Lake Huron

Sea surface temperature is defined as the average temperature of the top few millimeters of the ocean. Sea surface temperature monitoring tells us how the ocean and atmosphere interact, as well as providing fundamental data on the global climate system.

Data Interpretation:

Time series: The time series shows the integrated sea surface temperature across Lake Huron.  During the last five years there has been no notable trend and values were between the 10th and 90th percentile of all observed data in the time series.

Gauge: The gauge value of 39 indicates that between 2016 and 2021 the mean sea surface temperature for Lake Huron was only higher than 39% of the temperatures between 1995 and 2021 .

Description of Sea Surface Temperature:

Sea surface temperature (SST) is defined as the temperature of the top few millimeters of the ocean. This temperature directly or indirectly  impacts the rate of all physical, chemical, and most biological processes occurring in the ocean. SST is globally monitored by sensors on satellites, buoys, ships, ocean reference stations, autonomous underwater vehicles (AUVs) and other technologies. 

SST monitoring tells us how the ocean and atmosphere interact, as well as providing fundamental data on the global climate system. This information also aids us in weather prediction, i.e. identifying the onset of El Niño and La Niña cycles - multiyear shifts in atmospheric pressure and wind speeds. These shifts affect ocean circulation, global weather patterns, and marine ecosystems. SST anomalies have been linked to shifting marine resources. With warming temperatures, we observe the poleward movements of fish and other species. Temperature extremes—both ocean heatwaves and cold spells—have been linked to coral bleaching as well as fishery and aquaculture mortality. We present the annual average SST in all regions.
 

Lake Huron Description

Lake Huron is the second largest Great Lake with a water surface area of 59,565 square kilometers or 23,000 square miles. About 1.5 million US residents and 1.5 million Canadians live along Lake Huron.

Data:

Great Lakes SST data were accessed from (https://coastwatch.glerl.noaa.gov/glsea/glsea.html). 

The data are plotted in degrees Celsius. 

Values correspond to annual maximum percentage of total lake surface area of lake ice cover

Data Interpretation:

Time series: This time series shows the lake ice extent for Lake Huon from 1979 to 2021. During the last five years, there has been no notable trend and values are within the 10th and 90th percentiles.

Gauge: The gauge value of 59 indicates that mean lake ice extent between 2016 and 2021 for Lake Huron was higher than 59% of the lake ice extent measurements between 1979 and 2021.

Indicator and source information:

The time series shows the lake ice extent for the Great Lakes Region for each winter.  The annual maximum extent as percentage of total lake surface area between December and May of each winter season in the Great Lakes region.

Lake Huron Description

Lake Huron is the second largest Great Lake with a water surface area of 59,565 square kilometers or 23,000 square miles. About 1.5 million US residents and 1.5 million Canadians live along Lake Huron

Data background and limitations:

Great Lakes ice data was accessed from the NOAA Great Lakes Environmental Research Laboratory, https://www.glerl.noaa.gov/data/ice.  Original ice charts from 1973 through 1988 are from the Canadian Ice Service. Beginning in 1989, the source was the U.S. National Ice Center (USNIC). Currently, data from both Canadian and U.S. sources are combined in USNIC's daily products. The data are plotted as a percentage of total lake surface area.

Description of time series:

The time series shows the lake level for this region. During the last five years, there has been no significant trend and values were above the 90^th  percentile of all observed data in the time series.

 Description of gauge:

The gauge value of 95 indicates that the mean lake level between 2016 and 2021 for Lakes Michigan and Superior was higher than 95% of the lake level values across the entire time series

Description of Sea Level:

Water levels of the Great Lakes fluctuate dramatically in response to a variety of factors. The lakes have experienced record high levels in 2019 and 2020, less than a decade after an extended period of low water ending in 2013—showcasing the dramatic changes water levels can experience from year to year. Changing water levels can impact water dependent industries such as shipping, fisheries, tourism, and coastal infrastructure including coastal roads, piers, and wetlands. 

With 40 percent of Americans living in densely populated coastal areas, having a clear understanding of sea level trends is critical to societal and economic well being. Measuring and predicting sea levels, tides and storm surge are important for determining coastal boundaries, ensuring safe shipping, and emergency preparedness, etc. NOAA monitors sea levels using tide stations and satellite laser altimeters. Tide stations around the globe tell us what is happening at local levels, while satellite measurements provide us with the average height of the entire ocean. Taken together, data from these sources are fed into models that tell us how our ocean sea levels are changing over time. For this site, data from tide stations around the US were combined to create regionally averaged records of sea-level change since 1980. We present data for all regions.

