Environmental Public Health Tracking: Climate Change

Heat-related illness occurs when the body’s temperature and control system becomes overloaded. Normally, the body cools itself by sweating, but this cooling mechanism can become ineffective if the body’s temperature rises too fast.

There are several forms of heat-related illness, including heat stroke, heat exhaustion, rhabdomyolysis, heat syncope, heat cramps, and heat rash.

Old age, youth ages 0-4, obesity, fever, dehydration, heart disease, mental illness, poor circulation, sunburn, prescription medication use, and alcohol use are factors that impact the body’s ability to regulate temperature.

Workers whose jobs require them to work outside in hot weather are also at risk of heat-related illness.

As a result of climate change, events like heat waves happen more often. The frequency of heat waves may impact how often people suffer from heat-related illness.

• Tracking extreme heat events and heat-related injury gives public health professionals a better understanding of the health consequences of extreme heat across the country. We can monitor the impact of our warnings and preparedness efforts.
• Projecting extreme heat events can help certain areas prepare for these events in advance.

All of the heat and heat-related illness measures are from the Centers for Disease Control and Prevention's (CDC) National Environmental Public Health Tracking portal. See below for details about the original data sources.

• The original source of the emergency department (ED) and hospitalization data as displayed on the Wisconsin Tracking Portal is the Wisconsin Hospital Association Information Center, Inc.
• Heat-related mortality data are from the CDC’s National Center for Health Statistics.
• The North American Land Data Assimilation System from the National Aeronautics and Space Administration is the original source of the historical temperature data.
• Modeled temperature data obtained from 1/8 degree-CONUS Daily Downscaled Climate Projections by Katharine Hayhoe is the source for projected heat data.
• The vulnerability and preparedness data are from the Wisconsin Hospital Association Information Center, Inc. the U.S. Census Bureau, CDC’s Behavioral Risk Factor Surveillance System, American Hospital Association Annual Survey, and National Land Cover Database.

• Historical Temperature and Heat Index
  • Number of extreme heat days
• Temperature and Heat Projections
  • Projected number of future extreme heat days
  • Projected number of future extreme heat nights
• Heat-related illness
  • Heat-related emergency department visits
    • Age-adjusted rate of emergency department visits per 100,000 population
    • Crude rate of emergency department visits for heat stress per 100,000 population
    • Number of emergency department visits for heat stress
  • Heat-related hospitalizations
    • Age-adjusted rate of hospitalizations for heat stress per 100,000 population
    • Crude rate of hospitalizations for heat stress per 100,000 population
    • Number of hospitalizations for heat stress
  • Heat-related mortality
    • Number of summertime (May-September) heat-related deaths, by year
• Vulnerability and Preparedness: Heat
  • Age-adjusted rate of hospitalization for heart attack per 10,000 population
  • Median household income
  • Number and percent of people living in poverty
  • Number and percent of people without health insurance

• Heat index data takes both humidity and temperature into account.
• Hospital admission and emergency department visit data do not include people who experience symptoms but are not seen in the emergency room or admitted to the hospital.
• These data do not include inpatient admissions or emergency department visits at hospitals owned by the federal government, such as Veterans Administration hospitals.
• The death certificate dataset may be missing a small number of cases where the decedent is a Wisconsin resident but died in another state.
• Data users should keep in mind that many factors contribute to illness. These factors should be considered when interpreting the data. Factors include the following:
  • Demographics (race, gender, age)
  • Socioeconomic status (income level, education)
  • Geography (rural, urban)
  • Changes in the medical field (diagnosis patterns, reporting requirements)
  • Individual behavior (diet, smoking)

• CDC – Extreme Heat and Your Health
• CDC – Heat Stress – Heat Related Illness
• CDC – Frequently Asked Questions (FAQ) About Extreme Heat
• Wisconsin Department of Health Services – Heat-Related Health and Safety Tips
• Wisconsin Department of Health Services – Climate and Health Program
• Wisconsin Initiative on Climate Change Impacts

