistic approach are: 1. Variations in climate change, losses encountered due to climate change-related phenomena, and costs associated with mitigation or repair actions are expected and cannot be avoided; 2. If we consider a single mean value for each property ( deterministic approach), the values will be best guesses and may not be appropriate; 3. The resulting values from consideration of single values from the models will exclude the probabilities of risk, and hence will not help in the decision-making process; 4. The exclusion of uncertainty and risk generates unproductive debate and encourages division among stakeholders (Lewis 1995). The tool of choice for such probabilistic analysis is the Monte Carlo analysis technique (Metropolis and Ulam 1949, Walls and Smith 1998). This technique generates a value of the dependent variable, based on a random selection of data from the input variables, which are prescribed by the user as a normally (or some other type) distributed variable(s). The simulation continues until all the requested samples (for example 1000 simulations) have been conducted, and finally, reports the expected distribution of the dependent variable. The benefit is that the resulting distribution shows the range of numbers that we can expect for the dependent variable and higher the number of simulations, better the prediction.

4 International Journal of Pavement Engineering Risk analysis with Monte Carlo simulation Risk analysis helps decision-makers successfully manage situations that are subject to uncertainty (Walls and Smith 1998). The impact of climate change on pavements consists of many uncertainties, and single point estimates may lead to over conservative or over optimistic evaluations in different cases, and the combined errors from such estimates can lead to a prediction that is significantly different from reality. Single point estimates do not provide a complete picture of all expected outcomes, and can lead to significantly wrong decisions. A probabilistic risk analysis helps the decision-makers to explicitly include the uncertainty of all the factors, conduct simulations by combining the uncertainties of all the factors and get the entire range of expected values for the parameter(s) of interest, and the likelihood of occurrence of each value. Hence, risk analysis allows one to run many what-if scenarios (scenario analysis). The term Risk becomes relevant when a given action or a situation (outside the control of the decision-maker) has more than one outcome, and the range of outcomes is significant in some way, for example, loss of life, money and property in the case of infrastructure management. When decisions need to be taken on the basis of the outcomes, and the amount of Risk dictates the specific course of action, Risk Analysis becomes a necessary and critical tool for the decision-makers. There are three steps in risk analysis: (1) Defining the model in terms of the different parameters. (2) Identifying and including the uncertainties of the parameters. (3) Analysing the model with simulations. Step 3 can be repeated for different scenarios or decisions and hence the risks associated with different decisions can be evaluated, and on the basis of those results the most favourable decision can be made. In the face of uncertainty, risk analysis ensures that those strategic decisions are made on the basis of all available data at any point of time. Note that step 1, the definition of model has been accomplished in this paper with the help of system dynamics (SD), step 2 has been either taken or assumed on the basis of available literature (can be based on experience also) and step 3 has been conducted on the basis of Monte Carlo simulation. Therefore, note that the techniques mentioned above need to be utilised sequentially and in combination, for achieving the stated objective. 3. Plan of analysis The plan of analysis includes the following. (1) Determine mean and standard deviation of predicted change in climate change-related parameters for two different scenarios. (2) Utilise system dynamics to model the pavementclimate change system that includes all of the relevant parameters that are affected by climate change, and that in turn, affect pavement deterioration and life. Run the model with different values of climate change-related factors (x values), as obtained from Step 1, to generate a matrix of results in terms of percent of roads that need rehabilitation at any time (y values). (3) Based on the data obtained in step 2, develop regression models that relate climate change-related parameters (x values) to percent of roads that need rehabilitation at any time (y values). (4) Conduct Monte Carlo simulation to determine probabilities related to different percent of roads that will need rehabilitation as a result of a higher rate of deterioration due to climate change. Note that in this paper, a generic design (Mallick et al. 2014) (asphalt pavement with 75-mm thick Hot Mix Asphalt, HMA, surface, 400 mm of crushed aggregate base, 600 mm of sand and a A-1-b subgrade) has been utilised to evaluate the impact of climate change on the deterioration of the pavement, in terms of its fatigue life. The same approach could be adopted for any design or