Local land ownership for Southeast Alaska is shown. The data includes agency, administration group and the unit. File generated from running the Extract Data solution.

This dataset is available for download from: Wetlands (File Geodatabase).

Wetlands in California are protected by several federal and state laws, regulations, and policies. This layer was extracted from the broader land cover raster from the CA Nature project which was recently enhanced to include a more comprehensive definition of wetland. This wetlands dataset is used as an exclusion as part of the biological planning priorities in the CEC 2023 Land-Use Screens.

This layer is featured in the CEC 2023 Land-Use Screens for Electric System Planning data viewer.

For more information about this layer and its use in electric system planning, please refer to the Land Use Screens Staff Report in the CEC Energy Planning Library.

Change Log

Version 1.1 (January 26, 2023)

• Full resolution of wetlands replaced a coarser resolution version that was previously shared. Also, file type changed from polygon to raster (feature service to tile layer service).

This is a surface area polygon of the Indian Bend Watershed canal union.

This data layer references data from a high-resolution tree canopy change-detection layer for Seattle, Washington. Tree canopy change was mapped by using remotely sensed data from two time periods (2016 and 2021). Tree canopy was assigned to three classes: 1) no change, 2) gain, and 3) loss. No change represents tree canopy that remained the same from one time period to the next. Gain represents tree canopy that increased or was newly added, from one time period to the next. Loss represents the tree canopy that was removed from one time period to the next. Mapping was carried out using an approach that integrated automated feature extraction with manual edits. Care was taken to ensure that changes to the tree canopy were due to actual change in the land cover as opposed to differences in the remotely sensed data stemming from lighting conditions or image parallax. Direct comparison was possible because land-cover maps from both time periods were created using object-based image analysis (OBIA) and included similar source datasets (LiDAR-derived surface models, multispectral imagery, and thematic GIS inputs). OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, _location, size, and shape) into the classification process. A series of morphological procedures were employed to ensure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset was subjected to manual review and correction.University of Vermont Spatial Analysis LaboratoryThis dataset consists of City of Seattle Council District areas as they existed in the first comparison year (2016) which cover the following tree canopy categories:Existing tree canopy percentPossible tree canopy - vegetation percentRelative percent changeAbsolute percent changeFor more information, please see the 2021 Tree Canopy Assessment.

Wetlands in California are protected by several federal and state laws, regulations, and policies. This layer was extracted from the broader vegetation raster from the CA Nature project which was recently enhanced to include a more comprehensive definition of wetland. This wetlands dataset is used as an exclusion as part of the biological planning priorities in the CEC 2023 Land-Use Screens.

This layer is featured in the CEC 2023 Land-Use Screens for Electric System Planning data viewer.

For more information about this layer and its use in electric system planning, please refer to the Land Use Screens Staff Report in the CEC Energy Planning Library.

U.S. Counties represents the counties of the United States in the 50 states, the District of Columbia, and Puerto Rico.

Tree canopy was derived from high-resolution remotely sensed data -- 2018 NAIP and 2019 LiDAR. Object-based image analysis techniques (OBIA) were employed to extract potential tree canopy and trees using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:2000 and all observable errors were corrected.

Published: August 2022A vector polygon GIS file that includes 1) the New York State boundary over land areas and 2) the state shoreline, including islands, in areas where the state boundary extends over major hydrographic features. The purpose is to provide an “outline” of the state for GIS and cartographic uses. It can be used to clip the boundaries in the Cities_Towns and Counties layers back to the shoreline if it is desired to primarily only use or depict the land areas covered by jurisdictions around the perimeter of the state. The boundaries were revised to 1:24,000-scale accuracy. Ongoing work will adjust the shorelines to 1:24,000-scale accuracy.

