Increasing temperatures, heavier rain events, and the increasing frequency and severity of flood events driven by sea-level rise present numerous impacts to public health that will continue to intensify, while new health threats are emerging.

Health impacts from these various climate-related exposures may include:

• Increased respiratory disease;
• Increased cardiovascular disease;
• Injuries;
• Premature deaths related to extreme weather events (e.g, heat-related deaths; drowning-related fatalities);
• Food- and waterborne illnesses; and
• Mental health impacts (e.g., stress, anxiety and fear).

This public health municipal snapshot is a focused area of development for the Rutgers team. An initial set of data regarding temperature and flood exposure is presented while the Rutgers team is currently actively seeking additional sets of data that can support efforts to advance climate change and public health efforts at the state, regional and local levels.

There are 3 types of flood events:

1. Riverine (or ‘fluvial’) flood events occur when intense rain events cause rivers and streams to overtop their banks.
2. Flash (or ‘pluvial’) floods occur when intense rainfall causes a flood event that is not directly associated with a body of water. For example, flash flood events include floods in roadways from impaired stormwater management systems.
3. Coastal flood events occur when sea-level rise, high tides, and storm surge combine to create flood events that range from nuisance high-tide floods to destructive storm tides from seawater.

The Federal Emergency Management Agency (FEMA) models flood hazards, both riverine (1) and coastal (3), as part of the National Flood Insurance Program (NFIP) regulations and insurance requirements. FEMA does not model flash flood events (2) for their NFIP flood mapping.

• ¹ Representative Concentration Pathway (RCP) is a greenhouse gas concentration trajectory adopted by the IPCC, which describes different climate futures, all of which are considered possible depending on how much greenhouse gases are emitted in the years to come. Emissions in RCP 4.5 peak around 2040, then decline. In RCP 8.5, emissions continue to rise throughout the 21st century.
• ² The total number of days per year with maximum temperature above 95°F is an indicator of how often very hot conditions occur. Depending upon humidity, wind, and access to air-conditioning, humans may feel very uncomfortable or experience heat stress or other heat-related illness on very hot days. Hot days also stress plants and animals as well as infrastructure. Increased demand for cooling can stress energy infrastructure. A baseline is compiled from a long-term average of observations for a particular variable. In this case the baseline for the number of days per year in NJ with maximum temperature above 95°F was created over a 30-year period from 1981 to 2010.
• ³ The number of cooling degree days per year reflects the amount of energy people use to cool buildings during the warm season. For this calcluation a standard base temperature of 65°F is utilized. On a day when the average outdoor temperature is 85°F, reducing the indoor temperature by 20 degrees over 1 day requires 20 degrees of cooling multiplied by 1 day, or 20 cooling degree days. Utility companies use cooling degree days to estimate the annual amount of energy people will use to cool buildings. A baseline is compiled from a long-term average of observations for a particular variable. In this case the baseline for the change in the number of cooling degree days during the warm season in NJ was created over a 30-year period from 1981 to 2010.
• ⁴ The change in maximum July temperature reflects how this temperature will change under moderate and high emission scenarios by 2030, 2060, and 2090. A change in the maximum July temperature, historically the hottest month of the year in NJ, could cause temperatures to exceed thresholds of comfort, increasing the chances of heat related illnesses, energy demand, and deleterious impacts on plants and animals. A baseline is compiled from a long-term average of observations for a particular variable. In this case the baseline for change in maximum July temperature was created over a 30-year period from 1981 to 2010.