Climate Change Threatens Health: Data Sources and Map Methods

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Air Pollution: Smog, Smoke and Pollen

In NRDC's 2007 issue paper, Sneezing and Wheezing: How Global Warming Could Increase Ragweed Allergies, Air Pollution, and Asthma, pages 14-16 describe the methodology by which we created the online air pollution map on our pages. "Unhealthy ozone days" are those sites where at least one day per summer, on average, did not meet the US EPA's health-based standard for ground-level ozone smog, in the five study years from 2002-2006. The presence of ragweed in an area can mean allergenic pollen is also being produced in late summer and into autumn, which is often the same time that ozone smog is at its worst, posing a "double-whammy" to health for people with allergies and asthma.

Extreme Heat: More Intense Hot Days and Heat Waves

Background: The frequency, duration, and intensity of heat waves in the U.S. are projected to increase substantially by 2090 due to climate change.[1] Extreme heat is a significant public health threat and has been linked to increases in premature mortality, hospitalizations and emergency room visits.

Extreme Heat Vulnerability Indicator: The percentage of the U.S. affected by heat waves has risen since the 1970s, distinguished by a rise in extremely high nighttime temperatures, as well as daily high temperatures well above normal.[2] We applied the 90th percentile value of daily maximum summer temperatures as a measure of extreme heat, that is, daily temperatures exceeding those values were considered "extreme." Summer was calculated as June, July and August (JJA) temperatures at each meteorological station for which data were available, as is common practice in climate analyses. In addition, a 30-year period was used as a baseline, in this case a 1961-1990 reference period against which the most recent decade of data was compared, namely 2000-2009 summer daily temperatures.

Data Source: Data from all cooperative weather stations for all years historically from the National Climatic Data Center was collected. The data through 2008 was purchased from EarthInfo, a private vendor that collects NCDC data and makes it available on DVDs. 2009 data was downloaded manually from NCDC. Geographic detail for sites was also from NCDC. The NCDC defines cooperative stations as: "U.S. stations operated by local observers which generally report max/min temperatures and precipitation. National Weather Service (NWS) data are also included in this dataset. The data receive extensive automated + manual quality control."

Data Preparation: Data was assembled in an SQL data base. Each record represented a single site-month of data. The total was 24 million site-month records or a total of more than 720 million site-days of data.

Calculations: For each site we calculated the 90th percentile for the maximum temperature during the reference period of June, July and August of 1961-1990. All days June, July and August 2000-2009 at the same site were then compared against the reference period 90th percentile value. The total number of days that exceeded the 90th percentile reference value was computed. County-level averages were then computed by averaging site-level data within the county.

Sites were excluded from the analysis if A) they had less than 75% of possible reference days; B) one entire year from the 2000-2009 period was missing; or C) they had less than 75% of possible current period days.

Map: In a summer defined as June, July and August, there are a total of 92 days, so the "expected" number of days that would exceed the 90th percentile is 10% of 92, or 9.2 days. Rounding to the nearest whole number of days, locations with over 9 days on average per summer with temperatures above that station's 90th percentile reference value had "more than expected" number of days of extreme heat. The highest category (greater than 13.8 days) gives a sense of those locations with over approximately two weeks of these "more than expected" hot days in a recent decade, relative to temperatures in the 30 years from 1961-1990.

Note, too, that "extreme heat" is defined by local temperatures at each site; the map does not compare temperatures in one part of the U.S. to those in another region.

Infectious Diseases: Dengue Fever, West Nile Virus, and Lyme Disease

For NRDC's 2009 issue paper, Fever Pitch: Mosquito-Borne Dengue Fever Threat Spreading in the Americas, we created an online Appendix that describes the method we used to create the map of dengue fever vulnerability you're viewing now.

Drought: Threats to Water and Food Security

Background: Global warming is projected to alter precipitation patterns, increase the frequency and intensity of major storm events, and increase risks of floods throughout the U.S. and particularly the Midwest and Northeast.[1] Over the period from 2000 to 2009, roughly 30 to 60% of the U.S. land area experienced drought conditions at any one time.[2]

Drought Vulnerability Indicators: Drought vulnerability is indicated by the extreme low flow days that are defined as less than the 5th percentile for each monitoring station. This category is classified by USGS as "severe hydrological drought".[3] Current conditions (2000 -2009) were compared to historical conditions using a thirty-year reference period (1961-1990, in this case), consistent with other climate change-related studies.

