Bottom-Up/Top-Down Analysis

A combination of three approaches described in the literature is proposed to evaluate ad- aptation options for road infrastructure [Kwakkel, Walker et al., 2016]. These approaches are de- cision scaling, robust decision making (RDM) [Kasprzyk et al., 2013; Lempert et al., 2006], and dynamic adaptive policy pathways (DAPP) [Haasnoot et al., 2013; Kwadijk et al., 2010;

Kwakkel, Haasnoot et al., 2016]. Three characteristics of pavement design make it uniquely well suited for these approaches. First, the pavement structure controls pavement performance. Vari- ous pavement structures are evaluated and compared using the decision scaling climate stress test [Ray and Brown, 2015; Taner et al., 2017]. Second, some pavement structures are more resilient than others to environmental/climate change and these robust designs can be identified using RDM. Third, pavement adaptation can be flexible through relatively short-term (20 year) pave- ment design cycles and maintenance/rehabilitation practices. Adaptation tipping points, de- scribed in the DAPP approach, can be identified as the point when the more robust and capital- intensive adaptation strategies, i.e. underlying layer modification or reconstruction, are needed. The figures presented in this section are used to illustrate the methodology and are not based on a real analysis.

The decision scaling climate stress test is used to determine pavement performance sensi- tivity to environmental/climate change [Ray and Brown, 2015; Taner et al., 2017]. Various

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pavement structural designs are evaluated for all possible combinations of the chosen climate variables providing the stakeholders with a Pavement/Climate Sensitivity Catalog (PCSC). The performance metric can be Nf ratios, damage factors, O-HMA thickness, or adaptation costs, etc.

The environmental/climate variables are chosen based on the study objectives, stakeholder prior- ities, and the study location. Temperature, groundwater levels, precipitation, flooding frequency, and freeze-thaw parameters are all important to pavement life [Daniel et al., 2017; Knott et al.,

2017; Ping et al., 2010; Roshani, 2014; Salour and Erlingsson, 2014] and can be analyzed using the climate stress test. This process is used to provide stakeholders with increased knowledge of the pavements’ structural response to environmental/climate change.

An example PCSC using temperature and groundwater rise is presented in Figure 6-1 (modified from [Taner et al., 2017]. Each box labeled (a) through (l) represents a different pave- ment structural design. The two environmental/climate variables used in this example are SLR- induced groundwater rise and temperature increase plotted on the x and y-axes, respectively. For each two-variable combination, the Nf ratio (pavement Nf for the specific variable combina-

tion/Nf for the existing structure and no climate change) is calculated. Nf ratios less than one, in-

dicating projected pavement life less than the existing pavement’s designed life, are highlighted in red. Nf ratios greater than one, indicating projected pavement life greater than the existing

pavement’s designed life, are highlighted in green.

Robust pavement structures are those whose pavement life equals or exceeds the design life for many combinations of environmental/climate variables. Robust pavement designs with thick asphalt and base layers address environmental/climate change uncertainty, but do not ex- plicitly address adaptation costs. Robust structures are expensive to construct and may not be necessary until later in the century when changes in climate parameters are more pronounced

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than in the short term. Using the example presented in Figure 6-1, structural designs (j), (k), and (l) are all robust, i.e. they out live the design life for most of the climate-variable combinations, but their relatively high cost may be a barrier to implementation early in the pavement manage- ment period because of budgetary constraints. Other factors such as unanticipated traffic

changes, asphalt mix design advances, maintenance improvements, and budget allocation, etc. all affect pavement service life and necessitate flexibility in pavement design and maintenance/reha- bilitation practices. Short-term planning cycles within the longer pavement management period [NHDOT, 2016] allow for some flexibility.

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Figure 6- 1. Example of a Pavement/Climate Sensitivity Catalog (PCSC) presented to illustrate the methodology (modified from [Taner et al., 2017]) - Pavement structural designs, ordered from weak to strong are shown in boxes (a) through (l). Nf ratios are shown for each climate-

variable combination. Projected Nf less than the design Nf are highlighted in red, equal to the

design life are white, and exceeding the design Nf are highlighted in green. Note: This figure is

for illustration only and is not based on real data.

An alternative approach chooses the pavement structures that maintain the designed ser- vice life, i.e. have Nf ratios equal or close to one despite environmental/climate change. These

structures, identified in the PCSC (Figure 6-1), are summarized in Figure 6-2.

Figure 6- 2.Optimal PCSC– Pavement structures (a) through (l) with Nf ratios close to 1. The

cell colors highlight the optimum pavement structures for various GW and temperature rise combinations. The ovals represent plausible areas of temperature/groundwater rise combinations based on downscaled GCMs with oval colors from blue to red representing climate projections from early to late mid-century. This figure is used to illustrate the methodology and is based on Figure 6-1 not real data.

This figure, hereafter referred to as the Optimal PCSC, presents only those pavement structural designs that meet this criterion. For example, options (a), (b), and (c), highlighted in light blue, are optimal for air temperature rise from 0 to 2°C and groundwater rise from 0 to 30 cm. Options (j), (k) and (l), highlighted in red, are optimal for air temperature rise from 5 to 7°C and groundwater rise from 60 to 90 cm.

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The PCSC documents the environmental/climate sensitivity of the various pavement structures but does not address the timing of these changes. Taner et al. (2017) used downscaled GCMs to narrow the set of possible combinations of environmental/climate parameters to a smaller set of plausible combinations based on RCP 4.5 and 8.5 [IPCC, 2013]. Plausible climate combinations can also be identified within user-defined time periods. Important time intervals for pavement management include the 10-year planning cycle, 20-year design period, or 60-year pavement management period [AASHTO, 2008; Christopher et al., 2006; Tanquist, 2012]. The U.S. DOT CMIP Climate Data Processing Tool can be used for processing the U.S. Bureau of Reclamation’s Downscaled CMIP5 Climate and Hydrology Projections (DCHP) [Reclamation,

2016] at geographical scales appropriate for transportation assets. CMIP5 data is available from 1950 through 2099 to project temperatures and precipitation for future periods of interest [ICF International, 2016]. Future groundwater levels, if chosen for analysis, can be determined using a groundwater model [Bjerklie et al., 2012; Habel et al., 2017; Knott et al., 2017; Walter et al.,

2016] forced by SLR projections [Sallenger et al., 2012a; Sweet et al., 2017].

Plausible areas of environmental/climate variable combinations from the downscaled cli- mate models are superimposed on the Optimal PCSC as shown on Figure 6-2. These plausible areas can represent climate-projections from multiple climate models and two or more scenarios. The size of the plausible areas increases with increasing uncertainty for longer-range projections. Plausible areas of temperature rise were calculated in Chapter 5 and shown on Figure 5-7. If the climate projections change in the future, the PCSC and Optimal PCSC will still be relevant and the plausible areas can simply be changed accordingly, introducing flexibility into the analysis. This demonstrates the hybrid nature of the approach where a bottom-up methodology establishes

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the asset’s sensitivity to environmental/climate change and the top-down (scenario-based) ap- proach identifies the plausible climate-variable combinations over time.