Framework
The rail, roads, and coastal property analyses presented herein employ a consistent analytic framework to evaluate the impacts of climate change under three alternative infrastructure management scenarios: No Adaptation, Reactive Adaptation, and Proactive Adaptation. The sector-specific applications of these management scenarios are discussed in the respective sectoral method sections below, but are described here in general terms, with the term “damage” defined as the physical manifestation of climate effects, for our purposes, and the term “cost” as the economic implication of either repairing damage or, in the case of user and indirect costs, of failing to repair the damage. The No Adaptation scenario represents a “business as usual” approach to infrastructure management which does not incorporate climate change risks into the maintenance and repair decision making process beyond baseline expectations. In the Reactive Adaptation scenario, decision makers respond to climate change impacts by repairing damaged infrastructure, but do not take actions to prevent or reduce future climate change impacts. In the Proactive Adaptation scenario, decision makers take adaptive action with the goal of preventing infrastructure repair costs associated with future climate change impacts. These actions often involve trading higher upfront expenditures for potential future benefits or savings. This Proactive Adaptation scenario assumes well-timed infrastructure investments, which may be overly optimistic given that such investments have oftentimes been delayed and underfunded in the past, and because decisionmakers and the public are typically not fully aware of potential climate risks. Finally, the framework adopted here largely follows that developed for Melvin et al. (2017) for the Reactive and Proactive scenario definitions—but the No Adaptation scenario provides additional useful context for the scale of risk-motivated action that would be required to adapt infrastructure in each sector. A summary of the overall response scenario framework is provided in Table 1.
Table 1 Summary of costs included in the three adaptation scenarios Climate projections
The climate projections were selected to be consistent with those used in the Fourth NCA (USGCRP 2017). The analyses used Representative Concentration Pathway (RCP) 8.5 as a higher greenhouse gas emissions scenario and RCP 4.5 as a lower scenario. The RCPs are identified by their total approximate radiative forcing (in watts per square meter) in the year 2100 relative to 1750. The analyses employed five global climate models (GCMs) selected based on criteria detailed in U.S. EPA (2017) and the associated technical appendix. The five models applied here were from the Canadian Centre for Climate Modeling and Analysis (CanESM2); the National Center for Atmospheric Research (CCSM4); the NASA Goddard Institute for Space Studies (GISS-E2-R); the Meteorological Office at the Hadley Centre (HadGEM2-ES); and the Atmosphere and Ocean Research Institute, National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology (MIROC5).
The analyses use 20-year climatic time periods to ensure that results for any single year do not over- or underrepresent the characteristics of a future period from any of the five models. Results for the 20-year eras are represented using the central year in each period: 2030 (2020–2039); 2050 (2040–2059); 2070 (2060–2079); and 2090 (2080–2099). The baseline scenario, 1986–2005, is designed to reflect current climatic conditions.
The ten combinations of RCPs and GCMs were downscaled from the native GCM spatial resolution to a 1/16-degree resolution covering the contiguous USA. The dataset, called LOCA (which stands for localized constructed analogs), features a statistical downscaling technique using a multi-scale spatial matching scheme to pick appropriate analog days from observations. The LOCA dataset provides daily projections through 2100 for three variables: daily maximum temperature (tmax), daily minimum temperature (tmin), and daily precipitation (see US Bureau of Reclamation 2016 and supplemental materials for more details and download link). Projections of SLR and SS for each RCP are based on NOAA (2018) and Sweet et al. (2017). All analyses reflect adjustments over time for population and economic growth, including adjustments to traffic volume for the rail and road sectors, and adjustments to property value for the coastal property sector. Details are provided below and in the supplemental material.