location, and for other types of distresses, to conduct site and pavement specific evaluations of climate change impacts. Also, rehabilitation refers to major work that is needed to improve the structural capacity of the road, and does not include regular maintenance work that is required for keeping the rideability at a specific level Determination of climate change related factors The Intergovernmental Panel on Climate Change (IPCC) assesses multiple global climate models (GCMs) as part of the international climate change assessment reports. The World Climate Research Programme creates the framework for the models, making the analysis of multiple models much simpler; the aggregation of these models is called the Climate Model Intercomparison Project (CMIP). The most recent Climate Model Intercomparison project is CMIP5 (released 2013). The CMIP5 data-set contains output from a large number of GCMs and several emission scenarios. The CMIP5 scenarios use varying paths to reach different levels of greenhouse gas concentrations and include a lower to higher degree of climate change. The CMIP5 collection of GCMs consists of output from currently available GCMs and is the standard climate model output which is used for applications. The mean and standard deviation of rate of change of climate change-related factors were determined for the two different emission scenarios and for a set of cities across the US and are shown in Table 1. Daily temperature and precipitation model output was obtained for the period using Maurer et al. s (2007) Coupled Model Intercomparison Project, CMIP5 climate and hydrology projections downscaled to 1/8 degree rectangular grids, four of which were selected per city. The projections consisted of output from an ensemble of nine or ten GCMs for low (Representative Concentration Pathways, RCP 4.5) and high emission (RCP 8.5) scenarios, respectively (Taylor et al. 2012, Reclamation, 2013, 2014, Paul et al. 2014). Maximum air temperature and total precipitation were determined annually and used to calculate rates of change for the period Normality was tested and the Anderson Darling test detected no departure from normality for the distribution of temperatures trends and only two departures for precipitation (Phoenix RCP4.5 and Seattle RCP8.5). Thus, Monte Carlo simulations were

5 4 R. B. Mallick et al. Table 1. Mean and standard deviation (SD) of rate of change of climate change-related factors for low (RCP 4.5) and high emission (RCP 8.5) scenarios from by site. P-values from the Anderson Darling (AD) normality test results where p > 0.05 indicates failure to reject null hypothesis of normality. Factor Maximum air temperature, C/year Precipitation, mm/year RCP 4.5 RCP 8.5 RCP 4.5 RCP 8.5 AD AD AD AD Site (Mean, SD) P-value (Mean, SD) P-value (Mean, SD) P-value (Mean, SD) P-value Atlanta, GA 0.025, , , , Boston, MA 0.025, , , , Chicago, IL 0.026, , , , Denver, CO 0.028, , , , Durham, NH 0.025, , , , Phoenix, AZ 0.023, , , , Seattle, WA 0.021, , , , Figure 1. Map of the system dynamics model. conducted using a normal distribution with the calculated mean and standard deviation values System dynamics modelling An appropriate System Dynamics (SD) model (Mallick et al. 2014) that captures the problem of defining the impact of climate change on pavement life (over a long period of time) was developed with STELLA software (ISEE systems, 2015). The map of the SD model, its equations and the range of climate changerelated parameters and their ranges are presented in Figure 1 and Table 2, respectively (for details, please see Reference Mallick et al. 2014). The model was created through the following steps. 1. Two pavement design factors that would be impacted by climate change were considered: maximum air temperature (F1) and average rainfall (F2). 2. Equations relating F1 to maximum pavement temperature and F2 to soil resilient modulus (through a change in the time for which the subgrade soil would be saturated) were identified from existing literature; 3. A two-lane highway with HMA surface, crushed aggregate base and an A-1-b subgrade was analysed, using the different maximum pavement temperatures and the soil subgrade moduli, as caused by the changes in F1 and F2, respectively, due to climate change and the rutting life (since rutting was found to be the more critical distress, compared to the other distress, fatigue cracking and low temperature cracking) were determined for each case. The rutting lives were then used to determine the percentage of roads that will need rehabilitation at any time, which were

