The average temperature in December 2024 was 38.25 degrees Fahrenheit in the United States, the fourth-largest country in the world. The country has extremely diverse climates across its expansive landmass. Temperatures in the United States On the continental U.S., the southern regions face warm to extremely hot temperatures all year round, the Pacific Northwest tends to deal with rainy weather, the Mid-Atlantic sees all four seasons, and New England experiences the coldest winters in the country. The North American country has experienced an increase in the daily minimum temperatures since 1970. Consequently, the average annual temperature in the United States has seen a spike in recent years. Climate Change The entire world has seen changes in its average temperature as a result of climate change. Climate change occurs due to increased levels of greenhouse gases which act to trap heat in the atmosphere, preventing it from leaving the Earth. Greenhouse gases are emitted from various sectors but most prominently from burning fossil fuels. Climate change has significantly affected the average temperature across countries worldwide. In the United States, an increasing number of people have stated that they have personally experienced the effects of climate change. Not only are there environmental consequences due to climate change, but also economic ones. In 2022, for instance, extreme temperatures in the United States caused over 5.5 million U.S. dollars in economic damage. These economic ramifications occur for several reasons, which include higher temperatures, changes in regional precipitation, and rising sea levels.

The monthly average temperature in the United States between 2020 and 2025 shows distinct seasonal variation, following similar patterns. For instance, in August 2025, the average temperature across the North American country stood at 22.98 degrees Celsius. Rising temperatures Globally, 2016, 2019, 2021 and 2024 were some of the warmest years ever recorded since 1880. Overall, there has been a dramatic increase in the annual temperature since 1895. Within the U.S. annual temperatures show a great deal of variation depending on region. For instance, Florida tends to record the highest maximum temperatures across the North American country, while Wyoming recorded the lowest minimum average temperature in recent years. Carbon dioxide emissions Carbon dioxide is a known driver of climate change, which impacts average temperatures. Global historical carbon dioxide emissions from fossil fuels have been on the rise since the industrial revolution. In recent years, carbon dioxide emissions from fossil fuel combustion and industrial processes reached over 37 billion metric tons. Among all countries globally, China was the largest emitter of carbon dioxide in 2023.

The average temperature in the contiguous United States reached 55.5 degrees Fahrenheit (13 degrees Celsius) in 2024, approximately 3.5 degrees Fahrenheit higher than the 20th-century average. These levels represented a record since measurements started in ****. Monthly average temperatures in the U.S. were also indicative of this trend. Temperatures and emissions are on the rise The rise in temperatures since 1975 is similar to the increase in carbon dioxide emissions in the U.S. Although CO₂ emissions in recent years were lower than when they peaked in 2007, they were still generally higher than levels recorded before 1990. Carbon dioxide is a greenhouse gas and is the main driver of climate change. Extreme weather Scientists worldwide have found links between the rise in temperatures and changing weather patterns. Extreme weather in the U.S. has resulted in natural disasters such as hurricanes and extreme heat waves becoming more likely. Economic damage caused by extreme temperatures in the U.S. has amounted to hundreds of billions of U.S. dollars over the past few decades.

