USGS is assessing the feasibility of map projections and grid systems for lunar surface operations. We propose developing a new Lunar Transverse Mercator (LTM), the Lunar Polar Stereographic (LPS), and the Lunar Grid Reference Systems (LGRS). We have also designed additional grids designed to NASA requirements for astronaut navigation, referred to as LGRS in Artemis Condensed Coordinates (ACC), but this is not released here. LTM, LPS, and LGRS are similar in design and use to the Universal Transverse Mercator (UTM), Universal Polar Stereographic (LPS), and Military Grid Reference System (MGRS), but adhere to NASA requirements. LGRS ACC format is similar in design and structure to historic Army Mapping Service Apollo orthotopophoto charts for navigation. The Lunar Transverse Mercator (LTM) projection system is a globalized set of lunar map projections that divides the Moon into zones to provide a uniform coordinate system for accurate spatial representation. It uses a transverse Mercator projection, which maps the Moon into 45 transverse Mercator strips, each 8°, longitude, wide. These transverse Mercator strips are subdivided at the lunar equator for a total of 90 zones. Forty-five in the northern hemisphere and forty-five in the south. LTM specifies a topocentric, rectangular, coordinate system (easting and northing coordinates) for spatial referencing. This projection is commonly used in GIS and surveying for its ability to represent large areas with high positional accuracy while maintaining consistent scale. The Lunar Polar Stereographic (LPS) projection system contains projection specifications for the Moon’s polar regions. It uses a polar stereographic projection, which maps the polar regions onto an azimuthal plane. The LPS system contains 2 zones, each zone is located at the northern and southern poles and is referred to as the LPS northern or LPS southern zone. LPS, like is equatorial counterpart LTM, specifies a topocentric, rectangular, coordinate system (easting and northing coordinates) for spatial referencing. This projection is commonly used in GIS and surveying for its ability to represent large polar areas with high positional accuracy, while maintaining consistent scale across the map region. LGRS is a globalized grid system for lunar navigation supported by the LTM and LPS projections. LGRS provides an alphanumeric grid coordinate structure for both the LTM and LPS systems. This labeling structure is utilized in a similar manner to MGRS. LGRS defines a global area grid based on latitude and longitude and a 25×25 km grid based on LTM and LPS coordinate values. Two implementations of LGRS are used as polar areas require a LPS projection and equatorial areas a transverse Mercator. We describe the difference in the techniques and methods report associated with this data release. Request McClernan et. al. (in-press) for more information. ACC is a method of simplifying LGRS coordinates and is similar in use to the Army Mapping Service Apollo orthotopophoto charts for navigation. These data will be released at a later date. Two versions of the shape files are provided in this data release, PCRS and Display only. See LTM_LPS_LGRS_Shapefiles.zip file. PCRS are limited to a single zone and are projected in either LTM or LPS with topocentric coordinates formatted in Eastings and Northings. Display only shapefiles are formatted in lunar planetocentric latitude and longitude, a Mercator or Equirectangular projection is best for these grids. A description of each grid is provided below: Equatorial (Display Only) Grids: Lunar Transverse Mercator (LTM) Grids: LTM zone borders for each LTM zone Merged LTM zone borders Lunar Polar Stereographic (LPS) Grids: North LPS zone border South LPS zone border Lunar Grid Reference System (LGRS) Grids: Global Areas for North and South LPS zones Merged Global Areas (8°×8° and 8°×10° extended area) for all LTM zones Merged 25km grid for all LTM zones PCRS Shapefiles:` Lunar Transverse Mercator (LTM) Grids: LTM zone borders for each LTM zone Lunar Polar Stereographic (LPS) Grids: North LPS zone border South LPS zone border Lunar Grid Reference System (LGRS) Grids: Global Areas for North and South LPS zones 25km Gird for North and South LPS zones Global Areas (8°×8° and 8°×10° extended area) for each LTM zone 25km grid for each LTM zone The rasters in this data release detail the linear distortions associated with the LTM and LPS system projections. For these products, we utilize the same definitions of distortion as the U.S. State Plane Coordinate System. Scale Factor, k - The scale factor is a ratio that communicates the difference in distances when measured on a map and the distance reported on the reference surface. Symbolically this is the ratio between the maps grid distance and distance on the lunar reference sphere. This value can be precisely calculated and is provided in their defining publication. See Snyder (1987) for derivation of the LPS scale factor. This scale factor is unitless and typically increases from the central scale factor k_0, a projection-defining parameter. For each LPS projection. Request McClernan et. al., (in-press) for more information. Scale Error, (k-1) - Scale-Error, is simply the scale factor differenced from 1. Is a unitless positive or negative value from 0 that is used to express the scale factor’s impact on position values on a map. Distance on the reference surface are expended when (k-1) is positive and contracted when (k-1) is negative. Height Factor, h_F - The Height Factor is used to correct for the difference in distance caused between the lunar surface curvature expressed at different elevations. It is expressed as a ratio between the radius of the lunar reference sphere and elevations measured from the center of the reference sphere. For this work, we utilized a radial distance of 1,737,400 m as recommended by the IAU working group of Rotational Elements (Archinal et. al., 2008). For this calculation, height factor values were derived from a LOLA DEM 118 m v1, Digital Elevation Model (LOLA Science Team, 2021). Combined Factor, C_F – The combined factor is utilized to “Scale-To-Ground” and is used to adjust the distance expressed on the map surface and convert to the position on the actual ground surface. This value is the product of the map scale factor and the height factor, ensuring the positioning measurements can be correctly placed on a map and on the ground. The combined factor is similar to linear distortion in that it is evaluated at the ground, but, as discussed in the next section, differs numerically. Often C_F is scrutinized for map projection optimization. Linear distortion, δ - In keeping with the design definitions of SPCS2022 (Dennis 2023), we refer to scale error when discussing the lunar reference sphere and linear distortion, δ, when discussing the topographic surface. Linear distortion is calculated using C_F simply by subtracting 1. Distances are expended on the topographic surface when δ is positive and compressed when δ is negative. The relevant files associated with the expressed LTM distortion are as follows. The scale factor for the 90 LTM projections: LUNAR_LTM_GLOBAL_PLOT_HEMISPHERES_distortion_K_grid_scale_factor.tif Height Factor for the LTM portion of the Moon: LUNAR_LTM_GLOBAL_PLOT_HEMISPHERES_distortion_EF_elevation_factor.tif Combined Factor in LTM portion of the Moon LUNAR_LTM_GLOBAL_PLOT_HEMISPHERES_distortion_CF_combined_factor.tif The relevant files associated with the expressed LPS distortion are as follows. Lunar North Pole The scale factor for the northern LPS zone: LUNAR_LGRS_NP_PLOT_LPS_K_grid_scale_factor.tif Height Factor for the north pole of the Moon: LUNAR_LGRS_NP_PLOT_LPS_EF_elevation_factor.tif Combined Factor for northern LPS zone: LUNAR_LGRS_NP_PLOT_LPS_CF_combined_factor.tif Lunar South Pole Scale factor for the northern LPS zone: LUNAR_LGRS_SP_PLOT_LPS_K_grid_scale_factor.tif Height Factor for the south pole of the Moon: LUNAR_LGRS_SP_PLOT_LPS_EF_elevation_factor.tif Combined Factor for northern LPS zone: LUNAR_LGRS_SP_PLOT_LPS_CF_combined_factor.tif For GIS utilization of grid shapefiles projected in Lunar Latitude and Longitude, referred to as “Display Only”, please utilize a registered lunar geographic coordinate system (GCS) such as IAU_2015:30100 or ESRI:104903. LTM, LPS, and LGRS PCRS shapefiles utilize either a custom transverse Mercator or polar Stereographic projection. For PCRS grids the LTM and LPS projections are recommended for all LTM, LPS, and LGRS grid sizes. See McClernan et. al. (in-press) for such projections. Raster data was calculated using planetocentric latitude and longitude. A LTM and LPS projection or a registered lunar GCS may be utilized to display this data. Note: All data, shapefiles and rasters, require a specific projection and datum. The projection is recommended as LTM and LPS or, when needed, IAU_2015:30100 or ESRI:104903. The datum utilized must be the Jet Propulsion Laboratory (JPL) Development Ephemeris (DE) 421 in the Mean Earth (ME) Principal Axis Orientation as recommended by the International Astronomy Union (IAU) (Archinal et. al., 2008).

