 the county average land-surface temperature on days of extreme heat, we use the 95th percentile of daily average land-surface temperatures from 2000-2019 (Figure 3-3). For future projected heat, we use the heat index of each county (UCS 2019) generated from an ensemble of general circulation models. The heat index includes a combination of temperature and humidity to create a “feels-like” heat rating.
We base our calculation of sensitivity to heat on socioeconomic variables from the US Census Bureau, 2013-2017 American Community Survey 5-Year Estimates (US Census Bureau undated). Socioeconomic factors include age, the average income for people in the county, the percent of county population under the poverty line, the percent of poverty households with children, the percent of population employed in construction and production (implying predominantly outdoor workers with greatest exposure to heat), the percent of population unemployed, and type of housing (see further details in Appendix A).
Human exposure to heat has been quantified and mapped, although only a few studies have combined socioeconomic sensitivity with heat exposure data to evaluate human vulnerability to the health risks associated with extreme heat (Buscail et al. 2012; Ho et al. 2015). Most studies that examine health vulnerability to heat use aggregate data that matches the spatial units from which sensitivity information is derived by the US Census Bureau (Ho et al. 2015). For example, most socioeconomic factors are measured at the county scale so exposure measurements (i.e., land-surface temperature and change in future heat) must be averaged across a county. Despite this complication, heat vulnerability is a useful measure to relate climate to human health impacts (see further details in Appendix A). Municipalities can use heat vulnerability information to guide heat mitigation interventions, such as establishment of green or reflective roofs, urban parks, and water features (see Section 5).
By combining exposure and sensitivity data, we identified counties with populations most vulnerable to extreme temperatures. Vulnerability was split into four main categories represented in Figure 3-4. The darker the color the higher the vulnerability to heat.
Local temperature trends
To understand the local effects of extreme temperatures, we examined daily temperature data from 38 NOAA weather stations across Montana. We determined the maximum and minimum temperature trends in summer months (June, July and August) at each station. The temperature records at individual stations may not coincide or reveal the same patterns as the Montana climate region results indicated in the MCA, which were averaged (Whitlock et al. 2017). Our intent was to examine recent temperatures and their trends at a scale where human health may be monitored and impacted in the future. Our findings are:
- Summer maximum temperature.—Fifteen out of the 38 stations across Montana indicate an increasing trend in summer (June, July, August) maximum temperatures over the last 30 yr. The easternmost stations show no trend in maximum temperatures. No stations have a negative trend in maximum summer temperatures (i.e., cooling) over the last 30 yr (1990-2019).
- Summer minimum temperature.—From 1990-2019 minimum summer night temperature has an increasing trend at 15 of the 38 stations across Montana, and a decreasing trend at four stations. All of the decreasing trends in summer night temperature (i.e., cooling) are in western Montana. Five of the 15 stations with increasing summer maximum temperature also show increasing trends in summer night temperature; three are in urban centers. In other words, stations that show a trend towards increasing temperature during the day also experience a trend towards warming night conditions. Warm nights represent a burden on the human thermoregulatory system, particularly for the elderly and those who rely on natural ventilation (e.g., open windows) to reduce body temperature (Mills et al. 2015).
The number of continuous days and nights at high temperature 