The U.S. Geological Survey (USGS) Water Resources Mission Area (WMA) is working to address a need to understand where the Nation is experiencing water shortages or surpluses relative to the demand for water need by delivering routine assessments of water supply and demand and an understanding of the natural and human factors affecting the balance between supply and demand. A key part of the Integrated Water Availability Assessments (IWAAs) Trends and Drivers project is identifying long-term national trends in water availability, including groundwater and surface water quantity, quality, and use. This data release contains Mann-Kendall monotonic trend analyses for 58 observed annual (calendar, water, and climate years) and monthly stream temperature metrics. Data were collated (Oliver et al., 2024) from the U.S. Environmental Protection Agency (USEPA), USGS, and U.S. Department of Agriculture (USDA), including the Water Quality Portal (WQP), National Water Information System (NWIS), EcoSHEDS, and NorWeST databases (URLs below). Metrics were calculated at a total of 2,418 stream temperature monitoring locations within the conterminous United States, Alaska, Hawaii, and Puerto Rico that passed initial screening criteria, and are also included as part of this data release. Stream temperature metrics include monthly and annual summaries, extreme (i.e., min/max) and central (i.e., mean) tendencies, variability, and timing characteristics. Monthly ("mean_[month]") and annual ("mean") mean, annual maximum of seven-day averages ("high7d" and "date_high7d"), and annual sinusoidal regression metrics ("ampl_median" and "phase_median") were calculated using daily mean values. Monthly ("high7dmax_[month]") and annual ("high7dmax" and "date_high7dmax") maximum of seven-day averages and monthly ("cvmax_[month]") and annual ("cvmax") coefficient of variation were calculated using daily maximum values. The monthly ("low7dmin_[month]") and annual ("low7dmin" and "date_low7dmin") minimum of seven-day averages were calculated using daily minimum values. Trend magnitudes were computed for 2,213 qualifying monitoring locations using a modified form of the Theil-Sen slope that accounts for missing values. Trend analyses were computed between years 1948-2022 and trend periods are between 10-70 years long. Metric time series analyzed for trends satisfied two requirements to be considered complete records: (i) have values in at least eight out of every 10 years (i.e., 80 percent) within the entire trend period and (ii) have values in at least eight out of the first and last 10 years of the trend period. Trends at each site are available for five main periods: (i) 1980-2020, (ii) 1981-2010, (iii) 1990-2020, (iv) 1991-2020, and (v) 2000-2020. Trend periods (ii) and (iv) represent the previous and current climate normals periods, respectively, and were added in v2.0 of this data release. Additionally, trends for various ≥10-year sub-periods (e.g., 1997-2015) are included. The longest trend period for a given site-metric combination is explicitly identified. 90% confidence intervals (5th-95th percentiles) are also included for each trend. Caution must be exercised when utilizing monotonic trend analyses conducted over periods of up to several decades (and in some places longer ones) due to the potential for confounding deterministic gradual trends with multi-decadal climatic fluctuations. In addition, trend results for USGS locations (site_id prefix "USGS-") are only available for post-reservoir construction years to avoid including abrupt changes arising from the construction of larger reservoirs in periods for which gradual monotonic trends are computed. Reservoir impacts on non-USGS sites were not evaluated. Other abrupt changes, such as changes to water withdrawals and wastewater return flows, or episodic disturbances with multi-year recovery periods, such as wildfires, are also not evaluated for any site. Sites with pronounced abrupt changes or other non-monotonic trajectories of change may require more sophisticated trend analyses than those presented in this data release. EcoSHEDS: https://www.usgs.gov/apps/ecosheds NorWeST: https://www.fs.usda.gov/rm/boise/AWAE/projects/NorWeST.html First release: 2024-01-31 (ver. 1.0) Revised: 2025-06-13 (ver. 2.0)

