AndreaBozzo/ceres-open-data-index huggingface dataset:
Modeled change in the Seasonality between the modeled 2041-2070 period (HadGEM2-AO r1i1p1 model) and the 1971-2000 Normal period

Originators: US Environmental Protection Agency Publisher: US EPA Office of Research & Development (ORD) - National Health and Environmental Effects Research Laboratory (NHEERL) Publication place: Corvallis, OR Publication date: Time Period of Data: 1900-2010; Projected data for 2041-2070. Data location: GeoPlatform ("https://www.geoplatform.gov/") and EPA Environmental Dataset Gateway (https://edg.epa.gov/). Abstract: We apply the hydrologic landscapes (HL) concept to assess the hydrologic vulnerability of the western United States (U.S.) to projected climate conditions. Our goal is to understand the potential impacts for stakeholder-defined interests across large geographic areas. The basic assumption of the HL approach is that catchments that share similar physical and climatic characteristics are expected to have similar hydrologic characteristics. We map climate vulnerability by integrating the HL approach into a retrospective analysis of historical data to assess variability in future climate projections and hydrology, which includes temperature, precipitation, potential evapotranspiration, snow accumulation, climatic moisture, surplus water, and seasonality of water surplus. Projections that are not within two-standard deviations of the historical decadal average contribute to the vulnerability index for each metric. The resulting vulnerability maps show that temperature and potential evapotranspiration are consistently projected to have high vulnerability indices for the western U.S. Precipitation vulnerability is not as spatially-uniform as temperature. The highest elevation areas with snow are projected to experience significant changes in snow accumulation. The seasonality vulnerability map shows that specific mountainous areas in the West are most prone to changes in seasonality, whereas many transitional terrains are moderately susceptible. This paper illustrates how the HL approach can help assess climatic and hydrologic vulnerability across large spatial scales. By combining the HL concept and climate vulnerability analyses, we provide a planning approach that could allow resource managers to consider how future climate conditions may impact important economic and conservation resources. Purpose: These data were created in support of the US EPA’s ACE CIVA 2.3, Task Project (QAPP: E-WED-0030854). However, these climate data and hydrologic landscape summaries should have broad applicability for hydrological, geomorphic, or ecological modeling, management, and restoration.  This raster contains the modeled change in the seasonality of the month of maximum surplus precipitation between the target time period and model (in title) and the 1971-2000 Normal period (1 = same season, 0 = earlier season, 2 = later season).

Tags: 020:094, 1900 -2010, 2041-2070, climate, downloadable data, environment, united states, water
Organization: U.S. Environmental Protection Agency
EF BR

EF_BR is a subset of facilities from FRS_PROGRAM_FACILITY and associated best-available geospatial coordinates. Facility Registry Service (FRS) data are refrehsed daily. The layer shows only points from the 'BR' (Biennial Report) program. The program query is 'BR'. The BR subset is updated weekly.

Tags: air, alabama, alaska, american samoa, arizona, arkansas, california, colorado, connecticut, delaware, environment, exposure, facilities, florida, georgia, hawaii, hazards, health, human, idaho, illinois, indiana, iowa, kansas, kentucky, louisiana, maine, maryland, massachusetts, michigan, minnesota, mississippi, missouri, monitoring, montana, nebraska, nevada, new hampshire, new jersey, new mexico, new york, north carolina, north dakota, ohio, oklahoma, oregon, pennsylvania, puerto rico, quality, regulatory, rhode island, sites, south carolina, south dakota, tennessee, texas, toxics, united states, utah, vermont, virgin islands, virginia, washington, washington dc, west virginia, wisconsin, wyoming
Organization: U.S. Environmental Protection Agency
Eastern Connecticut coastal waters sewage no-discharge zone, 2003, EPA HQ OWOW

This geospatial dataset depicts a sewage no-discharge zone established under Clean Water Act Section 312. The area(s) depicted are zones where the discharge of any sewage, treated or untreated, from vessels is prohibited. This layer was created using information from the corresponding Federal Register notice and is created for informational purposes, not to be used for enforcement.