Heat-related mortality

Number of heat-related deaths (counts)

These data include heat-related deaths collected from the Multiple Cause Mortality files from CDC’s National Center for Health Statistics. These data include deaths in which excessive heat exposure (ICD-10 code X30) or effects of heat and light (ICD-10 code T67) is reported as either the underlying or contributing cause of death. Deaths due to exposure to excessive heat of man-made origins (W92) are excluded. Only deaths that occurred in the summer months (May through September) are included in this measure. Data are suppressed when the number of deaths is less than 10 (including 0).

Heat-related ED visits

Age-adjusted rate of emergency department visits for heat stress per 100,000 population

These data include ED visits for heat stress and are collected from emergency room visit discharge records. ED visits resulting in subsequent hospitalization are also included. This measure includes ED visits with an ICD-9 code of 992.0-992.9 or an ICD-10 code of T67.0-T67.9, X30, or X32 in the principal or other diagnosis fields. ED visits due to exposure to excessive heat of man-made origin (ICD-9 code E900.1 or ICD-10 code W92) are excluded. Data for Wisconsin residents visiting an ED in Iowa and Minnesota are included. Counties with non-zero counts less than 6 and population less than 100,000 are suppressed for this measure.

An age-adjusted rate is statistically modified to eliminate the potential biasing effect of different age distributions across different populations. In other words, the age-adjusted rate accounts for the possibility that ED visits for heat stress may be more frequent among younger individuals and some counties have a higher population of younger individuals than others.

Direct age-adjustment is conducted using the 2000 U.S. standard population. Federally funded hospitals (for example, Veteran’s Administration (VA) hospitals, which are exempt from state reporting requirements) and are not included in these data. Data are based on the number of admissions, not the number of individuals admitted to the hospital.

Prior to 2018, the county variable in the emergency department data was assigned based on the zip code of the residence of the patient. In cases where a zip code spanned more than one county, the ED visit was placed into the county with the higher population. Beginning in 2018, geocoded data became available in Wisconsin ED visit data, which allows for a more accurate determination of county of residence. The new block group variable is the geocoded latitude/longitude of the patient’s residential address. The first five digits of the block group variable are used to determine the Wisconsin county. Due to this change, caution should be used when comparing county-level data prior to 2018 with data from 2018 onward.

Crude rate of ED visits for heat stress per 100,000 population

These data include emergency department visits for heat stress and are collected from emergency room visit discharge records. This measure includes emergency department visits with an ICD-9 code of 992.0-992.9, E900.9 or an ICD-10 code of T67.0-T67.9, X30, or X32 in the principal or other diagnosis fields. ED visits due to exposure to excessive heat of man-made origin (ICD-9 code E900.1 or ICD-10 code W92) are excluded. Data for Wisconsin residents hospitalized in Iowa and Minnesota are included. Counties with non-zero counts less than 6 and population less than 100,000 are suppressed for this measure. Federally funded hospitals (for example Veteran’s Administration [VA] hospitals, which are exempt from state reporting requirements) are not included in these data. Data are based on the number of admissions, not the number of individuals admitted to the hospital. The crude rate is the number of ED visits divided by the total number of people in that age category (for example, people aged 65+). This is expressed as a number per unit population such as “per 100,000 people.” The crude rate does not take into account the differences in age distributions across counties and are therefore subject to bias. Use age-adjusted rates for a better standardized measure.

Prior to 2018, the county variable in the emergency department data was assigned based on the zip code of the residence of the patient. In cases where a zip code spanned more than one county, the ED visit was placed into the county with the higher population. Beginning in 2018, geocoded data became available in Wisconsin ED visit data, which allows for a more accurate determination of county of residence. The new block group variable is the geocoded latitude/longitude of the patient’s residential address. The first five digits of the block group variable are used to determine the Wisconsin county. Due to this change, caution should be used when comparing county-level data prior to 2018 with data from 2018 onward.