6 International Journal of Pavement Engineering 5 then used as dependent variables, and the change in F1 and F2 was considered as independent variables to develop a regression equation of percent of road that will require rehabilitation at 50 and 100 years. (Mallick et al. 2014). Note that the two other parameters which were indicated in Reference Mallick et al. 2014, an increase in sea water level and increase in number of hurricanes that cause flooding have not been utilised in this paper because of the relatively great acceptance of the predictions for the temperature and precipitation parameters, and their applicability to a wider range of locations (such as those which are not on the coast and not vulnerable to sea water level rise). The SD model relates climate change to pavement deterioration and life through two parameters, the effective subgrade modulus and the minimum HMA modulus. Two additional parameters have been included in this model the option to use a stabilised subgrade and another one to use a modified HMA. The effect of stabilisation has been considered as an additional 25% increase in the modulus of the subgrade, whereas the effect of using a modified HMA has been considered as an increase in the minimum modulus of the HMA, as a function of the maximum pavement temperature (Table 2). The reasoning is that depending on the maximum pavement temperature, specific types of modifiers (that will result in specific increase in modulus) are likely to be utilised, and hence the increase in modulus increases with an increase in the maximum pavement temperature. The output from the simulation of the SD model that is utilised in this paper is the percent of roads that need rehabilitation, i.e. the percent of roads that have reached the end of their lives. Note that any time, the mileage of roads that is (actually) rehabilitated has been considered to be a fraction, and a function of the total mileage of roads that require rehabilitation, as shown in Figure 2. The plot in Figure 2 was developed on the basis of a table of total roads needing rehabilitation and roads actually rehabilitated. For example, it was assumed that if there is 10 km of road that needs total rehabilitation, 50% will be actually rehabilitated; however, if there is 100 km of road that needs rehabilitation, only 5% of it will be actually rehabilitated. This assumption is based on the fact that in general, state departments of transportation (DOTs) have a fixed budget for pavement rehabilitation, and only a fixed mileage of roads (and NOT a fixed percentage of roads that require rehabilitation) could be rehabilitated at any point of Table 2. Equations used in the SD model. Average Pavement Life = *Effective Resilient Modulus of Subgrade Soil *Temperature Adjusted Minimum HMA Modulus Effective Resilient Modulus of Subgrade Soil = 26500*((12-Months with 100%_ Saturation Adjustor for Maximum of 8_months)/12)+8690*(Months With 100% Saturation Adjustor for Maximum of 8 months/12) *0.25*(Policy to use improved subgrade) Increase in 100% Saturation Months = (0.0071*Rate of Change in Average Annual Rainfall) Pavement Temperature Increase = 0.78*Rate of Change in Maximum Air Temperature Temperature Adjusted Minimum HMA Modulus = *Maximum Pavement Temperature+500*Maximum Pavement Temperature*Policy to Use High Modulus HMA Note that the equations were derived from a study reported in Reference Mallick et al Figure 2. Assumed function for fraction of roads rehabilitated at any time. Table 3. Changes in key climate factors considered for SD analyses to develop regression equation (selected from data presented in Table 1). Rate of change, per year Minimum Maximum Mean Max air temperature, C per year Standard Deviation Rainfall, mm per year time. Furthermore, there is an ever-increasing budget shortfall for DOTs, which is preventing them from attending to all roads that require rehabilitation work. As a result of this consideration, it is expected (and will be seen in the results of the simulation later) that the percent of roads that require rehabilitation will increase over time, even if there is no climate change, although, in that case the rate of change will slow down over time. The SD model was utilised to simulate the behaviour of the pavement-climate change system in the following way. Specific values of the two parameters (change in maximum air temperature and annual precipitation, indicated in Table 3, extracted from Table 2) were input in the model, and through the different linkages, the percent of roads requiring rehabilitation (at every year for a period of 100 years) were obtained. The utility of the SD model is that apart from relating parameters from different disciplines (climate change and pavement life), it also helps in understanding the dynamic nature of the problem to evaluate the evolution of the behaviour of the system over time. The percent of roads that require rehabilitation accumulates over time, and the cumulative effect can be seen as a non-linear growth in the value. The results of the 10 sets of simulations with the SD model are shown in Table 4. Note that when one parameter was varied, the (mean) values of the other parameters were held constant in the model. The data were utilised to develop linear regression models (because of the high R 2 ), which are also shown in Table 4. These equations are required since there is no direct relationship between climate change parameters and the percent of roads that require rehabilitation at any specific time. Two models were developed one for the percent of roads that need rehabilitation in 50 years and another for those that need rehabilitation in 100 years. As it can be seen, the impact is higher for 100 years than for 50 years. Note that for the purpose of consideration of climate change (small change in relatively short period of time), it is imperative that long periods of time are considered for analysis.