Rail
Chinowsky et al. (2017) estimated the impacts of projected changes in temperature on the currently existing (as of 2015) Class 1 rail network in the USA. Specifically, this work analyzed a Reactive Adaptation scenario in which operators implement “blanket” speed restrictions during periods of high temperature to avoid track buckling events, and a Proactive Adaptation scenario in which operators use track temperature sensors to optimize their speed restrictions. The analysis estimated the costs of delays in each scenario, along with the capital costs of installing sensors in the Proactive Adaptation scenario.
The current analysis expands on the 2017 work by estimating repair and delay costs in a No Adaptation scenario in which operators do not reduce speeds when temperatures increase, resulting in an increased risk of track buckling. There is also a potential for track buckling to cause train derailment, but recent literature suggests that less than four percent of derailments are caused by buckled track (Liu, Saat, and Barkan, 2012). We therefore do not model derailments or associated costs and damages, which may result in an underestimate of costs for the No Adaptation scenario.
We estimate the number of track buckling events using Equation (1) (Kish and Samavedam 2013):
$$ {e}_b=\left({P}_b\times {P}_T\times {n}_t\times 365\times L\right)/\left({L}_t\right) $$
(1)
where
- Pb:
-
probability of buckling at rail temperature
- Pt:
-
annual rail temperature frequency
- nt:
-
number of trains per day
- L:
-
total length of track
- Lt:
-
length of train
We assume that Pb increases exponentially between Tb,min and Tb,max based on work by the Volpe Center, Foster-Miller, Inc. (FMI), and the Federal Railroad Administration (FRA) as part of the CWR-SAFE program, described in Appendix C of Kish and Samavedam (2013). Additional details on this approach are provided in the Supplementary Material.
We estimate costs of repairing damage associated with buckling events, including (1) costs of replacing track to repair lateral alignment defects in the buckling zone and (2) costs of re-aligning rail in adjoining zones. Based on national average cost data, we estimate that repairs for each track buckling event would take 14 h and cost $21,000 (Gordian 2017). By comparison, a UK study estimated that the average repair cost associated with a buckling event is £10,000 (approximately $15,000) (Hall and Jenkins n.d.). Delay costs associated with track buckling events are difficult to estimate due to the lack of publicly available data. For freight traffic, we follow the approach described in Chinowsky et al. (2017). However, we do not quantify costs associated with emissions for trains that are stopped for repair of buckling events because there would be no locomotive emissions. For passenger rail, we assume that passengers would de-board trains that are stopped due to a buckling event and find an alternative mode of transportation to reach their destination, with an estimated total delay time of 8 h. To quantify the costs of passenger delay, we rely on DOT’s 2016 guidance for the valuation of travel time in economic analysis (U.S. DOT 2016).
In the Reactive Adaptation scenario, we assume that train operators implement speed orders that result in blanket speed reductions for operations occurring in areas where the expected daily high exceeds a temperature that is deemed unsafe, consistent with their on-the-books policies. We estimate train delay from extreme heat events according to our 2017 work. Train-delay minutes are calculated at a grid level in accordance with the granularity of the climate projections. Since impacts are limited to estimates at the grid level, impacts are averaged over the inventory in that grid cell to reflect the granularity of the projection. In this grid approach, the generalized approach is summarized as follows:
$$ {\mathrm{TDM}}_g=\left(\raisebox{1ex}{${L}_g$}\!\left/ \!\raisebox{-1ex}{${S}_r$}\right.-\raisebox{1ex}{${L}_g$}\!\left/ \!\raisebox{-1ex}{${S}_o$}\right.\right)\times 60\times \left({H}_d/{H}_o\right) $$
(2)
where
- TDMg:
-
train delay minutes per grid
- Sr:
-
reduced speed
- So:
-
base speed
- Lg:
-
total length of rail traveled per grid
- Hd:
-
hours of speed order
- Ho:
-
hours of railroad operation
In this method, train delay minutes are first calculated based on a speed restriction, the length of track in the grid, and the number of hours in which the speed order will be put into effect.