This data set provides a grid of quads and projection information to be used for rover operations and the informal geographic naming convention for the regional geography of Oxia Planum. Both subject to update prior to the landed mission.Contents This data set contains 4 shapefiles and 1 zipped folder.OxiaPlanum_GeographicFeatures_2021_08_26. Point shapefile with the names of geographic features last updated at the date indicatedOxiaPlanum_GeographicRegions_2021_08_26. Polygon shapefile with the outlines of geographic regions fitted to the master quad grid and last updated at the date indicated.OxiaPlanum_QuadGrid_1km. Polygon shapefile of 1km quad that will be used for ExoMars rover missionOxiaPlanum_Origin_clong_335_45E_18_20N. The center point of the Oxia Planum as defined by the Rover Operations and Control center and origin point used for the Quad gridCRS_PRJ_Equirectangular_OxiaPlanum_Mars2000.zip. Zip folder containing the projection information use for all the data associated with this study. These are saved in the ESRI projection (.prj) and well know text formal (.wkt)Guide to individual filesFile name (example) Description OxiaPlanum_QuadGrid_1km.cpg Text display information OxiaPlanum_QuadGrid_1km.dbf Database file OxiaPlanum_QuadGrid_1km.prj Projection information OxiaPlanum_QuadGrid_1km.sbx Spatial index file OxiaPlanum_QuadGrid_1km.shp Shape file data

The Climate Resilience Information System (CRIS) provides data and tools for developers of climate services. This layer has projections of VAR in decadal increments from 1950 to 2100 and for three Shared Socioeconomic Pathways (SSPs). The variables included are:Annual number of days with a maximum temperature greater than or equal to 85°F Annual number of days with a maximum temperature greater than or equal to 86°F Annual number of days with a maximum temperature greater than or equal to 90°F Annual number of days with a maximum temperature greater than or equal to 95°F Annual number of days with a maximum temperature greater than or equal to 100°F Annual number of days with a maximum temperature greater than or equal to 105°F Annual number of days with a maximum temperature greater than or equal to 110°F Annual number of days with a maximum temperature greater than or equal to 115°F This layer uses data from the LOCA2 downscaled climate models for the Contiguous United States. Further processing by the NOAA Technical Support Unit at CICS-NC and Esri are explained below.For each time and SSP, there are minimum, maximum, and mean values for the defined respective geography: counties, tribal areas, HUC-8 watersheds. The process for deriving these summaries is available in Understanding CRIS Data. The combination of time and geography is available for a weighted ensemble of 16 climate projections. More details on the models included in the ensemble and the weighting methodologies can be found in CRIS Data Preparation. Other climate variables are available from the CRIS website’s Data Gallery page or can be accessed in the table below. Additional geographies, including Alaska, Hawai’i and Puerto Rico will be made available in the future.GeographiesThis layer provides projected values for three geographies: county, tribal area, and HUC-8 watersheds.County: based on the U.S. Census TIGER/Line 2022 distribution. Tribal areas: based on the U.S. Census American Indian/Alaska Native/Native Hawaiian Area dataset 2022 distribution. This dataset includes federal- and state-recognized statistical areas.HUC-8 watershed: based on the USGS Washed Boundary Dataset, part of the National Hydrography Database Plus High Resolution. Time RangesProjected climate threshold values (e.g. Days Over 90°F) were calculated for each year from 2005 to 2100. Additionally, values are available for the modeled history runs from 1951 - 2005. The modeled history and future projections have been merged into a single time series and averaged by decade.Climate ScenariosClimate models use future scenarios of greenhouse gas concentrations and human activities to project overall change. These different scenarios are called the Shared Socioeconomic Pathways (SSPs). Three different SSPs are available here: 2-4.5, 3-7.0, and 5-8.5 (STAR does not have SSP3-7.0). The number before the dash represents a societal behavior scenario. The number after the dash indicates the amount of radiative forcing (watts per meter square) associated with the greenhouse gas concentration scenario in the year 2100 (higher forcing = greater warming). It is unclear which scenario will be the most likely, but SSP 2-4.5 currently aligns with the international targets of the COP-26 agreement. SSP3-7.0 may be the most likely scenario based on current emission trends. SSP5-8.5 acts as a cautionary tale, providing a worst-case scenario if reductions in greenhouse gasses are not undertaken. Data ExportExporting this data into shapefiles, geodatabases, GeoJSON, etc is enabled.

The Climate Resilience Information System (CRIS) provides data and tools for developers of climate services. This layer has projections of VAR in decadal increments from 1950 to 2100 and for three Shared Socioeconomic Pathways (SSPs). The variables included are:Cooling degree (F) days Heating degree (F) days This layer uses data from the LOCA2 and STAR-ESDM downscaled climate models for the Contiguous United States. Further processing by the NOAA Technical Support Unit at CICS-NC and Esri are explained below.For each time and SSP, there are minimum, maximum, and mean values for the defined respective geography: counties, tribal areas, HUC-8 watersheds. The process for deriving these summaries is available in Understanding CRIS Data. The combination of time and geography is available for a weighted ensemble of 16 climate projections. More details on the models included in the ensemble and the weighting methodologies can be found in CRIS Data Preparation. Other climate variables are available from the CRIS website’s Data Gallery page or can be accessed in the table below. Additional geographies, including Alaska, Hawai’i and Puerto Rico will be made available in the future.GeographiesThis layer provides projected values for three geographies: county, tribal area, and HUC-8 watersheds.County: based on the U.S. Census TIGER/Line 2022 distribution. Tribal areas: based on the U.S. Census American Indian/Alaska Native/Native Hawaiian Area dataset 2022 distribution. This dataset includes federal- and state-recognized statistical areas.HUC-8 watershed: based on the USGS Washed Boundary Dataset, part of the National Hydrography Database Plus High Resolution. Time RangesProjected climate threshold values (e.g. Days Over 90°F) were calculated for each year from 2005 to 2100. Additionally, values are available for the modeled history runs from 1951 - 2005. The modeled history and future projections have been merged into a single time series and averaged by decade.Climate ScenariosClimate models use future scenarios of greenhouse gas concentrations and human activities to project overall change. These different scenarios are called the Shared Socioeconomic Pathways (SSPs). Three different SSPs are available here: 2-4.5, 3-7.0, and 5-8.5 (STAR does not have SSP3-7.0). The number before the dash represents a societal behavior scenario. The number after the dash indicates the amount of radiative forcing (watts per meter square) associated with the greenhouse gas concentration scenario in the year 2100 (higher forcing = greater warming). It is unclear which scenario will be the most likely, but SSP 2-4.5 currently aligns with the international targets of the COP-26 agreement. SSP3-7.0 may be the most likely scenario based on current emission trends. SSP5-8.5 acts as a cautionary tale, providing a worst-case scenario if reductions in greenhouse gasses are not undertaken. Data ExportExporting this data into shapefiles, geodatabases, GeoJSON, etc is enabled.