Number of ED visits for heat stress

These data include emergency department visits for heat stress and are collected from emergency room visit discharge records. This measure includes emergency department visits with an ICD-9 code of 992.0-992.9, E900.0 or E900.9 or an ICD-10 code of T67.0-T67.9, X30, or X32 in the principal or other diagnosis fields. ED visits due to exposure to excessive heat of man-made origin (ICD-9 code for E900.1 or ICD-10 code W92) are excluded. Data for Wisconsin residents hospitalized in Iowa and Minnesota are included. Counties with non-zero counts less than six and population less than 100,000 are suppressed for this measure. Federally funded hospitals (for example VA hospitals, which are exempt from state reporting requirements) are not included in these data. Data are based on the number of admissions, not the number of individuals admitted to the hospital. Please note that counts are a statistically limited way to consider ED visits because they do not allow for accurate comparison between counties. Counties with higher populations, such as Milwaukee, will have more emergency visits simply because they have more people. An age-adjusted rate is a better measure for true comparison between counties.

Prior to 2018, the county variable in the emergency department data was assigned based on the zip code of the residence of the patient. In cases where a zip code spanned more than one county, the ED visit was placed into the county with the higher population. Beginning in 2018, geocoded data became available in Wisconsin ED visit data, which allows for a more accurate determination of county of residence. The new block group variable is the geocoded latitude/longitude of the patient’s residential address. The first five digits of the block group variable are used to determine the Wisconsin county. Due to this change, caution should be used when comparing county-level data prior to 2018 with data from 2018 onward.

Heat-related hospitalizations

Age-adjusted rate of hospitalizations for heat stress per 100,000 population

These data include hospitalizations for heat stress and are collected from inpatient hospital discharge records. This measure includes hospitalizations with an ICD-9 code of 992.0-992.9, E900.0 or E900.9 or an ICD-10 code of T67.0-T67.9, X30, or X32 in the principal or other diagnosis fields. Hospitalizations due to exposure to excessive heat of man-made origin (ICD-9 code for E900.1 or ICD-10 code W92) are excluded. Data for Wisconsin residents hospitalized in Iowa and Minnesota are included. Counties with non-zero counts less than six and population less than 100,000 are suppressed for this measure. Federally, funded hospitals (for example VA hospitals, which are exempt from state reporting requirements) are not included in these data. Data are based on the number of admissions, not the number of individuals admitted to the hospital. An age-adjusted is statistically modified to eliminate the potential biasing effect of different age distributions across different populations. In other words, the age-adjusted rates accounts for the possibility that hospitalizations for heat stress may be more frequent among younger individuals and some counties have a higher population of younger individuals than others. Direct age-adjustment is conducted using the 2000 US standard population.

Prior to 2018, the county variable in the hospitalization data was assigned based on the zip code of the residence of the patient. In cases where a zip code spanned more than one county, the hospitalization was placed into the county with the higher population. Beginning in 2018, geocoded data became available in Wisconsin hospitalization data, which allows for a more accurate determination of county of residence. The new block group variable is the geocoded latitude/longitude of the patient’s residential address. The first five digits of the block group variable are used to determine the Wisconsin county. Due to this change, caution should be used when comparing county-level data prior to 2018 with data from 2018 onward.

Crude rate of hospitalizations for heat stress per 100,000 population

These data include hospitalizations for heat stress and are collected from inpatient hospital discharge records. This measure includes hospitalizations with an ICD-9 code of 992.0-992.9, E900.0 or E900.9 or an ICD-10 code of T67.0-T67.9, X30, or X32 in the principal or other diagnosis fields. Hospitalizations due to exposure to excessive heat of man-made origin (ICD-9 code for E900.1 or ICD-10 code W92) are excluded. Data for Wisconsin residents hospitalized in Iowa and Minnesota are included. Counties with non-zero counts less than six and population less than 100,000 are suppressed for this measure. Federally funded hospitals (for example VA hospitals, which are exempt from state reporting requirements) are not included in these data. Data are based on the number of admissions, not the number of individuals admitted to the hospital. The crude rate is the number of hospitalizations divided by the total number of people in the area of interest (for example, a county). This is expressed as a number per unit population, such as “per 100,000 people.” The crude rate does not take into account the differences in age distributions across counties and are therefore subject to bias. Use age-adjusted rates for a better standardized measure.