Water levels of the Great Lakes fluctuate dramatically in response to a variety of factors. The lakes have experienced record high levels in 2019 and 2020, less than a decade after an extended period of low water ending in 2013—showcasing the dramatic changes water levels can experience from year to year. Changing water levels can impact water dependent industries such as shipping, fisheries, tourism, and coastal infrastructure including coastal roads, piers, and wetlands. 

Great Lakes water levels are continuously monitored by U.S. and Canadian federal agencies in the region through a binational partnership. Water level monitoring stations are operated by NOAA's Center for Operational Oceanographic Products and Services (CO-OPS) and the Department of Fisheries and Oceans' Canadian Hydrographic Service. The U.S. Army Corps of Engineers (Detroit, Chicago, Buffalo) and Environment and Climate Change Canada play crucial roles in research, coordination of data and operational seasonal water level forecasts for the basin.

Lake Huron Description

Lake Huron is the second largest Great Lake with a water surface area of 59,565 square kilometers or 23,000 square miles. About 1.5 million US residents and 1.5 million Canadians live along Lake Huron.

Lake Michigan Description

Lake Michigan is the third largest Great Lake and has a water surface area of 57,573 square kilometers or 22,300 square miles. Unlike the other four Great Lakes, Lake Michigan is entirely within the United States. Roughly 12 million people live along the shores of Lake Michigan. (While Lakes Michigan and Huron are often referred to as two separate lakes, technically they are one hydrologic system connected by the Straits of Mackinac.)

Data Source:

Great Lakes data come from:https://www.glerl.noaa.gov/data/wlevels/#overview and http://www.greatlakescc.org/wp36/

Description of Time Series: Between 2016 and 2021 the average integrated degree day value was high, and  showed an increasing trend.

Description of Gauge: The gauge value of 88 indicates that between 2016 and 2021 the average integrated degree day value was significantly higher than the median average integrated degree day value between 1982 and 2021.

Description of Marine Heatwaves (Spatial Coverage and Integrated Degree Days):

When ocean temperatures reach exceptionally high temperatures in a specific area for an extended period of time, it has the potential to affect the biology and chemistry of an ecosystem. These events, where temperatures exceed the 90th percentile of 30-year historic values for 5 or more days, are known as Marine Heatwaves. Understanding the duration, intensity, and spatial coverage of marine heatwaves is important to coastal communities as increased incidence of intense heatwave events may affect the area available for fishing, recreational, or cultural practices and the overall health of a local ecosystem. We present two metrics for marine heatwaves: a spatial coverage indicator that displays the fraction of total area within an LME affected by a marine heatwave in a given year, and an integrated degree day indicator that compounds the number of heatwave days with the maximum sea-surface temperature in an affected area.

Lake Huron Description

Lake Huron is the second largest Great Lake with a water surface area of 59,565 square kilometers or 23,000 square miles. About 1.5 million US residents and 1.5 million Canadians live along Lake Huron.

Indicator Source Information:

The marine heatwave data shown here are calculated by NOAA’s National Centers for Environmental Information using Optimum Interpolation Sea Surface Temperature (OISST) data. The NOAA 1/4° OISST is a long term Climate Data Record that incorporates observations from different platforms (satellites, ships, buoys and Argo floats) into a regular global grid. The dataset is interpolated to fill gaps on the grid and create a spatially complete map of sea surface temperature. Satellite and ship observations are referenced to buoys to compensate for platform differences and sensor biases.

Data Background and Caveats:

Heatwave metrics are calculated using OISST, a product that uses some forms of interpolation to fill data gaps. Heatwaves are defined by Hobday et al., 2016 as distinct events where SST anomaly reaches the 90th percentile in a pixel for at least 5 days, separated out by 3 or more days.

Values indicate monthly percent of an LME area affected by heatwave

Description of Time Series: This time series shows the monthly heatwave spatial coverage for Lake Huron. During the last five years there has been no significant trend and the five-year average is within the 10th and 90th percentiles of all observed data in the time series.

Description of Gauge: The gauge value of 40 indicates that between 2016 and 2021 the average  area fraction value was lower than the median area fraction between 1982 and 2021.

Gauge Values

• 0 - 10: The five-year area fraction value is very low compared to the median value.
• 10 - 25: The five-year area fraction value is much lower than the median value.
• 25 - 50: The five-year area fraction value is lower than the median value.
• 50: The five-year area fraction value average equals the median value.
• 50 - 75: The five-year area fraction value is higher than the median value.
• 75 - 90: The five-year area fraction value is much higher than the median value.
• 90 - 100: The five-year area fraction value is very high compared to the median value.