This data is a mosaic of CTX DEM and ORI’s covering the ExoMars rover landing site in Oxia Planum. This data is a basemap for Oxia Planum and will act as a georeferencing base layer for future High resolution datasets of the rover landing site.ContentsThis data set contains 4 directories:03_a Sets of elevation contours at 100 m and 25 m spacing made from the DEM and smoothed for use in map publications.03_b Mosaic of orthorectified CTX images that accompany the DEM. These data are provided in an equirectangular projection centered at 335.45°E 03_c Hillshade model of the CTX DEM mosaic. These data are provided to help assess the variability and quality of the DEM. These data are provided in an equirectangular projection centered at 335.45°E03_d CTX DEM mosaic. These data are provided in an equirectangular projection centered at 335.45°EGuide to individual files03_a_CTX_DEM_contoursNaming convention: CTX_OXIA_DEM = data from which the contours where created, _cont = contour data, _m = vertical separation of the contours (25 or 100.)File name (example) Description CTX_OXIA_DEM_cont_100m.cpg CTX_OXIA_DEM_cont_100m.dbf CTX_OXIA_DEM_cont_100m.prj Projection information CTX_OXIA_DEM_cont_100m.sbx CTX_OXIA_DEM_cont_100m.shp <- Shape file data Open this data in GiS with the other supporting files in the same directoryCTX_OXIA_DEM_cont_100m.shp.xml Geoprocessing history CTX_OXIA_DEM_cont_100m.shx 03_b_CTX_ORINaming convention: CTX = Instrument, OXIA = Location, ORI = Orthorectified image, 6m = pixel sizeFile name Description CTX_OXIA_ORI_6m.tfw World file <- Open this data in GiS with the other supporting files in the same directoryCTX_OXIA_ORI_6m.tif Image data CTX_OXIA_ORI_6m.tif.aux.xml Auxiliary symbology statistics CTX_OXIA_ORI_6m.tif.ovr Image overviews CTX_OXIA_ORI_6m.tif.xml Geoprocessing history These data are provided with the following projection: Equirectangular_Mars_Oxia_Planum, Projections = Equidistant_Cylindrical, Datum = D_Mars_2000 Spheroid, Central meridian = 335.4503_c_CTX_DEM_hsNaming convention: CTX = Instrument, OXIA = Location, DEM = Digital Elevation Model, 20m = Pixel Size, _hs = hill shade model (sun potion 315°, azimuth 45°)File name Description CTX_OXIA_DEM_20m_hs.tfw World file <- Open this data in GiS with the other supporting files in the same directoryCTX_OXIA_DEM_20m_hs.tif Image data CTX_OXIA_DEM_20m_hs.tif.aux.xml Auxiliary symbology statistics CTX_OXIA_DEM_20m_hs.ovr Image overviews CTX_OXIA_ DEM_20m_hs.tif.xml Geoprocessing history 03_d_CTX_DEMNaming convention: CTX = Instrument, OXIA = Location, DEM = Digital Elevation Model, 20m = Pixel SizeFile name Description CTX_OXIA_DEM_20m.tfw World file <- Open this data in GiS with the other supporting files in the same directoryCTX_OXIA_DEM_20m.tif Image data CTX_OXIA_DEM_20m.tif.aux.xml Auxiliary symbology statistics CTX_OXIA_DEM_20m.ovr Image overviews These data are provided with the following projection: Equirectangular_Mars_Oxia_Planum, Projections = Equidistant_Cylindrical, Datum = D_Mars_2000 Spheroid, Central meridian = 335.45Digital elevation models Digital elevation models (DEMs) were produced from CTX stereo images using the USGS Integrated Software for Imagers and Spectrometers (ISIS) software and the BAE photogrammetric package SOCET SET according to the method of Kirk et al. (2008). We selected 6 CTX image pairs to maximise coverage of the canyon. Tie points were automatically populated in SOCET SET between each image pair. In a departure from previous methods, we ran bundle adjustments on adjacent stereo pairs, removing erroneous tie points until the remaining points had an RMS pixel matching error of ≤ 0.6 pixels. This approach resulted in improved coregistration between stereo pairs, and minimal topographic artefacts across stereo pair boundaries. Each resultant DEM was tied vertically to Mars Orbital Laser Altimeter (MOLA; Zuber et al., 1992) topography and exported with a horizontal post spacing of 20 m/pixel. We then exported orthorectified images from SOCET SET at a resolution of 6 m/pixel. The orthorectified images (ORI) and DEMs were then post-processed in ISIS, mosaicked in the software ENvironment for Visualising Images (ENVI), provided by Harris Geospatial, before manual georeferencing in ArcGIS. Finally, the georeferenced image mosaic was blended in Adobe Photoshop to remove seamlines using the Avenza Geographic Imager extension, which retains geospatial information in the blended product.The output from SocetSet® are 18 – 20 m/pix DEM resolving topography of ~50 – 60 m features and 12 orthorectified CTX images at 6 m/pix. The Expected Vertical Precision (EVP) in each CTX DEM can be estimated based on viewing geometry and pixel scale (Randolph L. Kirk et al., 2003, 2008) e.g. EVP = Δp IFOV / (parallax/height). Where: Δp is the RMS stereo matching error in pixel units, assumed to be 0.2 pixels (Cook et al., 1996) and confirmed with matching software for several other planetary image data sets (Howington-Kraus et al., 2002; R. L. Kirk et al., 1999). The pixel matching error is influenced by signal-to-noise ratio, scene contrast and differences in illumination between the images. Pattern noise can also be introduced by the automatic terrain extraction algorithm, especially in areas of low correlation. These can be identified as patches of ‘triangles’ in the hillshade model (e.g., smooth, low contrast slopes and along shadows). IFOV is the instantaneous field of view of the image (pixel size in metres). If the paired images have different IFOV the RMS values is used e.g. IFOV = √(pixel scale image 1 + pixel scale image 2). The parallax/height ratio, calculated from the three-dimensional intersection geometry, reduces to tan(e) for an image with emission angle ‘e’ paired with a nadir image, e.g., parallax/height = tan(e) where e = |emission angle 1 − emission angle 2|.GeoreferencingMars Express High Resolution Stereo Camera (HRSC; Gwinner et al., 2016) MC11- mosaic (Kersten et al., 2018) has been used as the base control mosaic (tile HMC_11W24_co5ps.tif http://hrscteam.dlr.de/HMC30/).. This data is controlled to the Mars Orbital Laser Altimeter (MOLA; Smith et al., 2001) data the most accurate elevation data for Mars.Registration of the CTX DEM mosaic to the HRSC mosaic used manual tie points between the CTX ORI and HRSC mosaic and applying these tie points to the DEM mosaic. Manual tie points were used because automatic methods gave unsatisfactory results. The CTX mosaic data was rectified using the spline transformation. which optimizes for local accuracy but not global accuracy (Esri, 2020). This method provided good results for images with a range of viewing angles and accounts well for local adjustments needed for abrupt elevation changes.Topographic contoursTopographic contours were created at 25 m intervals from a CTX DEM down sampled to 100 m/pix, and contours shorter than 1500 m were removed and the lines smoothed using the PAEK algorithm at a tolerance of 200 m (USGS & MRCTR GIS Lab, 2018).

The Climate Resilience Information System (CRIS) provides data and tools for developers of climate services. This layer has projections of VAR in decadal increments from 1950 to 2100 and for three Shared Socioeconomic Pathways (SSPs). The variables included are:Annual number of days with a minimum temperature less than or equal to 0°F Annual number of days with a minimum temperature less than or equal to 28°F Annual number of days with a minimum temperature less than or equal to 32°F Annual number of days with a maximum temperature less than or equal to 32°F This layer uses data from the LOCA2 and STAR-ESDM downscaled climate models for the Contiguous United States. Further processing by the NOAA Technical Support Unit at CICS-NC and Esri are explained below.For each time and SSP, there are minimum, maximum, and mean values for the defined respective geography: counties, tribal areas, HUC-8 watersheds. The process for deriving these summaries is available in Understanding CRIS Data. The combination of time and geography is available for a weighted ensemble of 16 climate projections. More details on the models included in the ensemble and the weighting methodologies can be found in CRIS Data Preparation. Other climate variables are available from the CRIS website’s Data Gallery page or can be accessed in the table below. Additional geographies, including Alaska, Hawai’i and Puerto Rico will be made available in the future.GeographiesThis layer provides projected values for three geographies: county, tribal area, and HUC-8 watersheds.County: based on the U.S. Census TIGER/Line 2022 distribution. Tribal areas: based on the U.S. Census American Indian/Alaska Native/Native Hawaiian Area dataset 2022 distribution. This dataset includes federal- and state-recognized statistical areas.HUC-8 watershed: based on the USGS Washed Boundary Dataset, part of the National Hydrography Database Plus High Resolution. Time RangesProjected climate threshold values (e.g. Days Over 90°F) were calculated for each year from 2005 to 2100. Additionally, values are available for the modeled history runs from 1951 - 2005. The modeled history and future projections have been merged into a single time series and averaged by decade.Climate ScenariosClimate models use future scenarios of greenhouse gas concentrations and human activities to project overall change. These different scenarios are called the Shared Socioeconomic Pathways (SSPs). Three different SSPs are available here: 2-4.5, 3-7.0, and 5-8.5 (STAR does not have SSP3-7.0). The number before the dash represents a societal behavior scenario. The number after the dash indicates the amount of radiative forcing (watts per meter square) associated with the greenhouse gas concentration scenario in the year 2100 (higher forcing = greater warming). It is unclear which scenario will be the most likely, but SSP 2-4.5 currently aligns with the international targets of the COP-26 agreement. SSP3-7.0 may be the most likely scenario based on current emission trends. SSP5-8.5 acts as a cautionary tale, providing a worst-case scenario if reductions in greenhouse gasses are not undertaken. Data ExportExporting this data into shapefiles, geodatabases, GeoJSON, etc is enabled.