Prior to 2018, the county variable in the hospitalization data was assigned based on the zip code of the residence of the patient. In cases where a zip code spanned more than one county, the hospitalization was placed into the county with the higher population. Beginning in 2018, geocoded data became available in Wisconsin hospitalization data, which allows for a more accurate determination of county of residence. The new block group variable is the geocoded latitude/longitude of the patient’s residential address. The first five digits of the block group variable are used to determine the Wisconsin county. Due to this change, caution should be used when comparing county-level data prior to 2018 with data from 2018 onward.

Number of hospitalizations for heat stress

These data include hospitalizations for heat stress and are collected from inpatient hospital discharge records. This measure includes hospitalizations with an ICD-9 code of 992.0-992.9, E900.0 or E900.9 or an ICD-10 code of T67.0-T67.9, X30, or X32 in the principal or other diagnosis fields. Hospitalizations due to exposure to excessive heat of man-made origin (ICD-9 code for E900.1 or ICD-10 code W92) are excluded. Counties with non-zero counts less than six and population less than 100,000 are suppressed for this measure. Federally funded hospitals (for example VA hospitals, which are exempt from state reporting requirements) are not included in these data. Data are based on the number of admissions, not the number of individuals admitted to the hospital. Please note that counts are a statistically limited way to consider hospitalizations because they do not allow for accurate comparison between counties. Counties with higher populations, such as Milwaukee, will have more hospitalizations simply because they have more people. An age-adjusted rate is a better measure for true comparison between counties.

Prior to 2018, the county variable in the hospitalization data was assigned based on the zip code of the residence of the patient. In cases where a zip code spanned more than one county, the hospitalization was placed into the county with the higher population. Beginning in 2018, geocoded data became available in Wisconsin hospitalization data, which allows for a more accurate determination of county of residence. The new block group variable is the geocoded latitude/longitude of the patient’s residential address. The first five digits of the block group variable are used to determine the Wisconsin county. Due to this change, caution should be used when comparing county-level data prior to 2018 with data from 2018 onward.

Historical temperature and heat index

Number of extreme heat days

The purpose of this dataset is to keep record of past extreme days.

The CONUS Downscaled Climate Projections calculated temperature and precipitation projections using outputs from 16 global change models at 1/8th degree resolution to generate a single comprehensive dataset. Grid-level data were converted into county-level estimates by processing modeled data. The measure involves calculation of a daily maximum heat index, which takes into account relative humidity and temperature. The measure uses a threshold of 90 degrees Fahrenheit to constitute an extreme heat day. Data were collected only for summer months (May through September). Measures for daily maximum temperature and heat index are available in relative thresholds (90th, 95th, 98th, 99th percentiles) and absolute thresholds (90 ̊F, 95 ̊F, 100 ̊F, 105 ̊F).

Temperature and heat projections

Projected difference in extreme heat days as compared to the historical period

These estimates represent the difference in extreme heat days between the time period selected (2016-2045, 2036-2065, 2070-2099) and the referent period (1976-2005). These measures are derived using Localized Constructed Analogs (LOCA): a statistical downscaling technique that uses historical data derived from CMIP5 simulations to predict future climate scenarios. The county-level temperature estimates, which are available at a 1/16th-degree resolution, are obtained by using LOCA to downscale data from 32 global climate models from 1950-2100 with a historical period from 1950-2005. The resulting models are used to estimate two future scenarios (RCP 4.5 and RCP 8.5) over the period 2006-2100. The process of converting grid-level data to county-level estimates uses a population-weighted centroid approach which may lead to the misclassification of temperature for some areas.