Streamflow Data

Data Source: Data from all streamflow gauging stations for all years historically from the United States Geological Survey was collected. The data through 2009 was purchased from EarthInfo, a private vendor that collects USGS data and makes it available on DVDs. Watershed data also comes from the USGS (http://water.usgs.gov/GIS/huc.html). The original watershed data was at the HUC12-level (high resolution). These watersheds were aggregated to HUC4 based on HUC ID.

Data Preparation: Data was assembled in an SQL data base. Each record represented a single site-month of data. The total was 7 million site-months or a total of more than 210 million site-days of data.

Calculations: A 95th and 5th percentile value for the reference period (1961-1990) was calculated for each site for each day of the year using a moving average of 7 days before and after the day. For example, the reference period percentiles for June 15th would be calculated by selecting all days June 8-June 22 for the years 1961-1990 (15 x 30 possible days = 450 days contribute to the percentile calculations). June 16th would be based on all days June 9-June 23 and so on. For the first and last six days of the year (Jan 1- Jan 6 and Dec 26-Dec 31), the reference period moving average included days from 1960 and 1991. Current flows at a site were then compared against the reference period percentiles day by day. For example, the ten possible June 15th values from 2000-2009 would be compared against the 95th and 5th percentiles for that day from the reference period. Watershed-level averages were then computed by averaging site-level data within the watersheds.

Analysis was limited to stations that make up the Hydro-Climatic Data Network, a subset of stations that are unaffected by artificial diversions. See appendix for a description of these sites below.

Sites were excluded from the analysis if A) they had less than 75% of possible reference days; B) two entire years from the 2000-2009 period were missing; or C) they had less than 75% of possible current period days.

Map: Color gradations reflect terciles of the data distribution.

Flood Stage Data

Data Source: Data was collected from the USGS WaterWatch website (http://waterwatch.usgs.gov?/new/?id=wwdp2_2).

Data Preparation: We developed Python scripts to iterate through all states and download/format all available flood data 2000-2009. Tabular data was converted to geographic data by linking to the dataset described above on USGS station number.

Calculations: No additional calculations were performed. Graduated circles were mapped based on the "No. of days above flood stage" variable from USGS.

Map: Graduated circles reflect natural data breaks in the distribution. Note: flood stage information is not currently available for all Hydro-Climatic Data Network (HCDN) streamflow gauge sites.

Appendix: Definition of HCDN sites

HCDN Description: Pasted from USGS Hydro-Cliatic Data Network: Streamflow Data Set 1874-1988 by By J.R. Slack, Alan M. Lumb, and Jurate Maciunas Landwehr. USGS Water-Resources Investigations Report 93-4076

The potential consequences of climate change to continental water resources are of great concern in the management of those resources. Critically important to society is what effect fluctuations in the prevailing climate may have on hydrologic conditions, such as the occurrence and magnitude of floods or droughts and the seasonal distribution of water supplies within a region. Records of streamflow that are unaffected by artificial diversions, storage, or other works of man in or on the natural stream channels or in the watershed can provide an account of hydrologic responses to fluctuations in climate. By examining such records given known past meteorologic conditions, we can better understand hydrologic responses to those conditions and anticipate the effects of postulated changes in current climate regimes. Furthermore, patterns in streamflow records can indicate when a change in the prevailing climate regime may have occurred in the past, even in the absence of concurrent meteorologic records.

A streamflow data set, which is specifically suitable for the study of surface-water conditions throughout the United States under fluctuations in the prevailing climatic conditions, has been developed. This data set, called the Hydro-Climatic Data Network, or HCDN, consists of streamflow records for 1,659 sites throughout United States and its Territories. Records cumulatively span the period 1874 through 1988, inclusive, and represent a total of 73,231 water years of information.