The Climate Resilience Information System (CRIS) provides data and tools for developers of climate services. This layer has projections of VAR in decadal increments from 1950 to 2100 and for three Shared Socioeconomic Pathways (SSPs). The variables included are:Annual number of days with total precipitation greater than or equal to 1 inch Annual number of days with total precipitation greater than or equal to 2 inches Annual number of days with total precipitation greater than or equal to 3 inches Annual number of days with total precipitation greater than or equal to 4 inches Annual number of days with precipitation exceeding the 90th percentile for non-zero precipitation days Annual number of days with precipitation exceeding the 95th percentile for non-zero precipitation days Annual number of days with precipitation exceeding the 99th percentile for non-zero precipitation days This layer uses data from the LOCA2 and STAR-ESDM downscaled climate models for the Contiguous United States. Further processing by the NOAA Technical Support Unit at CICS-NC and Esri are explained below.For each time and SSP, there are minimum, maximum, and mean values for the defined respective geography: counties, tribal areas, HUC-8 watersheds. The process for deriving these summaries is available in Understanding CRIS Data. The combination of time and geography is available for a weighted ensemble of 16 climate projections. More details on the models included in the ensemble and the weighting methodologies can be found in CRIS Data Preparation. Other climate variables are available from the CRIS website’s Data Gallery page or can be accessed in the table below. Additional geographies, including Alaska, Hawai’i and Puerto Rico will be made available in the future.GeographiesThis layer provides projected values for three geographies: county, tribal area, and HUC-8 watersheds.County: based on the U.S. Census TIGER/Line 2022 distribution. Tribal areas: based on the U.S. Census American Indian/Alaska Native/Native Hawaiian Area dataset 2022 distribution. This dataset includes federal- and state-recognized statistical areas.HUC-8 watershed: based on the USGS Washed Boundary Dataset, part of the National Hydrography Database Plus High Resolution. Time RangesProjected climate threshold values (e.g. Days Over 90°F) were calculated for each year from 2005 to 2100. Additionally, values are available for the modeled history runs from 1951 - 2005. The modeled history and future projections have been merged into a single time series and averaged by decade.Climate ScenariosClimate models use future scenarios of greenhouse gas concentrations and human activities to project overall change. These different scenarios are called the Shared Socioeconomic Pathways (SSPs). Three different SSPs are available here: 2-4.5, 3-7.0, and 5-8.5 (STAR does not have SSP3-7.0). The number before the dash represents a societal behavior scenario. The number after the dash indicates the amount of radiative forcing (watts per meter square) associated with the greenhouse gas concentration scenario in the year 2100 (higher forcing = greater warming). It is unclear which scenario will be the most likely, but SSP 2-4.5 currently aligns with the international targets of the COP-26 agreement. SSP3-7.0 may be the most likely scenario based on current emission trends. SSP5-8.5 acts as a cautionary tale, providing a worst-case scenario if reductions in greenhouse gasses are not undertaken. Data ExportExporting this data into shapefiles, geodatabases, GeoJSON, etc is enabled.

The Climate Resilience Information System (CRIS) provides data and tools for developers of climate services. This layer has projections of VAR in decadal increments from 1950 to 2100 and for three Shared Socioeconomic Pathways (SSPs). The variables included are:Annual number of days with a maximum temperature greater than or equal to 85°F Annual number of days with a maximum temperature greater than or equal to 86°F Annual number of days with a maximum temperature greater than or equal to 90°F Annual number of days with a maximum temperature greater than or equal to 95°F Annual number of days with a maximum temperature greater than or equal to 100°F Annual number of days with a maximum temperature greater than or equal to 105°F Annual number of days with a maximum temperature greater than or equal to 110°F Annual number of days with a maximum temperature greater than or equal to 115°F This layer uses data from the LOCA2 downscaled climate models for the Contiguous United States. Further processing by the NOAA Technical Support Unit at CICS-NC and Esri are explained below.For each time and SSP, there are minimum, maximum, and mean values for the defined respective geography: counties, tribal areas, HUC-8 watersheds. The process for deriving these summaries is available in Understanding CRIS Data. The combination of time and geography is available for a weighted ensemble of 16 climate projections. More details on the models included in the ensemble and the weighting methodologies can be found in CRIS Data Preparation. Other climate variables are available from the CRIS website’s Data Gallery page or can be accessed in the table below. Additional geographies, including Alaska, Hawai’i and Puerto Rico will be made available in the future.GeographiesThis layer provides projected values for three geographies: county, tribal area, and HUC-8 watersheds.County: based on the U.S. Census TIGER/Line 2022 distribution. Tribal areas: based on the U.S. Census American Indian/Alaska Native/Native Hawaiian Area dataset 2022 distribution. This dataset includes federal- and state-recognized statistical areas.HUC-8 watershed: based on the USGS Washed Boundary Dataset, part of the National Hydrography Database Plus High Resolution. Time RangesProjected climate threshold values (e.g. Days Over 90°F) were calculated for each year from 2005 to 2100. Additionally, values are available for the modeled history runs from 1951 - 2005. The modeled history and future projections have been merged into a single time series and averaged by decade.Climate ScenariosClimate models use future scenarios of greenhouse gas concentrations and human activities to project overall change. These different scenarios are called the Shared Socioeconomic Pathways (SSPs). Three different SSPs are available here: 2-4.5, 3-7.0, and 5-8.5 (STAR does not have SSP3-7.0). The number before the dash represents a societal behavior scenario. The number after the dash indicates the amount of radiative forcing (watts per meter square) associated with the greenhouse gas concentration scenario in the year 2100 (higher forcing = greater warming). It is unclear which scenario will be the most likely, but SSP 2-4.5 currently aligns with the international targets of the COP-26 agreement. SSP3-7.0 may be the most likely scenario based on current emission trends. SSP5-8.5 acts as a cautionary tale, providing a worst-case scenario if reductions in greenhouse gasses are not undertaken. Data ExportExporting this data into shapefiles, geodatabases, GeoJSON, etc is enabled.