The modeling tools are used to make a suite of future climate changes that illustrate the possibilities that may lie ahead. The scenarios include time series of emissions and concentrations of the full suite of greenhouse gases (GHGs), aerosols, and chemically-active gases, as well as land use. The representative concentration pathway (RCP) is used to represent two of many possible scenarios – in this case, the data on the portal is available for RCP 8.5 to represent high emissions and RCP 4.5 to represent low emissions.

Projected difference was determined by either absolute (i.e., 90 ̊F, 95 ̊F, 100 ̊F, 105 ̊F) or relative (i.e., 99th) thresholds. These thresholds are displayed for both RCP scenarios.

Projected difference in extreme heat nights as compared to the historical period

These estimates represent the difference in extreme heat nights between the time period selected (2016-2045, 2036-2065, 2070-2099) and the referent period (1976-2005). These measures are derived using Localized Constructed Analogs (LOCA): a statistical downscaling technique that uses historical data derived from CMIP5 simulations to predict future climate scenarios. The county-level temperature estimates, which are available at a 1/16th-degree resolution, are obtained by using LOCA to downscale data from 32 global climate models from 1950-2100 with a historical period from 1950-2005. The resulting models are used to estimate two future scenarios (RCP 4.5 and RCP 8.5) over the period 2006-2100. The process of converting grid-level data to county-level estimates uses a population-weighted centroid approach which may lead to the misclassification of temperature for some areas.

The modeling tools are used to make a suite of future climate changes that illustrates the possibilities that may lie ahead. The scenarios include time series of emissions and concentrations of the full suite of greenhouse gases (GHGs), aerosols, and chemically-active gases, as well as land use. The representative concentration pathway (RCP) is used to represent two of many possible scenarios – in this case, the data on the portal is available for RCP 8.5 to represent high emissions and RCP 4.5 to represent low emissions.

Projected difference was determined by either absolute (i.e., 75 ̊F, 80 ̊F, 85 ̊F, 90 ̊F) or relative (i.e., 99th) thresholds. These thresholds are displayed for both RCP scenarios.

Vulnerability and preparedness: heat

Age-adjusted estimates of the percent of adults >= 20 years diagnosed with diabetes

These data are provided by the CDC’s National Diabetes Surveillance System. Prevalence rates by county for adults 20 years and older were estimated using data from CDC’s Behavioral Risk Factor Surveillance Systems and age-adjusted using data from U.S. Census Bureau’s population estimates.

Age-adjusted rate of hospitalizations for heart attack among persons 35 and over per 10,000 population

These data include hospitalizations for heart attack and are collected from inpatient hospital discharge records. This measure includes hospitalizations with an ICD-9 code of 410.0-410.92, prior to October 2015 or an IDC-10 code of 121.0-122.9 from October 2015 and on. Starting in 2015, transfers between hospitals were excluded. Data for Wisconsin residents hospitalized in Iowa and Minnesota are included. Counties with non-zero counts less than six and population less than 100,000 are suppressed for this measure. Federally funded hospitals (for example Veteran’s Administration [VA] hospitals, which are exempt from state reporting requirements) are not included in these data. Data are based on the number of admissions, not the number of individuals admitted to the hospital. An age-adjusted rate is a rate that is statistically modified to eliminate the potential biasing effect of different age distributions across different populations. In other words, the age-adjusted rate accounts for the possibility that heart attacks are more frequent among older individuals and some counties have a higher count of older individuals than other. Direct age-adjustment is conducted using the 2000 U.S. standard population.

Median household income

Data are provided by the U.S. Census Bureau, Small Area Income and Poverty Estimates.

Number of hospital beds

Data are obtained from the 2016 American Hospital Association Annual Survey. Facility-level insights are not available for hospitals that did not complete the survey.