This data release (DR) is the update of the U.S. Geological Survey - ScienceBase data release by Bera (2024), with the processed data through September 30, 2024. The primary data for water year 2024 (a water year is the 12-month period, October 1 through September 30, in which it ends) is downloaded from ANL website (Argonne National Laboratory, 2025) and is processed following the guidelines documented in Over and others (2010). Processed WY24 data are appended to update the Watershed Data Management (WDM) database file ARGN23.WDM (Bera, 2024), which was then renamed as ARGN24.WDM. Daily potential evapotranspiration (PET) was computed from average daily air temperature, average daily dewpoint temperature, daily total wind speed, and daily total solar radiation and disaggregated to hourly PET, in thousandths of an inch, using the Fortran program LXPET (Murphy, 2005) for the period 10/01/2023 – 09/30/2024. This DR also describes the Watershed Data Management (WDM) database file ARGN24.WDM. ARGN24.WDM file contains nine data series: air temperature, in degrees Fahrenheit (dsn 400), dewpoint temperature, in degrees Fahrenheit (dsn 500), wind speed, in miles per hour (dsn 300), solar radiation, in Langleys (dsn 600), computed potential evapotranspiration, in thousandths of an inch (dsn 200), and four data-source flag series: for air temperature (dsn 410), dewpoint temperature (dsn 510), wind speed (dsn 310), and solar radiation (dsn 610), respectively, from January 1,1948, to September 30, 2024. Missing and apparently erroneous data values were replaced with adjusted values from nearby weather stations used as “backup.” The Illinois Climate Network (Water and Atmospheric Resources Monitoring Program, 2025) station at St. Charles, Illinois, was used as "backup" for the hourly air temperature, solar radiation, and wind speed data. The Midwestern Regional Climate Center (Midwestern Regional Climate Center, 2025) provided the hourly dewpoint temperature and wind speed data collected by the National Weather Service from the station at O'Hare International Airport and used as "backup." Each data source flag is of the form "xyz", which allows the user to determine its source and the methods used to process the data (Over and others, 2010). This DR provides WDM file ARGN24.WDM and the following tab-delimited text files, each with data from January 1, 1948, to September 30, 2024: "Air_temperature.txt" contains hourly air temperature data in degrees Fahrenheit and associated data-source flags. "Dewpoint_temperature.txt" contains hourly dewpoint temperature data in degrees Fahrenheit and associated data-source flags. "Solar_radiation.txt" contains hourly solar radiation data in Langleys and associated data-source flags. "Wind_speed.txt" contains hourly wind speed data in miles per hour and associated data-source flags. "PET.txt" contains hourly PET data, in thousandths of an inch. Tab-delimited text files can be opened with any text editor or Microsoft Excel. To open the WDM file user needs to use the SARA Timeseries utility executable file attached in this page. References Cited: Argonne National Laboratory, 2025, Meteorological data, accessed on February 18, 2025, at https://www.atmos.anl.gov/ANLMET/numeric/. Bera, M., 2024, Meteorological Database, Argonne National Laboratory, Illinois, January 1, 1948 - September 30, 2023: U.S. Geological Survey data release, https://doi.org/10.5066/P146RBHK. Midwestern Regional Climate Center, 2025, Meteorological data, accessed on February 19, 2025, at https://mrcc.purdue.edu/CLIMATE/. Murphy, E.A., 2005, Comparison of potential evapotranspiration calculated by the LXPET (Lamoreux Potential Evapotranspiration) Program and by the WDMUtil (Watershed Data Management Utility) Program: U.S. Geological Survey Open-File Report 2005-1020, 20 p., https://pubs.er.usgs.gov/publication/ofr20051020. Over, T.M., Price, T.H., and Ishii, A.L., 2010, Development and analysis of a meteorological database, Argonne National Laboratory, Illinois: U.S. Geological Survey Open-File Report 2010-1220, 67 p., http://pubs.usgs.gov/of/2010/1220/. Water and Atmospheric Resources Monitoring Program, Illinois Climate Network, 2025, Illinois State Water Survey, 2204 Griffith Drive, Champaign, IL 61820-7495. Data accessed on January 9, 2025, at http://dx.doi.org/10.13012/J8MW2F2Q.

This dataset contains two phases of research. The first dataset includes several variables that were sampled across aspen stands in the Santa Rosa, Ruby, and Jarbidge mountain ranges (Great Basin, Northern Nevada, USA) in 2010 and 2011. Across 101 aspen sites, several plot-level attributes were collected (e.g. elevation, slope, aspen stand type). For each plot, data describing live trees (both those less than 7.5 cm diameter and those greater than/equal to 7.5 cm) are included, such as species, diameter, and age. The data set also includes information for dead trees greater than/equal to 7.5 cm diameter (e.g. species, location, diameter). The second dataset includes tree ring measurements (for live trees greater than/equal to 7.5 cm diameter) and monthly climate data for a subset of sites (n = 20) that were included in the first phase. For this subset of 20 sites, we analyzed the relationship between tree ring width measurements and climate variables. The climate variables represent monthly total precipitation, average temperature, and climatic moisture index values by year for the period of record.