The Climate Resilience Information System (CRIS) provides data and tools for developers of climate services. This layer has projections of VAR in decadal increments from 1950 to 2100 and for three Shared Socioeconomic Pathways (SSPs). The variables included are:Annual total precipitation (inches) Annual highest precipitation total for a single day (inches) Annual highest precipitation total over a 5-day period (inches) Annual highest precipitation total over a 10-day period (inches) Annual total precipitation for all days exceeding the 90th percentile (inches) Annual total precipitation for all days exceeding the 95th percentile (inches) Annual total precipitation for all days exceeding the 99th percentile (inches) This layer uses data from the LOCA2 downscaled climate models for the Contiguous United States. Further processing by the NOAA Technical Support Unit at CICS-NC and Esri are explained below.For each time and SSP, there are minimum, maximum, and mean values for the defined respective geography: counties, tribal areas, HUC-8 watersheds. The process for deriving these summaries is available in Understanding CRIS Data. The combination of time and geography is available for a weighted ensemble of 16 climate projections. More details on the models included in the ensemble and the weighting methodologies can be found in CRIS Data Preparation. Other climate variables are available from the CRIS website’s Data Gallery page or can be accessed in the table below. Additional geographies, including Alaska, Hawai’i and Puerto Rico will be made available in the future.GeographiesThis layer provides projected values for three geographies: county, tribal area, and HUC-8 watersheds.County: based on the U.S. Census TIGER/Line 2022 distribution. Tribal areas: based on the U.S. Census American Indian/Alaska Native/Native Hawaiian Area dataset 2022 distribution. This dataset includes federal- and state-recognized statistical areas.HUC-8 watershed: based on the USGS Washed Boundary Dataset, part of the National Hydrography Database Plus High Resolution. Time RangesProjected climate threshold values (e.g. Days Over 90°F) were calculated for each year from 2005 to 2100. Additionally, values are available for the modeled history runs from 1951 - 2005. The modeled history and future projections have been merged into a single time series and averaged by decade.Climate ScenariosClimate models use future scenarios of greenhouse gas concentrations and human activities to project overall change. These different scenarios are called the Shared Socioeconomic Pathways (SSPs). Three different SSPs are available here: 2-4.5, 3-7.0, and 5-8.5 (STAR does not have SSP3-7.0). The number before the dash represents a societal behavior scenario. The number after the dash indicates the amount of radiative forcing (watts per meter square) associated with the greenhouse gas concentration scenario in the year 2100 (higher forcing = greater warming). It is unclear which scenario will be the most likely, but SSP 2-4.5 currently aligns with the international targets of the COP-26 agreement. SSP3-7.0 may be the most likely scenario based on current emission trends. SSP5-8.5 acts as a cautionary tale, providing a worst-case scenario if reductions in greenhouse gasses are not undertaken. Data ExportExporting this data into shapefiles, geodatabases, GeoJSON, etc is enabled.

The EPA/ORD/CEMM-contributed dataset consisted of hourly CMAQ output for all model species from a 2017 simulation over the northern hemisphere along a boundary curtain of a 36 km modeling domain specified over the CONUS. The horizontal and vertical extent of the 36 km modeling domain was specified by the external collaborator and was defined by 524 boundary grid cells and 34 vertical layers. The number of output species from the 2017 hemispheric CMAQ simulation was 191 through to September 23, 2017 and 213 starting September 24, 2017. The EPA/OAR/OAQPS-contributed dataset consistent of hourly gridded CMAQ output for surface ozone concentrations from four model simulations for the year 2016. Two of these simulations were performed over the northern hemisphere at a horizontal resolution of 108 km and the other two simulations were performed over the CONUS at a horizontal resolution of 12 km. This dataset is not publicly accessible because: The size of the data provided to the external researchers (>1TB) exceeds ScienceHub limits. It can be accessed through the following means: Data can be requested by contacting hogrefe.christian@epa.gov (EPA/ORD/CEMM-contributed dataset) and henderson.barron@epa.gov (EPA/OAR/OAQPS-contributed dataset) and providing an external hard to which the data can then be copied by staff at the National Computing Center. The model simulations are stored on the /asm archival system accessible through the atmos high-performance computing (HPC) system. Due to data management policies, files on /asm are subject to expiry depending on the template of the project. Files not requested for extension after the expiry date are deleted permanently from the system. Location of EPA/ORD/CEMM-provided CMAQ model output data on asm: • /asm/grc/NRT_WRF_CMAQ/model_outputs/nhemi108/cctm.conc • /asm/MOD3EVAL/css/NRT/data/gatech/bc • /asm/MOD3EVAL/css/NRT/data/gatech/scripts • /asm/MOD3EVAL/css/NRT/data/gatech/metbdy3d Location of EPA/OAR/OAQPS-provided CMAQ model output data on asm: • /asm/ROMO/global/CMAQv5.2/2016fe_hemi_cb6_16jh/108km/output • /asm/ROMO/global/CMAQv5.2.1/2016fe_hemi_cb6_16jh/108km/ZUSA/output • /asm/ROMO/2016platform/CMAQv521/2016fe_cb6r3_ae6nvpoa_16j/12US2/output • /asm/ROMO/2016platform/CMAQv521/2016fe_zusa_cb6r3_ae6nvpoa_16j/12US2/output. Format: The CMAQ model output datasets used for the analysis presented in this manuscript and documented here were provided by scientists in EPA/ORD/CEMM and EPA/OAR/OAQPS. The EPA/ORD/CEMM-contributed dataset consisted of hourly CMAQ output for all model species from a 2017 simulation over the northern hemisphere along a boundary curtain of a 36 km modeling domain specified over the CONUS. The horizontal and vertical extent of the 36 km modeling domain was specified by the external collaborator and was defined by 524 boundary grid cells and 34 vertical layers. The number of output species from the 2017 hemispheric CMAQ simulation was 191 through to September 23, 2017 and 213 starting September 24, 2017. The EPA/OAR/OAQPS-contributed dataset consistent of hourly gridded CMAQ output for surface ozone concentrations from four model simulations for the year 2016. Two of these simulations were performed over the northern hemisphere at a horizontal resolution of 108 km and the other two simulations were performed over the CONUS at a horizontal resolution of 12 km. The data files with the CMAQ model output provided to the external researchers use the ioapi/netcdf format. Documentation of this format, including definitions of the geographical projection attributes contained in the file headers, are available at https://www.cmascenter.org/ioapi/documentation/all_versions/html. This dataset is associated with the following publication: Skipper, T.N., Y. Hu, M.T. Odman, B. Henderson, C. Hogrefe, R. Mathur, and A. Russell. EST Publication: Estimating US background ozone levels using data fusion. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 55(8): 4504-4512, (2021).