Tags: 020:078, connecticut, downloadable data, environment, marine, water
Organization: U.S. Environmental Protection Agency
High Marsh at Dividing, NJ, Lower Delaware Bay, Intermediate Sea Level Rise Scenario, “Protect Developed Dry Land” model protection scenario, EPA ORD NCEA

This raster GIS dataset contains 5-meter-resolution cells depicting the areas of HIGH marsh gain (value=1), lost (value=-1) and remaining (no change; value=0). High marsh (HM) was defined as the aggregation of irregularly-flooded marsh [SLAMM category 7] and transitional salt marsh [SLAMM category 20]. HM is covered by water only sporadically (once per day or less). Based on SLAMM simulation outputs, we generated the gain and loss map by using the “Raster Calculator” tool under “Spatial Analyst Tools” in ArcGIS software. The methodology consists of the three steps listed below (where we use low marsh [LM] as an example). The same process can be applied to other SLAMM land cover categories. 

1) Open ArcMap, add SLAMM simulation raster outputs (all SLAMM categories) for baseline year and future years. 

2) In Raster Calculator, set the SLAMM code equal to 8 (low marsh = SLAMM category 8) to generate a new raster. Each individual cell in the new raster is assigned a value of “0” or “1”. “1” is low marsh and “0” is any other SLAMM land cover category. Perform this step for both the baseline year and future year. 

3) In Raster Calculator, subtract the new raster for the baseline year from the new raster for the future year (formula = new future year raster - new baseline year raster). The calculation generates a new raster, in which each individual cell is assigned a value of “-1”, “0”, or “1”. Based on the calculation, “-1” means low marsh loss in the future (the cell has converted from low marsh to a different SLAMM category), “0” means low marsh is remaining (the cell stays the same), and “1” means low marsh gain in the future (the cell has converted from a different SLAMM category to low marsh).

Prior SLAMM work has been performed in the Delaware Bay, but our methods differ in that we derive results for specific marsh areas and utilize more recent, higher resolution elevation data (2015 USGS CoNED Topobathy Model: New Jersey and Delaware), the most recent SLR projections, and site-specific accretion data (through 2016). These SLAMM simulations were performed as part of a larger project by the USEPA on frameworks and methods for characterizing relative wetland vulnerabilities. 

Note: additional raster files from this project are available upon request. These include files from low and high SLR scenarios and different model protection scenarios. For more information, contact Jordan West (West.Jordan@epa.gov).

Tags: 020:094, coastal wetland, delaware, downloadable data, ecosystem, environment, estuary, exposure, modeling, natural resources, new jersey, relative vulnerability, sea level rise, united states
Organization: U.S. Environmental Protection Agency
LULC Net Change for: Modeling Future Land Cover and Water Quality Change in Minneapolis, MN, USA to Support Drinking Water Source Protection Decisions

We developed four 2011-2050 land cover change scenarios and modeled the impact of projected land cover change on influent water quality to support long-term planning for the city of Minneapolis, MN.  Baseline land cover was from the NLCD2011 database.  IPCC SRES scenarios (https://www.ipcc.ch/site/assets/uploads/2018/03/sres-en.pdf) interpreted by Sohl et al. (2014) (https://doi.org/10.1890/13-1245.1) for the conterminous United States were downscaled from 250 meters to 30 meters.  The baseline and scenario land cover data were used as input into the Soil Water and Assessment Tool (SWAT) model to assess the effect of outyear projected land cover change on the source of raw water for the city of Minneapolis, MN. SWAT was used to assess the effect of land cover change raw water concentrations of sediment, total nitrogen, and total phosphorus. The IPCC SRES evaluated were A1B, A2, B1, and B2.  The four scenarios were implemented with (A1Bf) and without (A1B) forest recovery (e.g., conversion of cropland (2011) to forest (2050) for a total of eight scenarios.
		
		The LULC net change file reports total change in hectares between 2011 and 2015 for forest, developed, cropland, pastures, and wetland for the four IPCC SRES scenarios with and without forest recovery.