This layer is being made accessible on this platform as part of a larger collaborative project under development by Arizona Water Company, University of Arizona Water Resources Research Center, Babbitt Center for Land and Water Policy, and Center for Geospatial Solutions. This visualization for Pinal County expresses 2021 U.S. Census block, tract, and places data and the boundaries of the Active Management Areas and Pinal County within Arizona. All of these shapefiles have been altered for visualization purposes.The main sources of data present in this feature layer were taken from the following locations:U.S. Census Tiger/Line Shapefiles (2021)2021 TIGER/Line® Shapefiles (census.gov)The University of Arizona (2008)https://repository.arizona.edu/handle/10150/188734Arizona Department of Water Resources GIS Data (2021)https://gisdata2016-11-18t150447874z-azwater.opendata.arcgis.com/datasets/azwater::ama-and-ina-1/explore?location=34.158174%2C-111.970823%2C7.24These shapefiles were altered.

This data layer contains geothermal resource areas and their technical potential used in long-term electric system modeling for Integrated Resource Planning and SB 100. Geothermal resource areas are delineated by Known Geothermal Resource Areas (KGRAs) (Geothermal Map of California, 2002), other geothermal fields (CalGEM Field Admin Boundaries, 2020), and Bureau of Land Management (BLM) Geothermal Leasing Areas (California BLM State Office GIS Department, 2010). The fields that are considered in our assessment have enough information known about the geothermal reservoir that an electric generation potential was estimated by USGS (Williams et al. 2008) or estimated by a BLM Environmental Impact Statement (El Centro Field Office, 2007). For the USGS identified geothermal systems, any point that lies within 2 km of a field is summed to represent the total mean electrical generation potential from the entire field.

Geothermal field boundaries are constructed for identified geothermal systems that lie outside of an established geothermal field. A circular footprint is assumed with a radius determined by the area needed to support the mean resource potential estimate, assuming a 10 MW/km² power density.

Several geothermal fields have power plants that are currently generating electricity from the geothermal source. The total production for each geothermal field is estimated by the CA Energy Commission’s Quarterly Fuel and Energy Report that tracks all power plants greater than 1 MW. The nameplate capacity of all generators in operation as of 2021 were used to inform how much of the geothermal fields are currently in use. This source yields inconsistent results for the power plants in the Geysers. Instead, an estimate from the net energy generation from those power plants is used. Using these estimates, the net undeveloped geothermal resource potential can be calculated.

Finally, we apply the protected area layer for geothermal to screen out those geothermal fields that lie entirely within a protected area. The protected area layer is compiled from public and private lands that have special designations prohibiting or not aligning with energy development.

This layer is featured in the CEC 2023 Land-Use Screens for Electric System Planning data viewer.

For more information about this layer and its use in electric system planning, please refer to the Land Use Screens Staff Report in the CEC Energy Planning Library.

Change Log:

Version 1.1 (January 18, 2024)

• ProtectedArea_Exclusion field was updated to correct for the changes to the Protected Area Layer. A Development Focus Area on Bureau of Land Management (BLM) land that overlays the Coso Hot Springs allows its resource potential to be considered in the statewide estimate.

Data Dictionary:

Total_MWe_Mean: The estimated resource potential from each geothermal field. All geothermal fields, except for Truckhaven, was given an estimate by Williams et al. 2008. If more than one point resource intersects (within 2km of) the field, the sum of the individual geothermal systems was used to estimate the magnitude of the resource coming from the entire geothermal field. Estimates are given in MW.

Total_QFER_NameplateCapacity: The total nameplate capacities of all generators in operation as of 2021 that intersects (within 2 km of) a geothermal field. The resource potential already in use for the Geysers is determined by Lovekin et al. 2004. Estimates are given in MW.

ProtectedArea_Exclusion: Binary value representing whether a field is excluded by the land-use screen or not. Fields that are excluded have a value of 1; those that aren’t have a value of 0.

NetUndevelopedRP: The net undeveloped resource potential for each geothermal field. This field is determined by subtracting the total resource potential in use (Total_QFER_NameplateCapacity) from the total estimated resource potential (Total_MWe_Mean). Estimates are given in MW.

Acres_GeothermalField: This is the geodesic acreage of each geothermal field. Values are reported in International Acres using a NAD 1983 California (Teale) Albers (Meters) projection.