Once the total delay minutes are calculated on a per grid basis, the delay minutes per year are calculated by multiplying the delay minutes per grid by the average volume of trains per grid and the number of incident days per grid (defined as a day in which a speed order is put into place). Finally, the delay minutes are quantified as costs using a unit cost per minute of delay, referenced in the supplemental material.
Additionally, we update the methods to incorporate estimates of track buckling in this scenario because, although blanket speed orders reduce the probability of track buckling, they do not eliminate them. In the Proactive Adaptation scenario, we assume train operators install track temperature sensors which enable them to use a risk-based approach to speed orders. A reduction in speed to avoid buckling is calculated based on temperatures using Eq. 3 (Kish and Samavedam 2008).
$$ \frac{V_r}{V_{\mathrm{max}}}={\left(1-\frac{P_b(T)}{P_b\left({T}_L\right)}\right)}^{.5} $$
(3)
where
- Vr:
-
reduced speed
- Vmax:
-
permissible maximum authorized line speed
- Pb(T):
-
buckling probability at track temperature, T
- Pb(TL):
-
buckling probability at limiting temperature, TL
The limiting temperature for track safety is therefore the maximum allowable temperature (Tallowable) above the neutral temperature of the rail.
We assume that the risk-based approach reduces the probability of a track buckling event to zero. The costs in this scenario include the costs of purchasing, installing, and maintaining the track temperature sensors, and related software infrastructure, as described in Chinowsky et al. (2017). In addition, we estimate the costs of delay associated with risk-based speed reductions.
Roads
Our previous analysis of the current US roads network focused on evaluating the potential impacts of climate change in a Reactive Adaptation scenario, in which roads are repaired (to maintain service levels) in response to climate change-related damage, and in a Proactive Adaptation scenario, in which roads are protected and rehabilitated to prevent future impacts from climate stressors (U.S. EPA 2017). The current analysis expanded on this work by incorporating estimates of the costs to road users, quantified as delay costs and vehicle operating costs (VOCs).
We developed a No Adaptation scenario in which decision makers limit their annual spending on repairs due to climate change costs to what they spent historically (in the period 1986–2005). We assume that in making repairs, road managers prioritize repairs related to damage from extreme precipitation and precipitation (as opposed to temperature) since these damages are more severe and require attention in the current fiscal year. Once the damages from precipitation are addressed, the remaining budget is then used to repair temperature-based damages. In the case where precipitation damage remains, the continuation of these damages is not currently modeled due to a lack of previous research on which a generalized damage function can be based. However, in most cases, the remaining budget is used to address temperature damages. If repair costs exceed the budget, we model the potential for continued damage to roads by measuring the increased roughness using the international roughness index or “IRI.”
Based on research by Qiao et al. (2013), we developed a generalized correlation between temperature increase and rutting, which assumes that rut depth increases gradually and linearly over the life span of the road, and that in a year in which the average temperature increases by 5% (a climate stressor measure that is consistent with the underlying literature), there will be an additional 0.04 inches of rutting. Then we translate the estimated rut depth to IRI using a model from the Guide for Mechanistic-Empirical Design of New and Rehabilitated Pavement Structures (ARA, Inc. 2001) using Eq. 4:
$$ \mathrm{IRI}=57.56\times RD-334 $$
(4)
where
- IRI:
-
smoothness in cm/km
- RD:
-
rut depth, mm
We then estimate the costs associated with increased IRI, which include (1) increased VOCs resulting from driving on damaged roads, based on research by Barnes and Langworthy (2004), and (2) delays from vehicles traveling at reduced speeds to navigate damaged roads, based on research by Wang et al. (2013). Barnes and Langworthy detail the VOCs for 3 types of vehicles, automobile, pickup/van/SUV, and commercial truck for each VOC category. Two sets of VOCs were estimated for a smooth pavement and a poor pavement quality—we average the results.