Tags: 010, 020:072, climate, digital spatial data, environment, gis, image processing, imagerybasemapearthcover, land, land cover, land use land cover theme, national geospatial data asset, ngda, u.s. geological survey (usgs), united states
Organization: U.S. Environmental Protection Agency
Heat Wave Characteristics in 50 Large U.S. Cities, 1961–2023

These maps show changes in the number of heat waves per year (frequency); the average length of heat waves in days (duration); the number of days between the first and last heat wave of the year (season length); and how hot the heat waves were, compared with the local temperature threshold for defining a heat wave (intensity). These data were analyzed from 1961 to 2023 for 50 large metropolitan areas. The size of each circle indicates the rate of change per decade. Solid-color circles represent cities where the trend was statistically significant. For more information: www.epa.gov/climate-indicators

Tags: 020:072, air, cities, climate, health, heat, heat waves, indicator, other documents, united states, urban, weather
Organization: U.S. Environmental Protection Agency
Average Change in Drought (Five-Year SPEI) in the Contiguous 48 States, 1900–2020

This map shows how drought conditions have changed across the contiguous 48 states from 1900 to 2020. The data are showed for small regions called climate divisions, as defined by the National Oceanic and Atmospheric Administration. Blue areas represent increased moisture; brown areas represent a decrease or drier conditions. For more information: https://www.epa.gov/climatechange/science/indicators.

Tags: 020:033, climate, downloadable data, drought, dry, indicator, united states, wet
Organization: U.S. Environmental Protection Agency
Modeled change in the average Feddema Moisture Index (multiplied by 1000) between the modeled 2041-2070 period (GFDL-CM3 r1i1p1 model) and the 1971-2000 Normal period.

Originators: US Environmental Protection Agency Publisher: US EPA Office of Research &amp; Development (ORD) - National Health and Environmental Effects Research Laboratory (NHEERL) Publication place: Corvallis, OR Publication date: Time Period of Data: 1900-2010; Projected data for 2041-2070. Data location: GeoPlatform ("https://www.geoplatform.gov/") and EPA Environmental Dataset Gateway (https://edg.epa.gov/). Abstract: We apply the hydrologic landscapes (HL) concept to assess the hydrologic vulnerability of the western United States (U.S.) to projected climate conditions. Our goal is to understand the potential impacts for stakeholder-defined interests across large geographic areas. The basic assumption of the HL approach is that catchments that share similar physical and climatic characteristics are expected to have similar hydrologic characteristics. We map climate vulnerability by integrating the HL approach into a retrospective analysis of historical data to assess variability in future climate projections and hydrology, which includes temperature, precipitation, potential evapotranspiration, snow accumulation, climatic moisture, surplus water, and seasonality of water surplus. Projections that are not within two-standard deviations of the historical decadal average contribute to the vulnerability index for each metric. The resulting vulnerability maps show that temperature and potential evapotranspiration are consistently projected to have high vulnerability indices for the western U.S. Precipitation vulnerability is not as spatially-uniform as temperature. The highest elevation areas with snow are projected to experience significant changes in snow accumulation. The seasonality vulnerability map shows that specific mountainous areas in the West are most prone to changes in seasonality, whereas many transitional terrains are moderately susceptible. This paper illustrates how the HL approach can help assess climatic and hydrologic vulnerability across large spatial scales. By combining the HL concept and climate vulnerability analyses, we provide a planning approach that could allow resource managers to consider how future climate conditions may impact important economic and conservation resources. Purpose: These data were created in support of the US EPA’s ACE CIVA 2.3, Task Project (QAPP: E-WED-0030854). However, these climate data and hydrologic landscape summaries should have broad applicability for hydrological, geomorphic, or ecological modeling, management, and restoration. This raster contains the modeled change in the average Feddema Moisture Index between the 2041-2070 modeled data (specified in the title) relative to the 1971-2000 Normal period.

Tags: 020:094, arizona, california, climate, downloadable data, environment, ground water, idaho, natural resources, nevada, oregon, washington, water
Organization: U.S. Environmental Protection Agency
U.S. Temperature, 1901–2023

This indicator shows how annual average air temperatures have changed in different parts of the United States since the early 20th century (since 1901 for the contiguous 48 states and 1925 for Alaska). The data are shown for climate divisions, as defined by the National Oceanic and Atmospheric Administration. For more information: www.epa.gov/climate-indicators

Tags: 020:072, air, climate, indicator, observations, other documents, rate of change, temperature, u.s. temperatures, united states, weather
Organization: U.S. Environmental Protection Agency
GFPLAIN90_Siberia

All floodplains were derived using modified scripts from GFPLAIN v1.0 (https://github.com/fnardi/GFPLAIN).    This data was created by modifying the scripts to work with 90-m data.
Global FloodPLAIN (GFPLAIN) mapping uses a geomorphic algorithm. Geomorphic FloodPLAIN mapping algorithm GFPLAIN is based on Nardi et al. (2006, 2018).  
The data is available at the hydrobasin 4 level.  HydroBASINS data provides a series of polygon layers that depict watershed boundaries and sub-basin delineations at a global scale (http://www.hydrosheds.org).   