United States Natural Gas: Spot Price: Henry Hub-I data was reported at 3.260 USD/MN BTU in 05 May 2025. This records an increase from the previous number of 3.100 USD/MN BTU for 02 May 2025. United States Natural Gas: Spot Price: Henry Hub-I data is updated daily, averaging 2.930 USD/MN BTU from Jan 1997 (Median) to 05 May 2025, with 7145 observations. The data reached an all-time high of 23.860 USD/MN BTU in 17 Feb 2021 and a record low of 1.210 USD/MN BTU in 11 Nov 2024. United States Natural Gas: Spot Price: Henry Hub-I data remains active status in CEIC and is reported by U.S. Energy Information Administration. The data is categorized under World Trend Plus’s Commodity Market – Table US.P026: Petroleum Spot Price: Energy Information Administration. Previously named as Henry Hub Released once a week (every Wednesday) with data from Wednesday to Friday of the previous week up to Tuesday of the current week. If Wednesday falls on a holiday, the data will be released on the next business day. Price spike on Feb 11 to 18, 2021 data was caused by the effect of decline in natural gas production brought about by the cold wave experienced during the month. Price spike on Jan 12, 2024 data was caused by the anticipation of increased natural gas consumption because of the weather forecast for well-below-normal temperatures for most of the United States over the long weekend. [COVID-19-IMPACT]

'''DEFINITION'''

The medsea_omi_tempsal_sst_area_averaged_anomalies product for 2024 includes unfiltered Sea Surface Temperature (SST) anomalies, given as monthly mean time series starting on 1982 and averaged over the Mediterranean Sea, and 24-month filtered SST anomalies, obtained by using the X11-seasonal adjustment procedure (see e.g. Pezzulli et al., 2005; Pisano et al., 2020). This OMI is derived from the CMEMS Reprocessed Mediterranean L4 SST satellite product (SST_MED_SST_L4_REP_OBSERVATIONS_010_021, see also the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-MEDSEA-SST.pdf), which provides the SSTs used to compute the evolution of SST anomalies (unfiltered and filtered) over the Mediterranean Sea. This reprocessed product consists of daily (nighttime) optimally interpolated 0.05° grid resolution SST maps over the Mediterranean Sea built from the ESA Climate Change Initiative (CCI) (Embury et al., 2024) and Copernicus Climate Change Service (C3S) initiatives, including also an adjusted version of the AVHRR Pathfinder dataset version 5.3 (Saha et al., 2018) to increase the input observation coverage. Anomalies are computed against the 1991-2020 reference period. The 30-year climatology 1991-2020 is defined according to the WMO recommendation (WMO, 2017) and recent U.S. National Oceanic and Atmospheric Administration practice (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate). The reference for this OMI can be found in the first and second issue of the Copernicus Marine Service Ocean State Report (OSR), Section 1.1 (Roquet et al., 2016; Mulet et al., 2018).

'''CONTEXT'''

Sea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). The Mediterranean Sea is a climate change hotspot (Giorgi F., 2006). Indeed, Mediterranean SST has experienced a continuous warming trend since the beginning of 1980s (e.g., Pisano et al., 2020; Pastor et al., 2020). Specifically, since the beginning of the 21st century (from 2000 onward), the Mediterranean Sea featured the highest SSTs and this warming trend is expected to continue throughout the 21st century (Kirtman et al., 2013).

'''KEY FINDINGS'''

Over the past four decades (1982–2024), Sea Surface Temperature (SST) in the Mediterranean Sea has increased at an average rate of 0.032 ± 0.001 °C per year, resulting in a total warming of approximately 1.4 °C, in line with findings from previous studies (e.g., Pisano et al., 2020; Pastor et al., 2020). In 2024, the Mediterranean Sea continued to experience the intense SST warming that began in May 2022 (e.g., Martínez et al., 2023; Marullo et al., 2023). The annual mean SST reached 21.4 °C, which is 1.2 °C above the 1991–2020 climatological average of 20.2 °C, marking the highest annual value in the observational record. The year was characterized by a pronounced temperature rise, with a minimum basin-average SST of 16.5 °C in February and a maximum of 28 °C in August — the highest monthly mean SST recorded in the region since 1982. '''DOI (product):''' https://doi.org/10.48670/moi-00268

'''DEFINITION'''