Indicator Source Information:

The marine heatwave data shown here are calculated by NOAA’s National Centers for Environmental Information using Optimum Interpolation Sea Surface Temperature (OISST) data. The NOAA 1/4° OISST is a long term Climate Data Record that incorporates observations from different platforms (satellites, ships, buoys and Argo floats) into a regular global grid. The dataset is interpolated to fill gaps on the grid and create a spatially complete map of sea surface temperature. Satellite and ship observations are referenced to buoys to compensate for platform differences and sensor biases.

Data Background and Caveats:

Heatwave metrics are calculated using OISST, a product that uses some forms of interpolation to fill data gaps. Heatwaves are defined by Hobday et al., 2016 as distinct events where SST anomaly reaches the 90th percentile in a pixel for at least 5 days, separated out by 3 or more days

The time series shows monthly averages of satellite-derived surface chlorophyll concentration estimates by year

Data Interpretation:

Time series: This time series shows the average concentration levels of chlorophyll ɑ for Lake Huron. During the last five years there has been no significant trend and values have remained within the 10th and 90th percentiles of all observed data in the time series.

Gauge: The gauge value of 48 indicates that between 2015 and 2020 the average concentration levels of chlorophyll a in Lake Huron were slightly lower than the long term median of all chlorophyll ɑ concentration levels between 1998 and 2020.

Description of Indicator

The U.S. Environmental Protection Agency (U.S. EPA) Great Lakes National Program Office (GLNPO) makes estimates of surface chlorophyll-a concentrations derived from satellite observations of the surface waters of the Great Lakes. These data are used to supplement data collected during GLNPO’s in-situ Great Lakes monitoring programs. The satellite-derived estimates reported here are based on a band-ratio retrieval algorithm developed using GLNPO monitoring data (Lesht et al. 2013; 2016) applied to data from the Sea-viewing Wide Field-of-view (SeaWiFS, 1998–2007) and Moderate resolution imaging spectroradiometer (MODIS, 2002–present) ocean color sensors. Source data are extracted from NASA Level-L2 image files at each GLNPO station location. This workbook contains the monthly averages of satellite-derived surface chlorophyll concentration estimates in this lake by year for the period 1998-present. By contouring the month/year averages in this file this analysis is intended to illustrate long-term trends in surface chlorophyll concentrations with temporal detail not possible using the twice-a-year regular monitoring data.

Lake Huron Description

Lake Huron is the second largest Great Lake with a water surface area of 59,565 square kilometers or 23,000 square miles. About 1.5 million US residents and 1.5 million Canadians live along Lake Huron.

Data Background and Caveats

Data obtained from NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group; (2014): Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Ocean Color Data, NASA OB.DAAC. doi: 10.5067/ORBVIEW-2/SEASWIFS_OC.2014.0 and Moderate-resolution imaging spectroradiometer (MODIS) Ocean Color Data; 2018 Reprocessing. NASA OB.DAAC. doi: 10.5067/AQUA/MODIS/L2/OC/2018. Accessed on various dates.

Source data are extracted from daily NASA Level-L2 image files. Pixels in a 5x5 pixel box surrounding each station location are examined and the average, standard deviation, and number of valid pixels within the box are calculated for each daily image. The daily images are sorted by month and for each month those daily station averages for which there are 19 or more valid pixels (of the 25) and for which the standard deviation is less than 0.5*max_allowable_value (lake/basin dependent) and for which the mean + standard deviation is less than 1.1*max_allowable_value and for which the mean - one standard deviation is greater than zero are averaged to provide the monthly average for that station. Then the monthly values for the stations are averaged to produce the lake average.

1. Data have been subjected to GLNPO’s quality assurance procedures, and approved by the GLNPO program technical lead and database manager. GLNPO contractors and grantees are responsible for verifying their data before submitting it to GLNPO. Once received by GLNPO, the data are evaluated for completeness, accuracy, transcription errors, and compliance with quality documentation requirements. Unless otherwise noted by data qualifiers, these data meet all quality standards relative to their original purpose. It is the user's responsibility to validate these data consistent with their intended purpose. Data is provided on the condition that neither GLNPO nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the information. GLNPO, USEPA, and the US Government provide no warranty, nor accept any liability occurring from any incomplete, incorrect, or misleading data, or from any incorrect, incomplete, or misleading use of the data.
2. Any presentation or publication of this information should include an acknowledgement of USEPA GLNPO by stating "information for this work was provided in part by USEPA GLNPO's Monitoring Program." When possible, references to the specific dataset should also be included.