Tags: 020:096, 020:121, drought mitigation, flood-hazard, floodplain, floodplains, global, hydrobasin, non-floodplain, siberia, wetland habitat, wetland mapping
Organization: U.S. Environmental Protection Agency
Heating and Cooling Degree Day by State, 1960–2023 Versus 1895–1959

This indicator shows how heating and cooling degree days have changed by state, based on a comparison of the first 65 years of available data (1895–1959) with the most recent 64 years (1960–2023). For more information: https://www.epa.gov/climate-indicators.   

Tags: 020:033, climate, climatologymeteorologyatmosphere, united states
Organization: U.S. Environmental Protection Agency
GFPLAIN90_SouthAmerica

All floodplains were derived using modified scripts from GFPLAIN v1.0 (https://github.com/fnardi/GFPLAIN).    This data was created by modifying the scripts to work with 90-m data.
Global FloodPLAIN (GFPLAIN) mapping uses a geomorphic algorithm. Geomorphic FloodPLAIN mapping algorithm GFPLAIN is based on Nardi et al. (2006, 2018).  
The data is available at the hydrobasin 4 level.  HydroBASINS data provides a series of polygon layers that depict watershed boundaries and sub-basin delineations at a global scale (http://www.hydrosheds.org).   

Tags: 020:096, 020:121, drought mitigation, flood-hazard, floodplain, floodplains, global, hydrobasin, non-floodplain, south america, wetland habitat, wetland mapping
Organization: U.S. Environmental Protection Agency
Cleanups in My Community

Cleanups in My Community (CIMC) is a public web application that enables integrated access through maps, lists and search filtering to site-specific information EPA has across all cleanup programs. CIMC taps into data publicly available from EPA's Facility Registry Service (FRS) - RCRA Corrective Action facilities, Brownfields properties and grant areas, Superfund NPL sites, other facility data) and web services (water monitoring stations, impaired waters, emergency responses, tribal boundaries, congressional districts, etc.) - and connects to other applications (e.g., Superfund's Public User Database, ACRES, RCRAInfo, etc.) to provide easy seamless access to site-specific cleanup information with explanatory text and within the context of related data. Data can be filtered by cleanup program, geography, environmental indicators, controls, and cleanup stage. CIMC also provides some web services that integrate these data for others to use in their applications.

Tags: alaska, american samoa, cleanup, emergency, facilities, hawaii, indicator, land, monitoring, puerto rico, remediation, response, sites, spills, united states, virgin islands, washington dc, waste, water
Organization: U.S. Environmental Protection Agency
Air Sampling Data for BP Spill/Deepwater Horizon

The Deepwater Horizon oil spill (also referred to as the BP oil spill) began on 20 April 2010 in the Gulf of Mexico on the BP-operated Macondo Prospect. Following the explosion and sinking of the Deepwater Horizon oil rig, a sea-floor oil gusher flowed for 87 days, until it was capped on 15 July 2010. In response to the BP oil spill, EPA sampled air, water, sediment, and waste generated by the cleanup operations.

Tags: air, alabama, cleanup, contaminant, disaster, emergency, environment, florida, hazards, health, human, louisiana, mississippi, response
Organization: U.S. Environmental Protection Agency
Brownfields Site Information

This asset contains all Brownfield facility data. It includes all information necessary to track Brownfields grant recipients' environmental progress at Brownfield properties such as property location, acreage, identification and characterization, past uses, environmental assessment information and analyses, contaminants found, media affected, information on property cleanup, and information on site reuse and land revitalization. Regulatory authority for the collection and use of this information is found in the Small Business Liability Relief and Brownfields Revitalization Act of 2002 ("the Brownfields Law"), which amended the Comprehensive Environmental Response, Compensation and Liability Act (CERCLA) of 1980.

