Roads and bridges are vulnerable to a range of stressors, such as flooding, heat waves, and other extreme events. The probability of these stressors impacting roads and bridges cannot be exactly calculated due to various uncertainties related to the scientific understanding of future environmental conditions. Resilient design methods find ways to account for the uncertainty in various stressors. This data set provides temperature and precipitation variables that can be used to help transportation professionals better characterize risks to transportation assets and provide more resilient designs.
We applied daily climate projections to calculate 19 variables related to resilient roadway design. The source data set is the statistically downscaled CMIP6-LOCA2 (Localized Constructed Analogs, v20240915 version, Pierce et al. 2023), which includes temperature and precipitation projections from the Climate Model Intercomparison Program Phase 6 (CMIP6) for 27 models under the ssp245, ssp370, and ssp585 scenarios. The “unsplit Livneh” (Pierce et al., 2021) is used as the training data set for LOCA2. We adopt the v20240915 version of CMIP6-LOCA2 as it includes recent changes to the downscaling methodology to improve the representation of precipitation extreme events. The Python xclim (v0.56) library was used to process daily temperature and precipitation from CMIP6-LOCA2. These data are provided as climatology and percentile maps for the 1981-2010, 2025-2049, 2050-2074, and 2075-2099 periods. County-level time series from 1950-2100 are provided, as well as climatology and percentile summaries for 1981-2010, 2025-2049, 2050-2074, and 2075-2099 periods. Users interested in 6 km grids are referred to the home pages of each of the respective sources. The county-level data sets are spatially averaged using the 2023 United States Census Bureau TIGER/Line Shapefiles (https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html). The NetCDF time series files herein can be linked to the shapefile geometry using the “GEOID” field.
The variables included are:
Minimum daily minimum temperature (TN_min, units=degF, freq=monthly/annual)
Minimum 7-day minimum temperature (TN7day_min, units=degF, freq=monthly/annual)
Maximum daily maximum temperature (TX_max, units=degF, freq=monthly/annual)
Maximum 7-day maximum temperature (TX7day_max, units=degF, freq=monthly/annual)
Maximum number of consecutive days with maximum daily temperature above 95 degF (maximum_consecutive_warm_days_95F, units=d, freq=annual)
Maximum number of consecutive days with maximum daily temperature above 100 degF (maximum_consecutive_warm_days_100F, units=d, freq=annual)
Maximum number of consecutive days with maximum daily temperature above 105 degF (maximum_consecutive_warm_days_105F, units=d, freq=annual)
Maximum number of consecutive days with maximum daily temperature above 110 degF (maximum_consecutive_warm_days_110F, units=d, freq=annual)
Maximum Near-Surface Air Temperature (95th percentile) (TX95p_per, units=degF, freq=time window)
Maximum Near-Surface Air Temperature (99th percentile) (TX99p_per, units=degF, freq=time window)
Minimum Near-Surface Air Temperature (1st percentile) (TN01p_per, units=degF, freq=time window)
Minimum Near-Surface Air Temperature (5th percentile) (TN05p_per, units=degF, freq=time window)
Number of days with daily precipitation at or above 0.01 in/day (wetdays, units=d, freq=monthly/annual)
Number of days with daily precipitation at or above 0.5 in/day (intense_wetdays, units=d, freq=monthly/annual)
Maximum 1-day total precipitation (rx1day, units=in/d, freq=annual)
Maximum 1-day total precipitation (50th percentile) (rx1day_50p_per, units=in/d, freq=time window, notes=See Processing Step 4 for details)
Maximum 1-day total precipitation (90th percentile) (rx1day_90p_per, units=in/d, freq=time window, notes=See Processing Step 4 for details)
Maximum 1-day total precipitation (estimated 90th percentile) (rx1day_90p_per_est, units=in/d, freq=time window, notes=See Processing Step 4 for details)
Maximum 1-day total precipitation (96th percentile) (rx1day_96p_per, units=in/d, freq=time window, notes=See Processing Step 4 for details)
The 27 included CMIP6 GCMs are:
ACCESS-CM2, ACCESS-ESM1-5, AWI-CM-1-1-MR, BCC-CSM2-MR, CESM2-LENS, CNRM-CM6-1, CNRM-CM6-1-HR, CNRM-ESM2-1, CanESM5, EC-Earth3, EC-Earth3-Veg, FGOALS-g3, GFDL-CM4, GFDL-ESM4, HadGEM3-GC31-LL, HadGEM3-GC31-MM, INM-CM4-8, INM-CM5-0, IPSL-CM6A-LR, KACE-1-0-G, MIROC6, MPI-ESM1-2-HR, MPI-ESM1-2-LR, MRI-ESM2-0, NorESM2-LM, NorESM2-MM, TaiESM1