Description of time series:

Between 2014 and 2019 the average concentration of zooplankton biomass showed no significant trend.

Description of gauge:

The gauge value of 17 indicates that between 2014 and 2019 the average concentration of zooplankton biomass in Lake Huron waters was significantly lower than the median value of all zooplankton biomass concentration levels between 1997 and 2019.

Description of Zooplankton:

Zooplankton are a diverse group of animals found in oceans, bays, and estuaries. By eating phytoplankton, and each other, zooplankton play a significant role in the transfer of materials and energy up the oceanic food web (e.g., fish, birds, marine mammals, humans.) Like phytoplankton, environmental and oceanographic factors continuously influence the abundance, composition and spatial distribution of zooplankton. These include the abundance and type of phytoplankton present in the water, as well as the water’s temperature, salinity, oxygen, and pH. Zooplankton can rapidly react to changes in their environment. For this reason monitoring the status of zooplankton is essential for detecting changes in, and evaluating the status of ocean ecosystems. We present the annual average total biovolume of zooplankton in the Alaska, California Current, Gulf of Mexico, Hawai'i-Pacific Islands, Northeast and Great Lakes regions.

Lake Huron Description

Lake Huron is the second largest Great Lake with a water surface area of 59,565 square kilometers or 23,000 square miles. About 1.5 million US residents and 1.5 million Canadians live along Lake Huron.

Data Background:

Zooplankton data for the Great Lakes region were obtained from the U.S. Environmental Protection Agency Central Data Exchange (CDX): https://cdx.epa.gov/, Great Lakes Environmental Database System (GLENDA).  GLENDA  was developed to provide data entry, storage, access and analysis capabilities to meet the needs of mass balance modelers and other potential users of Great Lakes data.

Commercial Landings Time Series

Between 2010 and 2015, commercial landings from Lake Huron were significantly lower than historic levels, and there is a significant downward trend apparent.   

Commercial Landings Gauge

The gauge value of 8 indicates that the mean annual commercial landings between 2010 and 2015 for Lake Huron was only higher than 8% of all years between 1980 and 2015.

Description of Commercial Fishing (Landings and Revenue):

Commercial landings are the weight of, or revenue from, fish that are caught, brought to shore, processed, and sold for profit. It does not include sport or subsistence (to feed themselves) fishermen or for-hire sector, which earns its revenue from selling recreational fishing trips to saltwater and freshwater anglers. 

Commercial landings make up a major part of coastal economies. U.S. commercial fisheries are among the world’s largest and most sustainable; producing seafood, fish meal, vitamin supplements, and a host of other products for both domestic and international consumers. 

The weight (tonnage), and revenue from the sale of commercial landings provides data on the ability of marine ecosystems to continue to supply these important products.

Lake Huron Description

Lake Huron is the second largest Great Lake with a water surface area of 59,565 square kilometers or 23,000 square miles. About 1.5 million US residents and 1.5 million Canadians live along Lake Huron.

Data Source:

Commercial fish catch data (called production) for the Great Lakes were published by the Great Lakes Fishery Commission in 1962 (Technical Report No.3) and covered the period 1867-1960. A supplement covering the years 1961-1968 was released in 1970, and a revised edition covering the years 1867-1977 was published in 1979. This third update of a web-based version covers the period 1867-2015.

Beach closures are the number of days when beach water quality is determined to be unsafe.

Data Interpretation:

Time series: This time series shows the average number of beach closure days in Lake Huron from 2000 to 2021. During the last five years, there has been a significant downward trend and values have remained within the 10th and 90th percentiles of all observed data in the time series.

Gauge: The gauge value of 64 indicates that between 2016 and 2021 the average number of beach closure days in Lake Huron was above 64% of all beach closure days between 2000 and 2021.

Indicator and source information:

Unsafe water quality may have significant impacts on human health, local economies, and the ecosystem. Beach water quality is determined by the concentration of bacteria in the water (either Enterococcus sp. or Escherichia coli). 

The US Environmental Protection Agency (EPA) supports coastal states, counties and tribes in monitoring beach water quality, and notifying the public when beaches must be closed. The information presented is from states, counties, and tribes that submit data to the EPA Beach Program reporting database (BEACON). Data obtained from the EPA BEACON 2.0 website have been provided to EPA by the coastal and Great Lakes states, tribes and territories that receive grants under the BEACH Act. Data were refined to closure, by state or territory, by year.