Tags: acres, brownfields, brownfields grants, brownfields redevelopment, brownfields sites, environment, united states
Organization: U.S. Environmental Protection Agency
Low Marsh at Dividing, NJ, Lower Delaware Bay, Intermediate Sea Level Rise Scenario, “Protect Developed Dry Land” model protection scenario, EPA ORD NCEA

This raster GIS dataset contains 5-meter-resolution cells depicting the areas of LOW marsh gain (value=1), lost (value=-1) and remaining (no change; value=0). Low marsh (LM) was defined as regularly flooded marsh [SLAMM category 8]. LM is normally inundated by tidal water at least once per day. Based on SLAMM simulation outputs, we generated the gain and loss map by using the “Raster Calculator” tool under “Spatial Analyst Tools” in ArcGIS software. The methodology consists of the three steps listed below (where we use low marsh [LM] as an example). The same process can be applied to other SLAMM land cover categories. 

1) Open ArcMap, add SLAMM simulation raster outputs (all SLAMM categories) for baseline year and future years. 

2) In Raster Calculator, set the SLAMM codeequal to8 (low marsh = SLAMM category 8) to generate a new raster. Each individual cell in the new raster is assigned a value of “0” or “1”. “1” is low marsh and “0” is any other SLAMM land cover category. Perform this step for both the baseline year and future year. 

3) In Raster Calculator, subtract the new raster for the baseline year from the new raster for the future year (formula = new future year raster - new baseline year raster). The calculation generates a new raster, in which each individual cell is assigned a value of “-1”, “0”, or “1”. Based on the calculation, “-1” means low marsh loss in the future (the cell has converted from low marsh to a different SLAMM category), “0” means low marsh is remaining (the cell stays the same), and “1” means low marsh gain in the future (the cell has converted from a different SLAMM category to low marsh). 

Prior SLAMM work has been performed in the Delaware Bay, but our methods differ in that we derive results for specific marsh areas and utilize more recent, higher resolution elevation data (2015 USGS CoNED Topobathy Model: New Jersey and Delaware), the most recent SLR projections, and site-specific accretion data (through 2016). These SLAMM simulations were performed as part of a larger project by the USEPA on frameworks and methods for characterizing relative wetland vulnerabilities.

 Note: additional raster files from this project are available upon request. These include files from low and high SLR scenarios and different model protection scenarios. For more information, contact Jordan West (West.Jordan@epa.gov).

Tags: 020:094, coastal wetland, delaware, downloadable data, ecosystem, environment, estuary, exposure, modeling, natural resources, new jersey, relative vulnerability, sea level rise, united states
Organization: U.S. Environmental Protection Agency
2012 and 2013 Air Quality Fused Surface for the Conterminous U.S. Map Service

This web service contains a polygon layer that depicts fused air quality predictions for 2012, 2013, and 2014 for census tracts in the conterminous United States. Fused air quality predictions (for ozone and PM2.5) are modeled using a Bayesian space-time downscaling fusion model approach described in a series of three published journal papers: 1) (Berrocal, V., Gelfand, A. E. and Holland, D. M. (2012). Space-time fusion under error in computer model output: an application to modeling air quality. Biometrics 68, 837-848; 2) Berrocal, V., Gelfand, A. E. and Holland, D. M. (2010). A bivariate space-time downscaler under space and time misalignment. The Annals of Applied Statistics 4, 1942-1975; and 3) Berrocal, V., Gelfand, A. E., and Holland, D. M. (2010). A spatio-temporal downscaler for output from numerical models. J. of Agricultural, Biological,and Environmental Statistics 15, 176-197) is used to provide daily, predictive PM2.5 (daily average) and O3 (daily 8-hr maximum) surfaces for 2012, 2013, and 2014. Summer (O3) and annual (PM2.5) means calculated and published. The downscaling fusion model uses both air quality monitoring data from the National Air Monitoring Stations/State and Local Air Monitoring Stations (NAMS/SLAMS) and numerical output from the Models-3/Community Multiscale Air Quality (CMAQ). Currently, predictions at the US census tract centroid locations within the 12 km CMAQ domain are archived. Predictions at the CMAQ grid cell centroids, or any desired set of locations could be provided if needed. Predictions are not provided on December 31st of any year because complete daily CMAQ output are not available on those days after conversion of GMT to local eastern time.

Tags: 020:072, air, united states
Organization: U.S. Environmental Protection Agency