Population Density : This vector dataset provides the population density by commune in Cambodia, as provided by Cambodian Demographic Census 2008 (Ministry of Planning, National Institute of Statistics). Dataset were provided to Open Development Cambodia (ODC) in vector format by Save Cambodia's Wildlife's Atlas Working Group.Urban Density in Cambodia (2011) : This vector dataset provides the urban density in Cambodia, as given by the United Nations Population Fund (UNFPA). Dataset were provided to Open Development Cambodia (ODC) by Save Cambodia's Wildlife's Atlas Working Group.Population Projections for 2030 in Cambodia (2010) : This dataset provides projected population of 2030, projected annual growth rate in each province in Cambodia, given by National Institute of Statistics and the United Nations. Data were provided to Open Development Cambodia (ODC) in vector format by Save Cambodia's Wildlife's Atlas Working Group.River networks of Cambodia : Vector polyline data of river networks in Cambodia. Attributes include: name of river, name of basin, name of sub-basin, Strahler number.Canals in Cambodia (2008) : This dataset is included geographical locations of canals and types of canal such as earthen, levee and masonry. The data is released by Department of Geography of Ministry of Land Management, Urban Planning, and Construction of Cambodia, and then it is contributed by Office for the Coordination of Humanitarian Affairs (OCHA) and shared on Humanitarian Data Exchange (HDX). ODC's map and data team has collected the data from HDX website in Shapefile format and re-published it on ODC's website.Special economic zone in Cambodia (2006-2019) : This dataset describes the information of special economic zone (SEZ) in Cambodia from 2006 to 2019. The total number of 42 SEZ is recorded. The data was collected from many sources by ODC’s mappers such as the royal gazette of Cambodia's government, and reports of the governmental ministries in hard and soft copies of pdf format. Geographic data is encoded in the WGS 84, Zone 48 North coordinate reference system.Road and railway networks in Cambodia (2012- 2019) : Road networks are produced by Open Street Map. ODC's map and data team extracted the data in vector format. Moreover, the polyline data of railway​ given by Save Cambodia's Wildlife's Atlas Working Group in Cambodia for two statuses such as existing, proposed new lines in Cambodia.Forest cover in Cambodia (2015-2018) : This forest cover is extracted from the Forest Monitoring System (https://rlcms-servir.adpc.net/en/forest-monitor/) which is developed by SERVIR-Mekong and the Global Land Analysis and Discovery Lab (GLAD) from University of Maryland. The definition of forest for this dataset is the tree canopy greater than 10% with height more than 5 meters.Schools in flood-prone area 2013 (information 2012-2014) : This dataset is created by clipping between Cambodia flood-prone areas in 2013 dataset and Basic information of school dataset to identify schools are under the flood extend in 2013. The basic information of school contains the spatial location of school, the attribute information in 2014, and total enrollment in 2012.Basic map of Cambodia (2014) : These datasets contain three different types of administrative boundary levels: provincial, district and commune which were contributed by Office for the Coordination of Humanitarian Affairs (OCHA) to Humanitarian Data Exchange (HDX). The datasets were obtained from the Department of Geography of Ministry of Land Management, Urban Planning and Construction (MLMUPC) in 2008 and then unofficially updated in 2014 by referring to Sub-decrees on administrative modifications. Most Recent Changes: New province added (Tbong Khmum), with underlying districts and communes.Land cover in Cambodia (2012- 2016) : The land cover is extracted from the Regional Land Cover Monitoring System (https://rlcms-servir.adpc.net/en/landcover/) which is developed by SERVIR-Mekong. The primitives are calculated from remote sensing indices which were made from yearly Landsat surface reflectance composites. The training data were collected by combining field information with high-resolution satellite imagery.Cropland in Cambodia : This dataset contains information of cropland and location of croplands in Cambodia which was downloaded from World Food Programme GeoNode (WFPGeoNode) using data in 2013 from​ the Department of Land and Geography of the Ministry of Land Management, Urban Planning and Construction.Community Fisheries Map for Cambodia (2011) : This dataset provides 2011 geographic boundaries, size and the number of villages covered by each community fishery for which coordinates are available in Cambodia, as given by the Fisheries Administration. For those community fisheries sites without coordinates, locations are given as the center points of communes and metrics are taken from the Commune Database of 2011. Geographic data is encoded in the WGS 84 coordinate reference system. Data were provided to ODC in vector format by Save Cambodia's Wildlife's Atlas Working Group.Digital Elevation Model (DEM 12.5 m) in 2010 : This raster dataset provides the Digital Elevation Model in the world. Dataset were provided to ASF Data Search Vertex by EarthData. This dataset has high resolution terrain at 12.5 meter. Alaska Satellite Facility (ASF) : making remote-sensing data accessible. ASF operates the NASA archive of synthetic aperture radar (SAR) data from a variety of satellites and aircraft, providing these data and associated specialty support services to researchers in support of NASA’s Earth Science Data and Information System (ESDIS) project.Function Area : This dataset are produced by Open Street Map. The data extracted the data in vector format (point feature).Tourism area (Museum, Attraction) : This dataset are produced by Open Street Map. The data extracted the data in vector format (point feature).Entity : Royal Government of Cambodia, Ministry of Planning, National Institute of Statistics; Cambodian Demographic Census 2008. Phnom Penh, 2008; Save Cambodia's Wildlife; In Atlas of Cambodia: maps on socio-economic development and environment;Time period : 2006-2018Frequency of update : Always up-to-dateGeo-coverage() : NationalGeo-coverage: National() : Cambodia

This dataset contains the predicted prices of DeF-Ai for the upcoming years based on user-defined projections.

The Climate Resilience Information System (CRIS) provides data and tools for developers of climate services. This layer has projections of VAR in decadal increments from 1950 to 2100 and for three Shared Socioeconomic Pathways (SSPs). The variables included are:Annual number of days with a maximum temperature greater than or equal to 85°F Annual number of days with a maximum temperature greater than or equal to 86°F Annual number of days with a maximum temperature greater than or equal to 90°F Annual number of days with a maximum temperature greater than or equal to 95°F Annual number of days with a maximum temperature greater than or equal to 100°F Annual number of days with a maximum temperature greater than or equal to 105°F Annual number of days with a maximum temperature greater than or equal to 110°F Annual number of days with a maximum temperature greater than or equal to 115°F This layer uses data from the LOCA2 downscaled climate models for the Contiguous United States. Further processing by the NOAA Technical Support Unit at CICS-NC and Esri are explained below.For each time and SSP, there are minimum, maximum, and mean values for the defined respective geography: counties, tribal areas, HUC-8 watersheds. The process for deriving these summaries is available in Understanding CRIS Data. The combination of time and geography is available for a weighted ensemble of 16 climate projections. More details on the models included in the ensemble and the weighting methodologies can be found in CRIS Data Preparation. Other climate variables are available from the CRIS website’s Data Gallery page or can be accessed in the table below. Additional geographies, including Alaska, Hawai’i and Puerto Rico will be made available in the future.GeographiesThis layer provides projected values for three geographies: county, tribal area, and HUC-8 watersheds.County: based on the U.S. Census TIGER/Line 2022 distribution. Tribal areas: based on the U.S. Census American Indian/Alaska Native/Native Hawaiian Area dataset 2022 distribution. This dataset includes federal- and state-recognized statistical areas.HUC-8 watershed: based on the USGS Washed Boundary Dataset, part of the National Hydrography Database Plus High Resolution. Time RangesProjected climate threshold values (e.g. Days Over 90°F) were calculated for each year from 2005 to 2100. Additionally, values are available for the modeled history runs from 1951 - 2005. The modeled history and future projections have been merged into a single time series and averaged by decade.Climate ScenariosClimate models use future scenarios of greenhouse gas concentrations and human activities to project overall change. These different scenarios are called the Shared Socioeconomic Pathways (SSPs). Three different SSPs are available here: 2-4.5, 3-7.0, and 5-8.5 (STAR does not have SSP3-7.0). The number before the dash represents a societal behavior scenario. The number after the dash indicates the amount of radiative forcing (watts per meter square) associated with the greenhouse gas concentration scenario in the year 2100 (higher forcing = greater warming). It is unclear which scenario will be the most likely, but SSP 2-4.5 currently aligns with the international targets of the COP-26 agreement. SSP3-7.0 may be the most likely scenario based on current emission trends. SSP5-8.5 acts as a cautionary tale, providing a worst-case scenario if reductions in greenhouse gasses are not undertaken. Data ExportExporting this data into shapefiles, geodatabases, GeoJSON, etc is enabled.

Data sets used to prepare illustrative figures for the overview article “Multiscale Modeling of Background Ozone” Overview

The CMAQ model output datasets used to create illustrative figures for this overview article were generated by scientists in EPA/ORD/CEMM and EPA/OAR/OAQPS.

The EPA/ORD/CEMM-generated dataset consisted of hourly CMAQ output from two simulations. The first simulation was performed for July 1 – 31 over a 12 km modeling domain covering the Western U.S. The simulation was configured with the Integrated Source Apportionment Method (ISAM) to estimate the contributions from 9 source categories to modeled ozone. ISAM source contributions for July 17 – 31 averaged over all grid cells located in Colorado were used to generate the illustrative pie chart in the overview article. The second simulation was performed for October 1, 2013 – August 31, 2014 over a 108 km modeling domain covering the northern hemisphere. This simulation was also configured with ISAM to estimate the contributions from non-US anthropogenic sources, natural sources, stratospheric ozone, and other sources on ozone concentrations. Ozone ISAM results from this simulation were extracted along a boundary curtain of the 12 km modeling domain specified over the Western U.S. for the time period January 1, 2014 – July 31, 2014 and used to generate the illustrative time-height cross-sections in the overview article.

The EPA/OAR/OAQPS-generated dataset consisted of hourly gridded CMAQ output for surface ozone concentrations for the year 2016. The CMAQ simulations were performed over the northern hemisphere at a horizontal resolution of 108 km. NO2 and O3 data for July 2016 was extracted from these simulations generate the vertically-integrated column densities shown in the illustrative comparison to satellite-derived column densities.