Data background and limitations:

Data compiled by states or territories are combined in regions defined as US Large Marine Ecosystems (LME). Changes in the number of beach closure days may be driven by changes in the number of beaches monitored under the BEACH Act versus by state and local municipalities and not by changes in water and/or air quality. Not all US beach closures are captured in this database, because not all beaches in a state or territory are monitored through the EPA BEACH Act. Data that were not identified to a water body or identified as inland water were not included. Data for beaches monitored by state and local municipalities are not included. 

Data from 2020 and beyond may be inflated by the Covid-19 pandemic, as there was no consistent way for states to report pandemic-related closures.

Time Series

The 2014 – 2019 average coastal population around Lake Huron was consistent with historic levels, and the recent trend is not different from historical trends. 

Gauge

The gauge value of 42 indicates that the coastal population between 2014 and 2019 for Lake Huron was higher than 42% of the coastal population values between 1970 and 2019.

Description of Coastal Population:

While marine ecosystems are important for people all across the country, they are essential for  people living in coastal communities. The population density of coastal counties is over six times greater than inland counties. In the U.S. coastal counties make up less than 10 percent of the total land area (not including Alaska), but account for 39 percent of the total population. From 1970 to 2010, the population of these counties increased by almost 40% and are projected to increase by over 10 million people or 8+% into the 2020s. 

The population density of an area is an important factor for economic planning, emergency preparedness, understanding environmental impacts, resource demand, and many other reasons. Thus, this indicator is important to track. We present the number of residents within all regions.

Extreme Gauge values

A value of zero on the gauge means that the average coastal population over the last 5 years of data was below any annual population level up until that point, while a value of 100 would indicate the average over that same period was above any annual population level up until that point.

Indicator Source Information:

The American Community Survey (ACS) helps local officials, community leaders, and businesses understand the changes taking place in their communities. It is the premier source for detailed population and housing information about our nation. 

Data Background and Caveats:

The values represented here are coastal county population estimates for states bordering US Large Marine Ecosystems as calculated by the US Census Bureau from the American Community Survey.

Values correspond to total employment in all industries in the coastal counties of a given region

Time Series

Average coastal employment around Lake Huron between 2014 and 2019 was similar to historical levels, although no trend is apparent over that same period.

Gauge

The gauge value of 47 indicates that coastal employment between 2014 and 2019 for Lake Huron was higher than 47% of all years between 2005 and 2019.

Data Source:

Coastal employment numbers were downloaded from the NOAA ENOW Explorer Tool, filtered to present only coastal county values using the Census Bureau’s list of coastal counties within each state. ENOW Explorer streamlines the task of obtaining and comparing economic data, both county and state, for the six sectors dependent on the ocean and Great Lakes: living resources, marine construction, marine transportation, offshore mineral resources, ship and boat building, and tourism and recreation. Data are derived from Economics: National Ocean Watch (ENOW), available on NOAA’s Digital Coast. Of note is that these data fail to include self-employed individuals. Coastal county employment numbers were then summed within each region for reporting purposes.

Extreme Gauge values

A value of zero on the gauge means that the average coastal employment level over the last 5 years of data was below any annual employment level up until that point, while a value of 100 would indicate the average over that same period was above any annual employment level up until that point.

Values correspond to percent change in the GDP of the Tourism Sector of Coastal Counties in US States that border a region

Description of Time Series: Between 2013 and 2018 the average change in coastal county tourism GDP showed an upward trend.

Description of Gauge: The gauge value of 62 indicates that between 2014 and 2018 the average change in coastal county tourism sector GDP was higher than the median change in coastal county tourism sector GDP between 2006 and 2018.

Description of Coastal Tourism:

U.S. coasts are host to a multitude of travel, tourism, and recreation activities. To manage our coasts, plan for development, and assess impacts as a result of coastal hazards including sea level rise, it is important to have baseline economic information. To accomplish this, we need indicators of the economic value of recreation and tourism. We present the annual total change in billions of dollars of goods and services (GDP), employment and annual wages provided from tourism industries in the Gulf of Mexico, Mid-Atlantic, Northeast, Hawaii-Pacific Islands, Southeast, and California Current regions. This data does not include industries located in U.S. territories. 