Number of hospital beds per 10,000 population

Data are obtained from the 2016 American Hospital Association Annual Survey. Facility-level insights are not available for hospitals that did not complete the survey. The rate of hospital beds per 10,000 was calculated using the total population per county as provided by the U.S. Census Bureau.

Number of hospitals

Data are obtained from the 2016 American Hospital Association Annual Survey. Facility-level insights are not available for hospitals that did not complete the survey.

Number of hospitals per 100,000 population

Data are obtained from the 2016 American Hospital Association Annual Survey. Facility-level insights are not available for hospitals that did not complete the survey. The rate of hospital beds per 10,000 people was calculated using the total population per county as provided by the U.S. Census Bureau.

Number of people living in poverty

Data are provided by the U.S. Census Bureau, Small Area Income and Poverty Estimates. Data are also available for populations aged 0-17 in addition to the rest of the population.

Number of people without health insurance

Data are provided by the U.S. Census Bureau, Small Area Health Insurance Estimates. Data are also available for populations aged 0-18.

Percent of land covered by forest

Data are collected from National Land Cover Database provided by the U.S. Department of the Interior, U.S. Geological Survey. Percentages for each county were estimated as a proportion of gridded data available from the Multi-Resolution Land Characteristics Consortium.

Forest classification is defined in three categories; deciduous forest, evergreen forest, and mixed forest. A deciduous forest is defined as having 75% of trees shedding foliage in the fall. An evergreen forest has 75% of trees maintain their foliage for the full year. In a mixed forest, neither deciduous or evergreen trees make up 75% of tree species.

Percent of land used for development

Data are collected from National Land Cover Database provided by the U.S. Department of the Interior, Multi-Resolution Land Characteristics Consortium. Percentages for each county were estimated as a proportion of gridded data available from the Multi-Resolution Land Characteristics Consortium. Developed Land Use is the combined data from Developed-Open Space, Developed Low-Intensity, Developed-Medium Intensity, and Developed-High Intensity in gridded data from National Land Cover Database.

Developed open spaces are defined as areas with some constructed materials but mostly vegetation (e.g., golf courses).

Percent of population aged 65 years and over living alone in a non-family household

Data are provided by the U.S. Census Bureau, American Factfinder, American Community Survey five-year estimates. Data are displayed by aggregating census tracts to a minimum of 5,000 people or 200,000 people.

Percent of population living in poverty

Data are provided by the U.S. Census Bureau, Small Area Income and Poverty Estimates. Data are available for those populations aged 0-17.

Percent of population of a race other than white

Data are collected from Census 2000 Summary File 3 created by the U.S. Census Bureau.

Percent of population without health insurance

Data are provided by the U.S. Census Bureau, Small Area Health Insurance Estimates. This data does not include those over 65 years covered by Medicare. Data are available for populations aged 0-18 in addition to the rest of the population.

Explore definitions and explanations of terminology found on the portal, like age-adjusted rate and confidence intervals.

The precipitation and flooding topic refers to the measure of precipitation falling as rain or snow in a given year. The precipitation and flooding data on the Wisconsin Tracking portal include measures from previous years and projections for future years.

Serious weather events, such as floods, can cause a variety of public health impacts, including waterborne disease and drowning.

These data allow us to understand how changing weather patterns could impact populations in terms of precipitation, flooding, or drought. Understanding precipitation and flooding patterns can also help at-risk areas, emergency response services, and healthcare systems prepare for flooding events.

• The North American Land Data Assimilation System from the National Aeronautics and Space Administration is the source of the historical precipitation data.
• Modeled precipitation data were obtained from 1/8th degree CONUS Daily Downscaled Climate Projections by Katherine Hayhoe.
• Hospital information was obtained from the 2016 American Hospital Association (AHA) survey.
• Calculations of special flood hazard areas were determined from 2011 National Flood Layer data from the Federal Emergency Management Agency (FEMA).
• 2010 U.S. census block group data were used for population data.
• The two emissions scenarios used for precipitation projections were developed by the Intergovernmental Panel for Climate Change (IPCC).