References:

1. Geothermal Map of California, S-11. California Department of Conservation, 2002. https://www.conservation.ca.gov/calgem/geothermal/maps/Pages/index.aspx
2. CalGEM Field Admin Boundaries, 2020. https://gis.conservation.ca.gov/server/rest/services/CalGEM/Admin_Bounds/MapServer
   
3. California BLM State Office GIS Department, California BLM Verified and Potential Geothermal Leases in California, 2010. https://databasin.org/datasets/5ec77a1438ab4402bf09ef9bfd7f04d9/
   
4. Williams, Colin F., Reed, Marshall J., Mariner, Robert H., DeAngelo, Jacob, Galanis, S. Peter, Jr. 2008. "Assessment of moderate- and high-temperature geothermal resources of the United States: U.S. Geological Survey Fact Sheet 2008-3082." 4 p. https://certmapper.cr.usgs.gov/server/rest/services/geothermal/westus_favoribility_systems/MapServer/0
5. El Centro Field Office, Bureau of Land Management (2007). Final Environmental Impact Statement for the Truckhaven Geothermal Leasing Area (Publication Index Number: BLM/CA/ES-2007-017+3200). United States Department of the Interior Bureau of Land Management.
6. Lovekin, James W., Subir K. Sanyal, Christopher W. Klein. 2004. “New Geothermal Site Identification and Qualification.” Richmond, California:

In 2012, the CPUC ordered the development of a statewide map that is designed specifically for the purpose of identifying areas where there is an increased risk for utility associated wildfires. The development of the CPUC -sponsored fire-threat map, herein "CPUC Fire-Threat Map," started in R.08-11-005 and continued in R.15-05-006.

A multistep process was used to develop the statewide CPUC Fire-Threat Map. The first step was to develop Fire Map 1 (FM 1), an agnostic map which depicts areas of California where there is an elevated hazard for the ignition and rapid spread of powerline fires due to strong winds, abundant dry vegetation, and other environmental conditions. These are the environmental conditions associated with the catastrophic powerline fires that burned 334 square miles of Southern California in October 2007. FM 1 was developed by CAL FIRE and adopted by the CPUC in Decision 16-05-036.

FM 1 served as the foundation for the development of the final CPUC Fire-Threat Map. The CPUC Fire-Threat Map delineates, in part, the boundaries of a new High Fire-Threat District (HFTD) where utility infrastructure and operations will be subject to stricter fire‑safety regulations. Importantly, the CPUC Fire-Threat Map (1) incorporates the fire hazards associated with historical powerline wildfires besides the October 2007 fires in Southern California (e.g., the Butte Fire that burned 71,000 acres in Amador and Calaveras Counties in September 2015), and (2) ranks fire-threat areas based on the risks that utility-associated wildfires pose to people and property.

Primary responsibility for the development of the CPUC Fire-Threat Map was delegated to a group of utility mapping experts known as the Peer Development Panel (PDP), with oversight from a team of independent experts known as the Independent Review Team (IRT). The members of the IRT were selected by CAL FIRE and CAL FIRE served as the Chair of the IRT. The development of CPUC Fire-Threat Map includes input from many stakeholders, including investor-owned and publicly owned electric utilities, communications infrastructure providers, public interest groups, and local public safety agencies.

The PDP served a draft statewide CPUC Fire-Threat Map on July 31, 2017, which was subsequently reviewed by the IRT. On October 2 and October 5, 2017, the PDP filed an Initial CPUC Fire-Threat Map that reflected the results of the IRT's review through September 25, 2017. The final IRT-approved CPUC Fire-Threat Map was filed on November 17, 2017. On November 21, 2017, SED filed on behalf of the IRT a summary report detailing the production of the CPUC Fire-Threat Map(referenced at the time as Fire Map 2). Interested parties were provided opportunity to submit alternate maps, written comments on the IRT-approved map and alternate maps (if any), and motions for Evidentiary Hearings. No motions for Evidentiary Hearings or alternate map proposals were received. As such, on January 19, 2018 the CPUC adopted, via Safety and Enforcement Division's (SED) disposition of a Tier 1 Advice Letter, the final CPUC Fire-Threat Map.

Additional information can be found here.