Using this relationship and traffic values, the total change in VOC for a given road was estimated. Grid level traffic data was estimated using national and state level traffic census programs. Traffic was split into average daily traffic (ADT) and average daily truck traffic (ADTT). The annual VOCs for a road segment are calculated using Eq. 5:
$$ \mathrm{Annual}\ \mathrm{VOC}=\frac{{\mathrm{VOC}}_{\mathrm{unit}}}{100}\times \mathrm{ADT}\times L\times 365 $$
(5)
where
- VOCunit:
-
unit vehicle operating cost for vehicle type (cents/mile)
- L:
-
length of road (miles)
- ADT:
-
average daily traffic (vehicles per day)
Various studies have quantified the effect of roughness on travel speed. We rely on a California Department of Transportation study that found that a one unit change in IRI leads to a 0.48-km/h change in free-flow speed (Wang et al. 2013). Although these changes in free-flow speed are small relative to the posted speed limits, there can still be a significant impact on delay when considering across the entire US road system. Using this relationship between IRI and free-flow speed, the decrease in speed was calculated for PSR values—the conversion from IRI to PSR is described in the supplemental material. The cost of delay from change in free-flow speed can be calculated for a given road using Eq. 6:
$$ \mathrm{Total}\ \mathrm{cost}\ \mathrm{of}\ \mathrm{delay}=\left(\frac{L}{V-\Delta V}-\frac{L}{V}\right)\times \mathrm{ADT}\times 365\times {C}_D $$
(6)
where
- L:
-
length of road (miles)
- V:
-
posted speed limit
- ΔV:
-
change in free-flow speed
- ADT:
-
average daily traffic (vehicles per day)
- CD:
-
unit cost of delay (USD/vehicle-hour)
To estimate delay for the Reactive and Proactive Adaptation scenarios, we utilized national and state DOT productivity rates to estimate the disruption time for various types of road work associated with stressor-related maintenance and adaptation, as described in the supplemental material.
To account for the potential for vehicles to avoid damaged roads or roads undergoing repair work, we developed an index representative of redundancy in the road network. Specifically, we developed ratios for each 0.25-degree grid cell of road infrastructure to population and normalized the ratios on a scale from zero to one, and then multiplied them by our estimated delay time to account for redundancy. To quantify the costs of delay for passenger vehicle travel, we rely on the value of travel time savings estimates from U.S. DOT (2016). To quantify the cost of delay for freight vehicle travel, we rely on data from the National Cooperative Highway Research Program (NCHRP) that are used as inputs to their Truck Freight Reliability Valuation Model (NCHRP 2016).
Coastal property
The coastal analysis builds on a long-standing framework, the National Coastal Properties Model (NCPM; Lorie et al. 2020; Neumann et al. 2015b), which assesses SLR and SS risks to the existing footprint of coastal development. Since the last published description, modifications have been made to the NCPM. These include updating SLR projections, updating SS heights, improving the adaptation decision rules, and improving model initialization. These four modifications are described briefly below. More detail on these, as well as an update to property values, is described in the Supplementary Material.
Sea level rise projections
For the Fourth NCA, Sweet et al. (2017a) developed a set of six global SLR scenarios that used the Kopp et al. (2014) projections to tie global scenarios to regional sea level changes. The six scenarios project increases in global mean sea level (GMSL) between 2000 and 2100 of 0.3 m, 0.5 m, 1.0 m, 1.5 m, 2.0 m, and 2.5 m. For this analysis, we use these 1-degree gridded projections from Sweet et al. (2017a) by aggregating to the county-level. Of the three uncertainty scenarios—low, medium, high—we use the medium. Results for all six GMSL rise scenarios are developed, but we use probabilities associated with each (see Sweet et al. 2017a), which vary by RCP, to aggregate GMSL results.