7 6 R. B. Mallick et al. Table 4. Results from running the SD model. Rate of change, per year Values Percent of roads that need rehabilitation after 50 years, % Percent of roads that need rehabilitation after 100 years, % Maximum air temperature, C per year Annual rainfall, mm per year No consideration of climate change Percent of roads that need rehabilitation in 50 years = *(Rate of change in maximum air temperature) *(Rate of change in annual precipitation) R 2 = 0.99; p values for all parameters < Percent of roads that need rehabilitation in 100 years = *(Rate of change in maximum air temperature) *(Rate of change in annual precipitation) R 2 = 0.99; p values for all parameters < Rate of change in maximum air temperature C/year, annual rainfall mm/year Climate change and high modulus HMA 0063, Climate change and stabilised subgrade 0063, Climate change and high modulus HMA and stabilised subgrade 0063, Figure 3. Plots of time versus percentage of roads that need rehabilitation (based on data presented in Table 3). The impact of climate change can be mitigated using appropriate designs and/or materials of construction. There can be many alternatives for such mitigation work, and an exhaustive evaluation of all is not possible within the scope of this paper. Three alternatives were considered here. Mitigation action 1 consists of using a higher modulus asphalt mix (for example, by using modified asphalt binder), mitigation action 2 consists of using an improved subgrade (with a higher modulus, for example through stabilisation) and mitigation action 3 consists of both of the above approaches. Figure 3 shows the results of five simulations that were run to evaluate the impact of different scenarios no climate change, climate change with mean values of parameters, climate change plus mitigation action 1, climate change with mitigation action 2 and climate change with both mitigation actions (1 and 2). It can be seen that the net result of the mitigating action(s) is to bring down the percent of roads needing rehabilitation with consideration of climate change to, or close to, a level that is similar to that obtained without the consideration of climate change, or lower. For the cases considered here, mitigation action 2 is more effective than mitigation action 1, and the combination is, as expected, most effective. Note that these are presented just as example scenarios, there can be several other types of mitigation actions, and the results are dictated by the equations that are shown in Table 2, which were generated from repeated simulations with the MEPDG (explained in Mallick et al. 2014) hence they are dependent on the basic ME models also. The main purpose of this exercise is to demonstrate that if roads are continued to be designed and maintained in the way they are done at present ( business as usual ), because of climate change, a significantly more mileage of roads will require rehabilitation at any point of time in the future. Another purpose is to help determine which mitigating action could be more/most effective. For the cases considered here, mitigation action 2 (which considers an improved subgrade) and 3 (combination of improved subgrade and high modulus asphalt mix) could be applicable for pavements which require longer periods of time between rehabilitation work, due to the significant negative impact of lane closures during construction. Examples of such pavements include dedicated bus lanes in high traffic volume areas in major cities, special lanes for trucks and airport pavements, and evacuation routes which can be impacted by climate changes, and which are critical elements in the transportation infrastructure. Once the effectiveness of several mitigation options is evaluated, DOTs can use a cost benefit analysis to select the most optimum one. Next, using these regression models and the values of the mean and standard deviations shown in Table 1, Monte Carlo analyses were conducted to determine the distribution of percent of roads needing rehabilitation as a result of the change of two climate change-related parameters for two emission scenarios (RCP 4.5 and 8.5) for seven cities across the US. An example of such analysis (for the city of Atlanta, GA) is shown in Figure 4, which shows that in 100 years, the 90% confidence interval for percent of roads requiring rehabilitation is ( ) for the RCP 4.5 scenario, and ( ) for the RCP 8.5 scenario.