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 to meet NASA requirements for astronaut navigation, referred to as LGRS in Artemis Condensed Coordinates (ACC). This data release includes LGRS grids finer than 25km (1km, 100m, and 10m) in ACC format for a small number of terrestrial analog sites of interest. The grids contained in this data release are projected in the terrestrial Universal Transverse Mercator (UTM) Projected Coordinate Reference System (PCRS) using the World Geodetic System of 1984 (WGS84) as its reference datum. A small number of geotiffs used to related the linear distortion the UTM and WGS84 systems imposes on the analog sites include: 1) a clipped USGS Nation Elevation Dataset (NED) Digital Elevation Model (DEM); 2) the grid scale factor of the UTM zone the data is projected in, 3) the height factor based on the USGS NED DEM, 4) the combined factor, and 5) linear distortion calculated in parts-per-million (PPM). Geotiffs are projected from WGS84 in a UTM PCRS zone. Distortion calculations are based on the methods State Plane Coordinate System of 2022. See Dennis (2021; https://www.fig.net/resources/proceedings/fig_proceedings/fig2023/papers/cinema03/CINEMA03_dennis_12044.pdf) for more information. Coarser grids, (>=25km) such as the lunar LTM, LPS, and LGRS grids are not released here but may be acceded from https://doi.org/10.5066/P13YPWQD and displayed using a lunar datum. 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. Terrestrial Locations and associated LGRS ACC Grids and Files: Projection Location Files UTM 11N Yucca Flat 1km Grid Shapefile 100m Grid Shapefile 10m Grid Shapefile USGS 1/3" DEM Geotiff UTM Projection Scale Factor Geotiff Map Height Factor Geotiff Map Combined Factor Geotiff Map Linear Distortion Geotiff UTM 12N Buffalo Park 1km Grid Shapefile 100m Grid Shapefile 10m Grid Shapefile USGS 1/3" DEM Geotiff UTM Projection Scale Factor Geotiff Map Height Factor Geotiff Map Combined Factor Geotiff Map Linear Distortion Geotiff Cinder Lake 1km Grid Shapefile 100m Grid Shapefile 10m Grid Shapefile USGS 1/3" DEM Geotiff UTM Projection Scale Factor Geotiff Map Height Factor Geotiff Map Combined Factor Geotiff Map Linear Distortion Geotiff JETT3 Arizona 1km Grid Shapefile 100m Grid Shapefile 10m Grid Shapefile USGS 1/3" DEM Geotiff UTM Projection Scale Factor Geotiff Map Height Factor Geotiff Map Combined Factor Geotiff Map Linear Distortion Geotiff JETT5 Arizona 1km Grid Shapefile 100m Grid Shapefile 10m Grid Shapefile USGS 1/3" DEM Geotiff UTM Projection Scale Factor Geotiff Map Height Factor Geotiff Map Combined Factor Geotiff Map Linear Distortion Geotiff Meteor Crater 1km Grid Shapefile 100m Grid Shapefile 10m Grid Shapefile USGS 1/3" DEM Geotiff UTM Projection Scale Factor Geotiff Map Height Factor Geotiff Map Combined Factor Geotiff Map Linear Distortion Geotiff UTM 13N HAATS 1km Grid Shapefile 100m Grid Shapefile 10m Grid Shapefile 1km Grid Shapefile Derby LZ Clip 100m Grid Shapefile Derby LZ Clip 10m Grid Shapefile Derby LZ Clip 1km Grid Shapefile Eagle County Regional Airport KEGE Clip 100m Grid Shapefile Eagle County Regional Airport KEGE Clip 10m Grid Shapefile Eagle County Regional Airport KEGE Clip 1km Grid Shapefile Windy Point LZ Clip 100m Grid Shapefile Windy Point LZ Clip 10m Grid Shapefile Windy Point LZ Clip USGS 1/3" DEM Geotiff UTM Projection Scale Factor Geotiff Map Height Factor Geotiff Map Combined Factor Geotiff Map Linear Distortion Geotiff UTM 15N Johnson Space Center 1km Grid Shapefile 100m Grid Shapefile 10m Grid Shapefile USGS 1/3" DEM Geotiff UTM Projection Scale Factor Geotiff Map Height Factor Geotiff Map Combined Factor Geotiff Map Linear Distortion Geotiff UTM 28N JETT2 Icelandic Highlands 1km Grid Shapefile 100m Grid Shapefile 10m Grid Shapefile USGS 1/3" DEM Geotiff UTM Projection Scale Factor Geotiff Map Height Factor Geotiff Map Combined Factor Geotiff Map Linear Distortion Geotiff The shapefiles and rasters utilize UTM projections. For GIS utilization of grid shapefiles projected in Lunar Latitude and Longitude should utilize a registered PCRS. To select the correct UTM EPSG code, determine the zone based on longitude (zones are 6° wide, numbered 1–60 from 180°W) and hemisphere (Northern Hemisphere uses EPSG:326XX; Southern Hemisphere uses EPSG:327XX), where XX is the zone number. For display in display in latitude and longitude, select a correct WGS84 EPSG code, such as EPSG:4326. Note: 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 its 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 similarly 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 an LPS projection and equatorial areas a Transverse Mercator. We describe the differences in the techniques and methods reported in 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 grids are designed to condense a full LGRS coordinate to a relative coordinate of 6 characters in length. LGRS in ACC format is completed by imposing a 1km grid within the LGRS 25km grid, then truncating the grid precision to 10m. To me the character limit, a coordinate is reported as a relative value to the lower-left corner of the 25km LGRS zone without the zone information; However, zone information can be reported. As implemented, and 25km^2 area on the lunar surface will have a set of a unique set of ACC coordinates to report locations The shape files provided in this data release are projected in the LTM or LPS PCRSs and must utilize these projections to be dimensioned correctly.