Development of the HCDN Data Set: Records for the HCDN were obtained through a comprehensive search of the extensive surface- water data holdings of the U.S. Geological Survey (USGS), which are contained in the USGS National Water Storage and Retrieval System (WATSTORE). All streamflow discharge records in WATSTORE through September 30, 1988, were examined for inclusion in the HCDN in accordance with strictly defined criteria of measurement accuracy and natural conditions. No reconstructed records of "natural flow" were permitted, nor was any record extended or had missing values "filled in" using computational algorithms. If the streamflow at a station was judged to be free of controls for only a part of the entire period of record that is available for the station, then only that part was included in the HCDN, but only if it was of sufficient length (generally 20 years) to warrant inclusion. In addition to the daily mean discharge values, complete station identification information and basin characteristics were retrieved from WATSTORE for inclusion in the HCDN. Statistical characteristics, including the monthly mean discharge, as well as the annual mean, minimum and maximum discharge values, were derived for the records in the HCDN data set. For a full description of the development and content of the Hydro-Climatic Data Network, please take a look at the HCDN Report.

Flooding: Devastating Floods and Heavy Rains

Background: Global warming is projected to alter precipitation patterns, increase the frequency and intensity of major storm events, and increase risks of floods throughout the U.S. and particularly the Midwest and Northeast.[1] Flooding can cause a range of health impacts and risks, including: death and injury, contaminated drinking water, hazardous material spills, increased populations of disease-carrying insects and rodents, moldy houses, and community disruption and displacement. In recent years, a higher percentage of rainfall in the U.S. has come in the form of intense single-day events.[2]

Flooding Vulnerability Indicators: Vulnerability to flooding is indicated by the frequency of extreme high flow days and the frequency of days where flood conditions were recorded, consistent with the USGS WaterWatch program.[3] Extreme high streamflow is defined as above the 95th percentile for each monitoring station. Flood conditions are indicated by days above flood stage, which is defined by the Nation Weather service as the level of surface water where there is a hazard to lives, property, or commerce. Current conditions (2000 -2009) were compared to historical conditions using a 30-year reference period (1961-1990 in this case), consistent with other climate change-related studies.

Streamflow Data

Data Source: Data from all streamflow gauging stations for all years historically from the United States Geological Survey was collected. The data through 2009 was purchased from EarthInfo, a private vendor that collects USGS data and makes it available on DVDs. Watershed data also comes from the USGS (http://water.usgs.gov/GIS/huc.html). The original watershed data was at the HUC12-level (high resolution). These watersheds were aggregated to HUC4 based on HUC ID.

Data Preparation: Data was assembled in an SQL data base. Each record represented a single site-month of data. The total was 7 million site-months, or a total of more than 210 million site-days of data.

Calculations: A 95th and 5th percentile value for the reference period (1961-1990) was calculated for each site for each day of the year using a moving average of 7 days before and after the day. For example, the reference period percentiles for June 15th would be calculated by selecting all days June 8-22 for the years 1961-1990 (15 x 30 possible days = 450 days contribute to the percentile calculations). June 16th would be based on all days June 9-June 23 and so on. For the first and last 6 days of the year (Jan 1-6 and Dec 26-31) the reference period moving average included days from 1960 and 1991. Current flows at a site were then compared against the reference period percentiles day by day. For example, the ten possible June 15th values from 2000-2009 would be compared against the 95th and 5th percentiles for that day from the reference period. Watershed-level averages were then computed by averaging site-level data within the watersheds.

Analysis was limited to stations that make up the Hydro-Climatic Data Network a subset of stations that are unaffected by artificial diversions. See appendix for a description of these sites below.

Sites were excluded from the analysis if A) they had less than 75% of possible reference days; B) two entire years from the 2000-2009 period were missing; or C) they had less than 75% of possible current period days.

Map: Color gradations reflect terciles of the data distribution.

Flood Stage Data

Data Source: Data was collected from the USGS WaterWatch website (http://waterwatch.usgs.gov?/new/?id=wwdp2_2).

Data Preparation: We developed Python scripts to iterate through all states and download/format all available flood data 2000-2009. Tabular data was converted to geographic data by linking to the dataset described above on USGS station number.

Calculations: No additional calculations were performed. Graduated circles were mapped based on the "No. of days above flood stage" variable from USGS.

Map: Graduated circles reflect natural data breaks in the distribution. Note: flood stage information is not currently available for all Hydro-Climatic Data Network (HCDN) streamflow gauge sites.