• Historical Precipitation
  • Number of extreme precipitation days
• Precipitation and Flooding Projections
  • Projected annual precipitation intensity
  • Projected number of future extreme precipitation days
  • Projected ratio of precipitation falling as rain to that falling as snow
• Vulnerability and Preparedness
  • Number of housing units within FEMA-designated flood hazard area
  • Number of people within FEMA-designated flood hazard area
  • Number and percent of square miles within FEMA-designated flood hazard area
  • Percent of hospital beds within flood hazard area
  • Percent of hospitals within flood hazard area

Statistical downscaling drastically improves the spatial distribution of climate parameters. Also, modeled meteorological data may not accurately reflect the true precipitation values in each county.

• Centers for Disease Control and Prevention - Precipitation & Flooding
• Precipitation Extremes: Heavy Rainfall, Flooding, and Droughts
• Wisconsin Department of Health Services - Flood Hazards and Recovery
• Wisconsin Department of Health Services - Climate and Health Program
• Wisconsin Initiative on Climate Change Impacts
• Risk Assessment Flood Tool (RAFT)

Historical precipitation

Number of extreme precipitation days

This measure represents the number of extreme precipitation days per year. The raw data are collected from the North American Land Data Assimilation Systems (NLDAS). NLDAS takes measures via radar of land surface variables. These variables are then used to calculate precipitation estimates for how much precipitation fell within a 24-hour period. Precipitation data collected from radar are presented as a grid of latitude and longitude. CDC places county lines over the latitudinal and longitudinal grids produced by NLDAS in order to estimate precipitation within county boundaries. The data are available in absolute threshold (>.01”, 1”, 2”, 3”) and relative threshold (90th, 95th, 98th, 99th percentile).

Precipitation and flooding projections

Projected annual precipitation intensity

The CONUS Downscaled Climate Projections calculated temperature and precipitation projections using outputs from 16 global change models at 1/8th degree resolution to generate a single comprehensive dataset. Grid-level data was transformed into county-level data estimates to determine population exposure to extreme precipitation. Population within each grid-level was determined using the 2010 U.S. Census. Geo-imputation was used to convert grid-level meteorological data into county-level data. Grid polygons with population information were converted to centroids which were then related to the counties in the U.S. in order to determine which counties and populations are most likely to be affected by future precipitation levels.

Two story-lines are used to predict future extreme precipitation days – the A2 and B1 scenarios; the Intergovernmental Panel for Climate Change (IPCC) created the scenarios in order to predict the effects of climate change depending on potential global trends. The A2 scenario shows economic development to be regionally orientated with high population growth. The B1 scenario represents a trend towards a global service and information economy with reductions in material industry and therefore a decrease in emissions. In the B1 scenario, eco-friendlier technologies have been introduced, and the population growth is slow.

Precipitation intensity as determined by cumulative precipitation divided by the number of wet days (where precipitation is >.01”). Precipitation intensity is predicted for both the A2 (high emissions) and B1 (low emissions) scenarios.

Projected number of future extreme precipitation days

The CONUS Downscaled Climate Projections calculated temperature and precipitation projections using outputs from 16 global change models at 1/8th degree resolution to generate a single comprehensive dataset. Grid-level data was transformed into county-level data estimates to determine population exposure to extreme precipitation. Population within each grid-level was determined using the 2010 U.S. Census. Geo-imputation was used to convert grid-level meteorological data into county-level data. Grid polygons with population information were converted to centroids which were then related to the counties in the U.S. in order to determine which counties and populations are most likely to be affected by future precipitation levels.

Two story-lines are used to predict future extreme prediction days – the A2 and B1 scenarios; the Intergovernmental Panel for Climate Change (IPCC) created the scenarios in order to predict the effects of climate change depending on potential global trends. The A2 scenario shows economic development to be regionally orientated with high population growth. The B1 scenario represents a trend towards a global service and information economy with reductions in material industry and therefore a decrease in emissions. In the B1 scenario, eco-friendlier technologies have been introduced, and the population growth is slow.

