Lake Erie

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 Erie.  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.

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 Erie Description

Lake Erie is the shallowest of the five Great Lakes with a maximum depth of 64 m or 210 ft. While only the fourth largest Great Lake with a water surface area of 25,655 square kilometers or 9,910 square miles. In the United States, about 10.5 million US residents live along Lake Erie and in Canada, roughly 1.9 million Canadians live near Lake Erie.

Data:

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

The data are plotted in degrees Celsius. 

Close

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

Data Interpretation:

This time series shows the sea ice extent for Lake Erie from 1979 to 2023. During the last five years, there has been no notable trend and values are within the 10th and 90th percentiles.

Indicator and source information:

The time series shows the lake ice extent for Lake Erie 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.

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.

Close

Sea level varies due to the force of gravity, the Earth’s rotation and irregular features on the ocean floor. Other forces affecting sea levels include temperature, wind, ocean currents, tides, and other similar processes.

Description of time series:

The time series shows the relative lake level for this region. During the last five years, there has been a significant downward trend and values were above the 90^th  percentile of all observed data in the time series.

Indicator and source information:

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

Data background and limitations:

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

Values indicate cumulative annual heatwave intensity and duration in a region in degree-days

Description of Time Series: This time series shows the average integrated degree day value for Lake Erie. During the last five years there has been no significant trend and the five-year average is between the 10th and 90th percentiles of all observed data in the time series.

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 Erie. During the last five years there has been no trend and the five-year average is within the 10th and 90th percentiles of all observed data in the time series.

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 Erie. 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 43 indicates that between 2015 and 2020 the average concentration levels of chlorophyll a in Lake Erie 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 Erie Description

Lake Erie is the shallowest of the five Great Lakes with a maximum depth of 64 m or 210 ft. While only the fourth largest Great Lake with a water surface area of 25,655 square kilometers or 9,910 square miles. In the United States, about 10.5 million US residents live along Lake Erie and in Canada, roughly 1.9 million Canadians live near Lake Erie.

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 a significant  upward trend.

Description of gauge:

The gauge value of 39 indicates that between 2014 and 2019 the average concentration of zooplankton biomass in Lake Erie waters was 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 Erie Description

Lake Erie is the shallowest of the five Great Lakes with a maximum depth of 64 m or 210 ft. While only the fourth largest Great Lake with a water surface area of 25,655 square kilometers or 9,910 square miles. In the United States, about 10.5 million US residents live along Lake Erie and in Canada, roughly 1.9 million Canadians live near Lake Erie.

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 Erie were around historic levels, and there is no recent trend apparent.   

Commercial Landings Gauge

The gauge value of 72 indicates that the mean annual commercial landings between 2010 and 2015 for Lake Erie was only higher than 72% 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 Erie Description

Lake Erie is the shallowest of the five Great Lakes with a maximum depth of 64 m or 210 ft. While only the fourth largest Great Lake with a water surface area of 25,655 square kilometers or 9,910 square miles. In the United States, about 10.5 million US residents live along Lake Erie and in Canada, roughly 1.9 million Canadians live near Lake Erie.

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 Erie from 2000 to 2022. During the last five years, there has been no notable trend and values have remained within the 10th and 90th percentiles of all observed data in the time series.

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 may be inflated by the Covid-19 pandemic, as there was no consistent way for states to report pandemic-related closures

Values correspond to the total coastal population for a given region

Time Series

The 2017 – 2022 average coastal population around Lake Erie was around historic levels, and the recent trend is not different from historical trends. 

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 Erie between 2014 and 2019 was similar to historical levels, although no trend is apparent over that same period.

Gauge

The gauge value of 60  indicates that coastal employment between 2014 and 2019 for Lake Erie was higher than 60% 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 2015 and 2020 the average change in coastal county tourism GDP showed a significant decreasing trend.

Indicator Source Information

Coastal tourism Gross Domestic Product is the total measure (in billions of 2012 dollars) of goods and services provided from various industries involved in tourism services and products 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.

Close

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 2015 and 2020 the average change in coastal county real wage compensation showed a decreasing trend.

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.

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 2015 and 2020 the average change in coastal county employment showed a decreasing trend.

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.