Appendix: Definition of HCDN sites

HCDN Description: Pasted from USGS Hydro-Cliatic Data Network: Streamflow Data Set 1874-1988 by By J.R. Slack, Alan M. Lumb, and Jurate Maciunas Landwehr. USGS Water-Resources Investigations Report 93-4076

The potential consequences of climate change to continental water resources are of great concern in the management of those resources. Critically important to society is what effect fluctuations in the prevailing climate may have on hydrologic conditions, such as the occurrence and magnitude of floods or droughts and the seasonal distribution of water supplies within a region. Records of streamflow that are unaffected by artificial diversions, storage, or other works of man in or on the natural stream channels or in the watershed can provide an account of hydrologic responses to fluctuations in climate. By examining such records given known past meteorologic conditions, we can better understand hydrologic responses to those conditions and anticipate the effects of postulated changes in current climate regimes. Furthermore, patterns in streamflow records can indicate when a change in the prevailing climate regime may have occurred in the past, even in the absence of concurrent meteorologic records.

A streamflow data set, which is specifically suitable for the study of surface-water conditions throughout the United States under fluctuations in the prevailing climatic conditions, has been developed. This data set, called the Hydro-Climatic Data Network, or HCDN, consists of streamflow records for 1,659 sites throughout United States and its Territories. Records cumulatively span the period 1874 through 1988, inclusive, and represent a total of 73,231 water years of information.

Development of the HCDN Data Set: Records for the HCDN were obtained through a comprehensive search of the extensive surface- water data holdings of the U.S. Geological Survey (USGS), which are contained in the USGS National Water Storage and Retrieval System (WATSTORE). All streamflow discharge records in WATSTORE through September 30, 1988 were examined for inclusion in the HCDN in accordance with strictly defined criteria of measurement accuracy and natural conditions. No reconstructed records of "natural flow" were permitted, nor was any record extended or had missing values "filled in" using computational algorithms. If the streamflow at a station was judged to be free of controls for only a part of the entire period of record that is available for the station, then only that part was included in the HCDN, but only if it was of sufficient length (generally 20 years) to warrant inclusion. In addition to the daily mean discharge values, complete station identification information and basin characteristics were retrieved from WATSTORE for inclusion in the HCDN. Statistical characteristics, including the monthly mean discharge, as well as the annual mean, minimum and maximum discharge values, were derived for the records in the HCDN data set. For a full description of the development and content of the Hydro-Climatic Data Network, please take a look at the HCDN Report.

Extreme Weather: Record-Breaking Events in 2011

This map was generated based on "NRDC's Extreme Weather Map 2011" project which created an animated graphic that tracked "record-breaking" weather events over the course of 2011 within the 50 United States. The following modifications to the original methods were made to enable the geographic specificity of this map:

Monthly temperature, rain and snowfall records were compiled into a single map. Multiple monthly records at a single meteorological station are marked by a single icon.

Extreme drought point locations were replaced by a shaded polygon representing the geographic areas found to experience "Exceptional Drought" (D4) in 2011 by the National Drought Mitigation Center's Drought Monitor. http://droughtmonitor.unl.edu/dmshps_archive.htm

Extreme flood point locations to mark the Lower Mississippi Floods (http://www1.ncdc.noaa.gov/pub/data/cmb/special-reports/2011-spring-climate-extremes/ustatus_miss-II.jpg) and Upper Midwest floods (http://www.noaa.gov/extreme2011/midwest_flood.html) were replaced with shaded polygons created by digitizing maps produced by the National Climatic Data Center. Because flooded area maps were unavailable, the flood point locations marking record high peak flows due to the impacts of Hurricane Irene and Tropical Storm Lee were retained as point data.

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  1. Karl TR, Melillo JM, Peterson TC, editors. Global climate change impacts in the United States. New York: Cambridge University Press; 2009.
  2. EPA "Climate Change Indicators in the United States" (2010), EPA 430-R-1—007. www.epa.gov/climatechange/indicators.html
  3. US Geological Survey (USGS) WaterWatch: Flood conditions website. http://waterwatch.usgs.gov/new/index.php?id=ww_flood