CMAQ Model Data

The data from the CMAQ model simulations used in this research effort are very large (several terabytes) and cannot be uploaded to ScienceHub due to size restrictions. The model simulations are stored on the /asm archival system accessible through the atmos high-performance computing (HPC) system. Due to data management policies, files on /asm are subject to expiry depending on the template of the project. Files not requested for extension after the expiry date are deleted permanently from the system. The format of the files used in this analysis and listed below is ioapi/netcdf. Documentation of this format, including definitions of the geographical projection attributes contained in the file headers, are available at https://www.cmascenter.org/ioapi/

Documentation on the CMAQ model, including a description of the output file format and output model species can be found in the CMAQ documentation on the CMAQ GitHub site at https://github.com/USEPA/CMAQ.

This dataset is associated with the following publication: Hogrefe, C., B. Henderson, G. Tonnesen, R. Mathur, and R. Matichuk. Multiscale Modeling of Background Ozone: Research Needs to Inform and Improve Air Quality Management. EM Magazine. Air and Waste Management Association, Pittsburgh, PA, USA, 1-6, (2020).

The Climate Resilience Information System (CRIS) provides data and tools for developers of climate services. This layer has projections of VAR in decadal increments from 1950 to 2100 and for three Shared Socioeconomic Pathways (SSPs). The variables included are:Annual total precipitation (inches) Annual highest precipitation total for a single day (inches) Annual highest precipitation total over a 5-day period (inches) Annual highest precipitation total over a 10-day period (inches) Annual total precipitation for all days exceeding the 90th percentile (inches) Annual total precipitation for all days exceeding the 95th percentile (inches) Annual total precipitation for all days exceeding the 99th percentile (inches) This layer uses data from the LOCA2 and STAR-ESDM downscaled climate models for the Contiguous United States. Further processing by the NOAA Technical Support Unit at CICS-NC and Esri are explained below.For each time and SSP, there are minimum, maximum, and mean values for the defined respective geography: counties, tribal areas, HUC-8 watersheds. The process for deriving these summaries is available in Understanding CRIS Data. The combination of time and geography is available for a weighted ensemble of 16 climate projections. More details on the models included in the ensemble and the weighting methodologies can be found in CRIS Data Preparation. Other climate variables are available from the CRIS website’s Data Gallery page or can be accessed in the table below. Additional geographies, including Alaska, Hawai’i and Puerto Rico will be made available in the future.GeographiesThis layer provides projected values for three geographies: county, tribal area, and HUC-8 watersheds.County: based on the U.S. Census TIGER/Line 2022 distribution. Tribal areas: based on the U.S. Census American Indian/Alaska Native/Native Hawaiian Area dataset 2022 distribution. This dataset includes federal- and state-recognized statistical areas.HUC-8 watershed: based on the USGS Washed Boundary Dataset, part of the National Hydrography Database Plus High Resolution. Time RangesProjected climate threshold values (e.g. Days Over 90°F) were calculated for each year from 2005 to 2100. Additionally, values are available for the modeled history runs from 1951 - 2005. The modeled history and future projections have been merged into a single time series and averaged by decade.Climate ScenariosClimate models use future scenarios of greenhouse gas concentrations and human activities to project overall change. These different scenarios are called the Shared Socioeconomic Pathways (SSPs). Three different SSPs are available here: 2-4.5, 3-7.0, and 5-8.5 (STAR does not have SSP3-7.0). The number before the dash represents a societal behavior scenario. The number after the dash indicates the amount of radiative forcing (watts per meter square) associated with the greenhouse gas concentration scenario in the year 2100 (higher forcing = greater warming). It is unclear which scenario will be the most likely, but SSP 2-4.5 currently aligns with the international targets of the COP-26 agreement. SSP3-7.0 may be the most likely scenario based on current emission trends. SSP5-8.5 acts as a cautionary tale, providing a worst-case scenario if reductions in greenhouse gasses are not undertaken. Data ExportExporting this data into shapefiles, geodatabases, GeoJSON, etc is enabled.

Air traffic network-product is a link-knot routing dataset compliant with INSPIRE requirements. It includes f.ex. flight routes and aerodromes. Data shall not be used for operational flight activities or flight planning.

INSPIRE Air Traffic Network-product includes spatial information of air traffic network in accordance with the INSPIRE Directive. The data has been retrieved from the EAD database maintained by Eurocontrol. Information is updated regularly but is not constantly up to date. Data can be used for purposes that are in accordance with the INSPIRE Directive, but shall not be used for operational flight activities or flight planning. ANS Finland www.ais.fi –site provides information for operational flight activities or flight planning

Available layers

Aerodrome Node: Node located at the aerodrome reference point of an airport/heliport, which is used to represent it in a simplified way.DEFINITION Aerodrome Reference Point (ARP): The designated geographical location of an aerodrome, located near the initial or planned geometric centre of the aerodrome and normally remaining where originally established [AIXM3.3].DEFINITION Airport/heliport: A defined area on land or water (including any buildings, installations and equipment) intended to be used either wholly or in part for the arrival, departure and surface movement of aircraft/helicopters [AIXM5.0].

Air Route Link: A portion of a route to be flown usually without an intermediate stop, as defined by two consecutive significant points

Air Space Area: A defined volume in the air, described as horizontal projection with vertical limits.

Designated Point: A geographical location not marked by the site of a radio navigation aid, used in defining an ATS route, the flight path of an aircraft or for other navigation or ATS purposes.

Instrument Approach Procedure: A series of predetermined manoeuvres by reference to flight instruments with specified protection from obstacles from the initial approach fix, or where applicable, from the beginning of a defined arrival route to a point from which a landing can be completed and thereafter, if a landing is not completed, to a position at which holding or en route obstacle clearance criteria apply.

Navaid: One or more Navaid Equipments providing navigation services.DEFINITION Navaid equipment: A physical navaid equipment like VOR, DME, localizer, TACAN or etc.

Procedure Link: A series of predetermined manoeuvres with specified protection from obstacles.

Runway Area: A defined rectangular area on a land aerodrome/heliport prepared for the landing and take-off of aircraft.

Runway Centerline Point: An operationally significant position on the center line of a runway direction.

Standard Instrument Arrival: A designated instrument flight rule (IFR) arrival route linking a significant point, normally on an ATS route, with a point from which a published instrument approach procedure can be commenced.

Standard Instrument Departure: A designated instrument flight rule (IFR) departure route linking the aerodrome or a specific runway of the aerodrome with a specified significant point, normally on a designated ATS route, at which the en-route phase of a flight commences.

Surface Composition: Runway surface material

CTR (Not INSPIRE): A control zone (CTR) is a block of Controlled Airspace extending from the surface of the earth to a specified upper limit.

The Department of Veterans Affairs provides official estimates and projections of the Veteran population using the Veteran Population Projection Model (VetPop). Based on the latest model VetPop2023 and the most recent national survey estimates from the 2023 American Community Survey 1-Year (ACS) data, the projected number of Veterans living in the 50 states, DC and Puerto Rico for fiscal years, 2023 to 2025, are allocated to Urban and Rural areas. As defined by the Census Bureau, Rural encompasses all population, housing, and territory not included within an Urban area (https://www.census.gov/programs-surveys/geography/guidance/geo-areas/urban-rural.html).

This table contains the Veteran estimates by urban/rural, age group, poverty, and disability. The poverty level and disability are determined by ACS based on responses on total income and functional difficulties. Refer to the sections on Poverty and Disability Status in the document, https://www2.census.gov/programs-surveys/acs/tech_docs/subject_definitions/2023_ACSSubjectDefinitions.pdf

Note: rounding to the nearest 1,000 is always appropriate for VetPop estimates.