OMI_CLIMATE_SST_BAL_area_averaged_anomalies product includes time series of monthly mean SST anomalies over the period 1982-2024, relative to the 1991-2020 climatology, averaged for the Baltic Sea. The SST Level 4 analysis products that provide the input to the monthly averages are taken from the reprocessed product SST_BAL_SST_L4_REP_OBSERVATIONS_010_016 with a recent update to include 2023. The product has a spatial resolution of 0.02 in latitude and longitude. The OMI time series runs from Jan 1, 1982 to December 31, 2024 and is constructed by calculating monthly averages from the daily level 4 SST analysis fields of the SST_BAL_SST_L4_REP_OBSERVATIONS_010_016 product . The climatology period from 1991 to 2020 (30 years) is selected according to WMO recommendations (WMO, 2017) and the most recent practice from the U.S. National Oceanic and Atmospheric Administration practice (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate). See the Copernicus Marine Service Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018) for more information on the OMI product.

'''CONTEXT'''

Sea Surface Temperature (SST) is an Essential Climate Variable (GCOS) that is an important input for initialising numerical weather prediction models and fundamental for understanding air-sea interactions and monitoring climate change (GCOS 2010). The Baltic Sea is a region that requires special attention regarding the use of satellite SST records and the assessment of climatic variability (Høyer and She 2007; Høyer and Karagali 2016). The Baltic Sea is a semi-enclosed basin with natural variability and it is influenced by large-scale atmospheric processes and by the vicinity of land. In addition, the Baltic Sea is one of the largest brackish seas in the world. When analysing regional-scale climate variability, all these effects have to be considered, which requires dedicated regional and validated SST products. Satellite observations have previously been used to analyse the climatic SST signals in the North Sea and Baltic Sea (BACC II Author Team 2015; Lehmann et al. 2011). Recently, Høyer and Karagali (2016) demonstrated that the Baltic Sea had warmed 1-2 oC from 1982 to 2012 considering all months of the year and 3-5 °C when only July-September months were considered. This was corroborated in the Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018).

'''CMEMS KEY FINDINGS'''

The basin-average trend of SST anomalies for Baltic Sea region amounts to 0.039±0.003°C/year over the period 1982-2024 which corresponds to an average warming of 1.68°C. Adding the North Sea area, the average trend amounts to 0.026±0.002°C/year over the same period, which corresponds to an average warming of 1.19°C for the entire region since 1982.

'''DOI (product):''' https://doi.org/10.48670/moi-00205

In 2023/2024, the average winter temperature in Germany was *** degrees Celsius. That winter was part of a growing list of warmer winters in the country. Figures had increased noticeably compared to the 1960s. Warmer in the winter Everyone has a different perception of what actually makes a cold or warm winter, but the fact is that winter temperatures are, indeed, changing in Germany, and its 16 federal states are feeling it. Also in 2022/2023, Bremen and Hamburg in the north recorded the highest average figures at around 4 degrees each. The least warm states that year, so to speak, were Thuringia, Saxony, and Bavaria. The German National Meteorological Service (Deutscher Wetterdienst or DWD), a federal office, monitors the weather in Germany. Global warming Rising temperatures are a global concern, with climate change making itself known. While these developments may be influenced by natural events, human industrial activity has been another significant contributor for centuries now. Greenhouse gas emissions play a leading part in global warming. This leads to warmer seasons year-round and summer heat waves, as greenhouse gas emissions cause solar heat to remain in the Earth’s atmosphere. In fact, as of 2022, Germany recorded **** days with a temperature of at least 30 degrees Celcius, which was more than three times the increase compared to 2021.

'''DEFINITION'''