Storm surge probabilities
Rising sea levels will increase the severity of flooding by raising the baseline water level over which storms and other high water level events create a surge. For this analysis, we use historical tide gauge measurements (NOAA 2018), which allow direct estimation of SS for all 302 coastal counties in our domain. We extracted the maximum daily water level from each record, and de-trended the resulting set of maximum gauge heights from each time series—we then calculated a distribution of SS heights by fitting a generalized extreme value distribution to the annual maximum time series from each gauge, providing an estimate of the surge heights associated with return intervals from 2 to 500 years. Tide gauges with less than 10 years of data were excluded. Stations were matched to counties using proximity and topography.
Protection decision rules
Decision rules for adaptation to episodic flooding caused by SS, which rises as sea levels rise, were reformulated from Neumann et al. (2015b) to enable a traditional cost-benefit test—these are described in further detail in Lorie et al. (2020) and in the Supplementary Material for the NCPM. New rules compare the cost of different adaptation options within each cell to the expected reduction in costs that would result from those adaption options. This decision rule is based on an estimate of the cost of expected annual damages (EAD) and expected annual benefits of adaptation. EAB is the avoided damage cost given the assumption that adaptation will prevent damage for events up to and including the current 100-year flood. In its simplest form, the decision rule implements the lowest cost adaptation option.
Model initiation
Comprehensive data on existing SS protection (sea walls, elevated structures) across CONUS, to our knowledge, is not available. To identify where sea walls and elevated structures may already exist, we run the NCPM over an initiation period prior to the simulation. This technique is also called model “spin-up” and is often used in dynamic models to provide a consistent starting point for each simulation—spin-up was used in Lorie et al. 2020). Model initiation allows for the most cost-effective protection during model initiation, with all options available independent of the adaptation scenario. Spin-up costs are excluded in the reported costs.
Coastal adaptation scenarios
For the No Adaptation scenario, no protective measures are implemented to avoid the impacts of SLR and SS (see Table 1 for a summary). As a result, properties incur damage (inundation from SLR and flooding from SS). We assume that property owners abandon properties that are inundated by SLR and that they incur damage from SS flooding. If the costs of damage associated with SS exceed the value of the property, we assume that the property owner abandons the property. For this scenario, costs include the property values of abandoned properties and the structure damage from SS flooding (calculated as a percentage of the property value); but in this (and all scenarios), they exclude any direct measure of damage to public infrastructure, indirect costs associated with flooding (i.e., business interruptions or secondary impacts of loss of critical infrastructure), losses of marsh and other coastal ecosystems, and recreation value lost from loss of beaches, except as implicitly capitalized in diminished property value.
Similar to No Adaptation scenario, under Reactive Adaptation scenario, property owners do not implement protective measures to avoid SLR impacts. When a property is inundated by SLR, we assume the property owner abandons the property. When SS damages occur, however, we assume that the property owner evaluates whether or not to elevate the property to avoid future damage. This is done by multiplying the damage in the current year by 10 to estimate the decadal cost of damage. If the projected decadal cost of damage is greater than the property value, the property is abandoned. If the projected cost of damage is less than the property value, and also less than the costs of elevating, then the property incurs the cost of damage. If the projected cost of damage is less than the property value, but greater than the costs of elevation, then the property elevates. We assume that shoreline armoring is not implemented in this scenario, as this activity typically requires planning in advance by states or the Federal government, and thus is better characterized as Proactive Adaptation scenario. Costs in Reactive Adaptation scenario therefore include the property values of abandoned properties, structure damage from SS flooding, and costs of elevation where it is warranted.
In the Proactive Adaptation scenario, protective measures are implemented to avoid damage from both SLR and SS. These measures include beach nourishment, armoring, elevation, and abandonment. In this scenario, costs include the property values of abandoned properties and the costs of all forms of protection where it is warranted (see Table 1). The NCPM selects the least-cost option on an annual basis in response to sea level rise and on a decadal basis in response to storm surge. Additional information on the decision tree logic and other sources of information used in the NCPM is included in the Supplemental Material.