Extreme precipitation days were determined by absolute (>.01”, 1”, 2”, 3”) or relative (90th, 95th, 98th) thresholds. Extreme precipitation days are determined for both the A2 (high emissions) and B1 (low emissions) scenarios.

Projected ratio of precipitation falling as rain to that falling as snow

The CONUS Downscaled Climate Projections calculated temperature and precipitation projections using outputs from 16 global change models at 1/8th degree resolution to generate a single comprehensive dataset. Grid-level data was transformed into county-level data estimates to determine population exposure to extreme precipitation. Population within each grid-level was determined using the 2010 U.S. Census. Geo-imputation was used to convert grid-level meteorological data into county-level data. Grid polygons with population information were converted to centroids, which were then related to the counties in the U.S. in order to determine which counties and populations are most likely to be affected by future precipitation levels.

Two story-lines are used to predict future extreme precipitation days – the A2 and B1 scenarios; the Intergovernmental Panel for Climate Change (IPCC) created the scenarios in order to predict the effects of climate change depending on potential global trends. The A2 scenario shows economic development to be regionally orientated with high population growth. The B1 scenario represents a trend towards a global service and information economy with reductions in material industry and therefore a decrease in emissions. In the B1 scenario, eco-friendlier technologies have been introduced, and the population growth is slow.

The ratio of precipitation was defined as the ratio of the precipitation >.01” with daily maximum temperature above freezing to that at or below freezing. Precipitation ratio is predicted for both the A2 (high emissions) and B1 (low emissions) scenarios.

Vulnerability and preparedness

Number of housing units within FEMA-designated flood hazard area

This is an estimate of the number of housing units within the special flood hazard area by county. Data and calculation of special flood hazard areas are provided by FEMA using 2011 National Flood Hazard Layer data. Data are defined by 1% annual chance of coastal or riverine flooding. This is also known as the 100-year flood zone, in which a flood even has a 1 in 100 chance of being equaled or exceeded in any given year. The population distribution was obtained from 2010 census block group data along with 2010 LandScan U.S. population data.

Number of people within FEMA-designated flood hazard area

The number of people in a flood hazard area is determined by 2010 U.S. census block group data in conjunction with 2010 LandScan U.S. population data.

Number of square miles within FEMA-designated flood hazard area

This is an estimate of the square miles of area within the special flood hazard area by county. Data and calculation of special flood hazard areas are provided by FEMA using 2011 National Flood Hazard Layer data. Data are defined by 1% annual chance of coastal or riverine flooding. This is also known as the 100-year flood zone, in which a flood event had a 1 in 100 chance of being equaled or exceeded in any given year.

Percent area within FEMA-designated flood hazard area

This is a county estimate of the percentage of total area within the special flood hazard area. Data and calculation of special flood hazard area are provided by FEMA using 2011 National Flood Hazard Layer data. Data are defined by 1% annual chance of coastal or riverine flooding. This is also known as the 100-year flood zone, in which a flood event had a 1 in 100 chance of being equaled or exceeded in any given year.

Percent of hospital beds within flood hazard area (No data available for Wisconsin)

This is an estimation of the percentage of hospital beds within the special flood hazard area. Hospital data is collected from the 2016 American Hospital Association (AHA) survey. A geographic information system was used to identify hospitals within a FEMA-designated flood hazard zone.

Percent of hospitals within flood hazard area (No data available for Wisconsin)

This is an estimation of the percentage of hospitals within the special flood hazard area. Hospital data is collected from the 2016 American Hospital Association (AHA) survey. A geographic information system was used to identify hospitals within a FEMA-designated flood hazard zone.

Explore definitions and explanations of terminology found on the portal, like age-adjusted rate and confidence intervals.