The OMI_CLIMATE_SST_BAL_trend product includes the cumulative/net trend in sea surface temperature anomalies for the Baltic Sea from 1982-2024. The climatology period from 1991 to 2020 (30 years) is selected according to WMO recommendations (WMO, 2017) and the most recent practice from the U.S. National Oceanic and Atmospheric Administration practice (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate). The cumulative trend is the rate of change (°C/year) scaled by the number of years (43 years). The SST Level 4 analysis products that provide the input to the trend calculations are taken from the reprocessed product SST_BAL_SST_L4_REP_OBSERVATIONS_010_016 with a recent update to include 2024. The product has a spatial resolution of 0.02 in latitude and longitude. The OMI time series runs from Jan 1, 1982 to December 31, 2024 and is constructed by calculating monthly averages from the daily level 4 SST analysis fields of the SST_BAL_SST_L4_REP_OBSERVATIONS_010_016. The climatology period from 1991 to 2020 (30 years) is selected according to WMO recommendations (WMO, 2017) and the most recent practice from the U.S. National Oceanic and Atmospheric Administration practice (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate). See the Copernicus Marine Service Ocean State Reports for more information on the OMI product (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018). The times series of monthly anomalies have been used to calculate the trend in SST using Sen’s method with confidence intervals from the Mann-Kendall test (section 3 in Von Schuckmann et al., 2018).

'''CONTEXT'''

SST is an essential climate variable that is an important input for initialising numerical weather prediction models and fundamental for understanding air-sea interactions and monitoring climate change. The Baltic Sea is a region that requires special attention regarding the use of satellite SST records and the assessment of climatic variability (Høyer and She 2007; Høyer and Karagali 2016). The Baltic Sea is a semi-enclosed basin with natural variability and it is influenced by large-scale atmospheric processes and by the vicinity of land. In addition, the Baltic Sea is one of the largest brackish seas in the world. When analysing regional-scale climate variability, all these effects have to be considered, which requires dedicated regional and validated SST products. Satellite observations have previously been used to analyse the climatic SST signals in the North Sea and Baltic Sea (BACC II Author Team 2015; Lehmann et al. 2011). Recently, Høyer and Karagali (2016) demonstrated that the Baltic Sea had warmed 1-2oC from 1982 to 2012 considering all months of the year and 3-5oC when only July- September months were considered. This was corroborated in the Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018).

'''CMEMS KEY FINDINGS'''

SST trends were calculated for the Baltic Sea area and the whole region including the North Sea, over the period January 1982 to December 2024. The average trend for the Baltic Sea domain (east of 9°E longitude) is 0.039°C/year, which represents an average warming of 1.68°C for the 1982-2023 period considered here. When the North Sea domain is included, the trend decreases to 0.026°C/year corresponding to an average warming of 1.19°C for the 1982-2024 period. Trends are highest for the Baltic Sea and the North Sea, compared to other regions.

'''DOI (product):''' https://doi.org/10.48670/moi-00206

GapMaps Environmental Risk data sourced from Applied Geographic Solutions (AGS) covers five separate risks:

• Hurricane
• Tornado
• Hail
• Damaging Winds
• Coastal Storm Surge Inundation

A wide range of applications benefit from these databases, including insurance underwriting, retail merchandising, and real estate. In some cases, many of these variables are simply useful for reference purposes or general interest.

For automated merchandising systems, the environmental risk climate data (average January, July, and annual temperatures, rainfall, and snowfall) can help to avoid costly stocking errors. The heating and cooling degree days can assist in determining demand for heating and cooling equipment, for example. These measures also are of general interest, especially in residential real estate applications. The seismology risk includes both earthquake risk and tsunami risk, and can provide surprising insights in areas outside the well-known seismic zones of the far western states.

The air quality measures captured in the Environmental Risk Dataset can be important to individuals contemplating relocation, health care research, and commercial site selection and economic development.

The nature of the local terrain in the Environment Risk Dataset can be of great interest to real estate developers and others; local terrain variables include elevation (minimum, maximum, average), slope (average, maximum), and ruggedness. The TRI (Terrain Ruggedness Index) is a new index which encapsulates the overall nature of the local terrain.

Wildfire are an annual major risk in most areas of the western United States, and while large fires often burn in the rugged and generally unpopulated mountainous areas, the combination of dry conditions, heat, and winds can often lead to major disasters along what is known as the wildland-urban interface.

In addition, the coastal sea level rise risk index included in the Environmental Risk dataset is useful for long term planning, assuming that sea levels may rise over the coming decades due to climate change.

Detailed Methodology document available on the AGS website: https://appliedgeographic.com/wp-content/uploads/2024/05/AGS-Environmental-2024A.pdf