The Climate Resilience Information System (CRIS) provides data and tools for developers of climate services. This layer has projections of VAR in decadal increments from 1950 to 2100 and for three Shared Socioeconomic Pathways (SSPs). The variables included are:Annual number of days with total precipitation greater than or equal to 1 inch Annual number of days with total precipitation greater than or equal to 2 inches Annual number of days with total precipitation greater than or equal to 3 inches Annual number of days with total precipitation greater than or equal to 4 inches Annual number of days with precipitation exceeding the 90th percentile for non-zero precipitation days Annual number of days with precipitation exceeding the 95th percentile for non-zero precipitation days Annual number of days with precipitation exceeding the 99th percentile for non-zero precipitation days This layer uses data from the LOCA2 and STAR-ESDM downscaled climate models for the Contiguous United States. Further processing by the NOAA Technical Support Unit at CICS-NC and Esri are explained below.For each time and SSP, there are minimum, maximum, and mean values for the defined respective geography: counties, tribal areas, HUC-8 watersheds. The process for deriving these summaries is available in Understanding CRIS Data. The combination of time and geography is available for a weighted ensemble of 16 climate projections. More details on the models included in the ensemble and the weighting methodologies can be found in CRIS Data Preparation. Other climate variables are available from the CRIS website’s Data Gallery page or can be accessed in the table below. Additional geographies, including Alaska, Hawai’i and Puerto Rico will be made available in the future.GeographiesThis layer provides projected values for three geographies: county, tribal area, and HUC-8 watersheds.County: based on the U.S. Census TIGER/Line 2022 distribution. Tribal areas: based on the U.S. Census American Indian/Alaska Native/Native Hawaiian Area dataset 2022 distribution. This dataset includes federal- and state-recognized statistical areas.HUC-8 watershed: based on the USGS Washed Boundary Dataset, part of the National Hydrography Database Plus High Resolution. Time RangesProjected climate threshold values (e.g. Days Over 90°F) were calculated for each year from 2005 to 2100. Additionally, values are available for the modeled history runs from 1951 - 2005. The modeled history and future projections have been merged into a single time series and averaged by decade.Climate ScenariosClimate models use future scenarios of greenhouse gas concentrations and human activities to project overall change. These different scenarios are called the Shared Socioeconomic Pathways (SSPs). Three different SSPs are available here: 2-4.5, 3-7.0, and 5-8.5 (STAR does not have SSP3-7.0). The number before the dash represents a societal behavior scenario. The number after the dash indicates the amount of radiative forcing (watts per meter square) associated with the greenhouse gas concentration scenario in the year 2100 (higher forcing = greater warming). It is unclear which scenario will be the most likely, but SSP 2-4.5 currently aligns with the international targets of the COP-26 agreement. SSP3-7.0 may be the most likely scenario based on current emission trends. SSP5-8.5 acts as a cautionary tale, providing a worst-case scenario if reductions in greenhouse gasses are not undertaken. Data ExportExporting this data into shapefiles, geodatabases, GeoJSON, etc is enabled.

The Climate Resilience Information System (CRIS) provides data and tools for developers of climate services. This layer has projections of VAR in decadal increments from 1950 to 2100 and for three Shared Socioeconomic Pathways (SSPs). The variables included are:Annual total precipitation (inches) Annual highest precipitation total for a single day (inches) Annual highest precipitation total over a 5-day period (inches) Annual highest precipitation total over a 10-day period (inches) Annual total precipitation for all days exceeding the 90th percentile (inches) Annual total precipitation for all days exceeding the 95th percentile (inches) Annual total precipitation for all days exceeding the 99th percentile (inches) This layer uses data from the LOCA2 and STAR-ESDM downscaled climate models for the Contiguous United States. Further processing by the NOAA Technical Support Unit at CICS-NC and Esri are explained below.For each time and SSP, there are minimum, maximum, and mean values for the defined respective geography: counties, tribal areas, HUC-8 watersheds. The process for deriving these summaries is available in Understanding CRIS Data. The combination of time and geography is available for a weighted ensemble of 16 climate projections. More details on the models included in the ensemble and the weighting methodologies can be found in CRIS Data Preparation. Other climate variables are available from the CRIS website’s Data Gallery page or can be accessed in the table below. Additional geographies, including Alaska, Hawai’i and Puerto Rico will be made available in the future.GeographiesThis layer provides projected values for three geographies: county, tribal area, and HUC-8 watersheds.County: based on the U.S. Census TIGER/Line 2022 distribution. Tribal areas: based on the U.S. Census American Indian/Alaska Native/Native Hawaiian Area dataset 2022 distribution. This dataset includes federal- and state-recognized statistical areas.HUC-8 watershed: based on the USGS Washed Boundary Dataset, part of the National Hydrography Database Plus High Resolution. Time RangesProjected climate threshold values (e.g. Days Over 90°F) were calculated for each year from 2005 to 2100. Additionally, values are available for the modeled history runs from 1951 - 2005. The modeled history and future projections have been merged into a single time series and averaged by decade.Climate ScenariosClimate models use future scenarios of greenhouse gas concentrations and human activities to project overall change. These different scenarios are called the Shared Socioeconomic Pathways (SSPs). Three different SSPs are available here: 2-4.5, 3-7.0, and 5-8.5 (STAR does not have SSP3-7.0). The number before the dash represents a societal behavior scenario. The number after the dash indicates the amount of radiative forcing (watts per meter square) associated with the greenhouse gas concentration scenario in the year 2100 (higher forcing = greater warming). It is unclear which scenario will be the most likely, but SSP 2-4.5 currently aligns with the international targets of the COP-26 agreement. SSP3-7.0 may be the most likely scenario based on current emission trends. SSP5-8.5 acts as a cautionary tale, providing a worst-case scenario if reductions in greenhouse gasses are not undertaken. Data ExportExporting this data into shapefiles, geodatabases, GeoJSON, etc is enabled.

Technical Documentation for data repositoryJune 2021prepared by Mathis L. Messager (messamat@uw.edu)1. Overview and backgroundThis documentation describes the data produced for the research article: Messager, M. L., Ettinger, A. K., Murphy-Williams, M., & Levin, P. S. (2021). Fine-scale assessment of inequities in inland flood vulnerability. Applied Geography, 133, 102492. https://doi.org/https://doi.org/10.1016/j.apgeog.2021.102492In this study, we examine whether households experience unequal vulnerability to inland flooding based on their race and ethnicity in Washington State, U.S.A. Focusing on individual land parcels, we show that 9% of the population in the state lives in a flood zone, 16% of which are Latinx, even though Latinx residents make up only 8% of the overall population. Beyond disparities in exposure, we found that communities also differ in their vulnerability to floods. We demonstrate that using finer-grain data and improved flood hazard maps leads to starker estimates of total flood exposure and racial/ethnic inequities than using official data and conventional methods. Our results provide key information to advocate for and guide actions to mitigate racial and ethnic inequities in flood vulnerability.The data repository includes the main dataset resulting from this study: tract-level estimates of flood exposure and representativeness of residents across Washington State by race and ethnicity.All scripts used in this study are available for reuse:- https://github.com/messamat/flood_vulnerability_wa for spatial analysis, formatting of parcel database, flood exposure analysis- https://github.com/messamat/flood_vulnerability_waR for data post-processing, and statistical analysis.2. Data format and distribution The dataset is distributed both in ESRI® file geodatabase and shapefile formats. A shapefile is provided as a copy for users that cannot read the geodatabase. Each shapefile consists of five main files (.dbf, .sbn, .sbx, .shp, .shx), and projection information is provided in an ASCII text file (.prj). The attribute table can be accessed as a stand-alone file in dBASE format (.dbf) which is included in the Shapefile format.This data layer is provided in the following projected coordinate system NAD_1983_StatePlane_Washington_South_FIPS_4602_Feet (EPSG: 2286).File name: tracts_wa_floodinequities | Dimensions: 1,458 rows and 33 columns.See PDF of technical documentation for a description of the attributes. 3. License and citations3.1. License agreementThis documentation and datasets are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (CC-BY-4.0 License). For all regulations regarding license grants, copyright, redistribution restrictions, required attributions, disclaimer of warranty, indemnification, liability, waiver of damages, and a precise definition of licensed materials, please refer to the License Agreement (https://creativecommons.org/licenses/by/4.0/legalcode). For a human-readable summary of the license, please see https://creativecommons.org/licenses/by/4.0/.3.2. Citations and acknowledgements.Messager, M. L., Ettinger, A. K., Murphy-Williams, M., & Levin, P. S. (2021). Fine-scale assessment of inequities in inland flood vulnerability. Applied Geography, 133, 102492. https://doi.org/https://doi.org/10.1016/j.apgeog.2021.102492