8 International Journal of Pavement Engineering 7 Figure 4. Example of results of Monte Carlo simulation, Atlanta, GA. Figures 5a d shows the predictions for percent of roads requiring rehabilitation at 50 and 100 years for the two scenarios, for each of the seven cities. Clearly, the percentage increases significantly from the present to the future in all cases, and the mean value approximately doubles and triples the No Climate Change value in 50 and 100 years, respectively. Consider, for example, the plots for Seattle, WA. If a no climate change scenario is considered, then the percent of roads that require rehabilitation will stabilise at 14% in 50 years. If the RCP 4.5 scenario is considered then the percent will increase to 15 18% in 50 years, and 18 24% in 100 years; for the RCP 8.5 scenario, the increase is from 17 22% in 50 years to 23 33% in 100 years. The implication is that the effect of climate change will result in a significant increase in the demand for rehabilitation, which will increase over time no matter which scenario is considered. This, again, highlights the necessity for considering climate change in design of pavements across the US. Note that for the sake of this paper, a specific probability distribution for each climate change-related factor (Table 1) has been considered to be valid for the entire 100 years (for which the simulations were run) of analysis. Strictly speaking, that may not be the case, and projections for that long a period of time could best be done by scenario analyses (ISEE systems, 2015). However, such an analysis is outside the scope of this paper.

9 8 R. B. Mallick et al. Figure 5a. Predictions for percent of roads requiring rehabilitation for different sites and scenarios. Figure 5c. Predictions for percent of roads requiring rehabilitation for different sites and scenarios. Figure 5d. Predictions for percent of roads requiring rehabilitation for different sites and scenarios. Figure 5b. Predictions for percent of roads requiring rehabilitation for different sites and scenarios. 4. Conclusions and recommendations The following conclusions and recommendations are made on the basis of the study presented in this paper. (1) Maurer et al. s (2007) downscaled CMIP5 climate and hydrology projections of the CMIP5 multi-model

10 International Journal of Pavement Engineering 9 ensemble can provide model output at appropriate scales, which is needed to generate mean and standard deviation values of rate of change of climate change-related parameters across the United States. (2) A system dynamics approach could be utilised successfully to integrate the different climate change-related parameters that are relevant for pavement design/performance, to build models for prediction of pavement performance under different climate change scenarios. (3) Monte Carlo simulation with properly developed statistical models and variability of parameters can be conducted to estimate the risk of pavement failures/ percent of pavements requiring rehabilitation for different emission scenarios. (4) A combined use of the downscaled CMIP5 projections, system dynamics and Monte Carlo simulation can be utilised by DOTs to understand, analyse and prepare for the impacts of climate change on roadways. Acknowledgements The authors acknowledge the National Science Foundation support via the RCN-SEES: Engineering Research Collaboratory for Sustainable Infrastructure in a Changing Climate [CBET ]. Disclosure statement No potential conflict of interest was reported by the authors. Funding This work was supported by National Science Foundation support via the RCN-SEES: Engineering Research Collaboratory for Sustainable Infrastructure in a Changing Climate [grant number CBET ]. References Baglin, C., NCHRP synthesis 454: response to extreme weather impacts on transportation systems. National Cooperative Highway Research Program & Transportation Research Board of the National Academies, Report nr NCHRP Synthesis 454. Forrester, J.W., World dynamics. 2nd ed. Cambridge: Wright Allen Press. ISEE systems STELLA v Lewis, D., The future of forecasting risk analysis as a philosophy of transportation planning. TR News. 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