Data Source:

Coastal Tourism GDP, employment, and real wage data was taken from NOAA’s Office of Coastal Management Economics: National Ocean Watch (ENOW) custom report building tool. Growth was estimated by subtracting the previous year’s value from the current year’s value, then dividing by the previous year’s value to present a percentage. All data was deflated to 2012 constant dollars using the Bureau of Economic Analysis’ chained dollar methodology.

Extreme Gauge values:

A value of zero on the gauge means that the average coastal tourism over the last 5 years of data was below any annual coastal tourism level up until that point, while a value of 100 would indicate the average over that same period was above any annual coastal tourism level up until that point.

Values correspond to percent change in the total real wage compensation of the Tourism Sector of Coastal Counties in US States that border a region

Description of Time Series: Between 2013 and 2018 the average change in coastal county real wage compensation showed an increasing  trend.

Description of Gauge: Between 2013 and 2018 the average change in coastal county tourism sector real wage compensation was higher than the median change in coastal county tourism sector real wage compensation between 2006 and 2018.

Indicator Source Information:

Coastal tourism wage is the measure of wages (nominal) paid to employees in tourism industries along the coast. Data for Coastal Counties come from the US Census Bureau. This dataset represents US counties and independent cities which have at least one coastal border and select non-coastal counties and independent cities based on proximity to estuaries and other coastal counties. The dataset is built to support coastal and ocean planning and other activities pursuant to the Energy Policy Act, Coastal Zone Management Act, Magnuson-Stevens Fishery Conservation and Management Act, National Environmental Policy Act, Rivers and Harbors Act and the Submerged Lands Act.

Description of Coastal Tourism:

U.S. coasts are host to a multitude of travel, tourism, and recreation activities. To manage our coasts, plan for development, and assess impacts as a result of coastal hazards including sea level rise, it is important to have baseline economic information. To accomplish this, we need indicators of the economic value of recreation and tourism. We present the annual total change in billions of dollars of goods and services (GDP), employment and annual wages provided from tourism industries in the Gulf of Mexico, Mid-Atlantic, Northeast, Hawaii-Pacific Islands, Southeast, and California Current regions. This data does not include industries located in U.S. territories. 

Data Source:

Coastal Tourism GDP, employment, and real wage data was taken from NOAA’s Office of Coastal Management Economics: National Ocean Watch (ENOW) custom report building tool. Growth was estimated by subtracting the previous year’s value from the current year’s value, then dividing by the previous year’s value to present a percentage. All data was deflated to 2012 constant dollars using the Bureau of Economic Analysis’ chained dollar methodology.

Extreme Gauge values:

A value of zero on the gauge means that the average coastal tourism over the last 5 years of data was below any annual coastal tourism level up until that point, while a value of 100 would indicate the average over that same period was above any annual coastal tourism level up until that point.

Values correspond to percent change in the total employment of the Tourism Sector of Coastal Counties in US States that border a region

Description of Time Series: Between 2013 and 2018 the average change in coastal county employment showed no trend.

Description of Gauge: Between 2013 and 2018 the average change in coastal county tourism sector employment was higher than the median change in coastal county tourism sector employment between 2006 and 2018.

Indicator Source Information:

Coastal tourism employment is the total measure of jobs in tourism industries along the coast.  Data for Coastal Counties come from the US Census Bureau. This dataset represents US counties and independent cities which have at least one coastal border and select non-coastal counties and independent cities based on proximity to estuaries and other coastal counties. The dataset is built to support coastal and ocean planning and other activities pursuant to the Energy Policy Act, Coastal Zone Management Act, Magnuson-Stevens Fishery Conservation and Management Act, National Environmental Policy Act, Rivers and Harbors Act and the Submerged Lands Act.

Description of Coastal Tourism:

U.S. coasts are host to a multitude of travel, tourism, and recreation activities. To manage our coasts, plan for development, and assess impacts as a result of coastal hazards including sea level rise, it is important to have baseline economic information. To accomplish this, we need indicators of the economic value of recreation and tourism. We present the annual total change in billions of dollars of goods and services (GDP), employment and annual wages provided from tourism industries in the Gulf of Mexico, Mid-Atlantic, Northeast, Hawaii-Pacific Islands, Southeast, and California Current regions. This data does not include industries located in U.S. territories. 

Data Source:

Coastal Tourism GDP, employment, and real wage data was taken from NOAA’s Office of Coastal Management Economics: National Ocean Watch (ENOW) custom report building tool. Growth was estimated by subtracting the previous year’s value from the current year’s value, then dividing by the previous year’s value to present a percentage. All data was deflated to 2012 constant dollars using the Bureau of Economic Analysis’ chained dollar methodology.