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. This dataset is a GIS layer of Victorian groundwater-surface water interaction data clipped to the Gippsland region. Groundwater generally interacts with surface water through various processes and pathways. The development of ground water often has impacts on surface streams and visa versa. The data results from an assessment of the SW-GW interaction across the study area. The data describes GW and SW interaction in four broad classes : neutral/losing, gaining, variable and unclassified. The Australian Hydrological Geospatial Fabric Surface Hydrology Catchments dataset was used as the base layer for considering surface water as an asset. This dataset provided mapped major rivers for Victoria. Numerous investigations into the interaction between groundwater and surface water have occurred across Victoria. Where this information was available in a format suitable for use in a GIS, it was collated to inform a state wide dataset of groundwater and surface water interaction. The resulting product attributes the stream base layer (described above) with the following classification of groundwater and surface water interaction: Gaining//Variable//Neutral/Losing//Unclassified Dataset History GIS data based on DSE, 2012. Victorian groundwater-surface water interaction spatial data. Compiled for the Secure Allocation, Future Entitlements (SAFE) Project by the Department of Sustainability and Environment, Victoria. The method used to generate the dataset of groundwater and surface water interaction in Victoria can be summarised as: (1) Where a hydrogeochemistry study by Monash University was available to provide an indication of groundwater and surface water interaction it was adopted with high reliability; (2) Where a Murray-Darling Basin sustainable yields groundwater and surface water interaction study was available, it was adopted with medium reliability; (3) Where Melbourne Water Corporation (MWC) had indicated river reaches characterised by remnant pools of water in prolonged drought, this was considered to be indicative of gaining river conditions, with medium reliability; (4) Where a baseflow indices study was available, it was indicative of a gaining river reach and assigned medium reliability; (5) Where no other information existed and the river was classified as "unmodified", it was assumed to be gaining if it was perennial and neutral/losing if it was not perennial. These river reaches were assigned a low reliability; (6) Where no other information existed and the river was classified as "modified", an indication of groundwater-surface water interaction could not occur, given that the modification of flows could be responsible for the perenniality of the river reach, as opposed to being indicative of losing or gaining groundwater; (7) A reliability dataset was also generated that indicates the assumed reliability of the classification, which was directly correlated to the reliability of the input data. Dataset Citation

This web application highlights some of the capabilities for accessing Landsat imagery layers, powered by ArcGIS for Server, accessing Landsat Public Datasets running on the Amazon Web Services Cloud. The layers are updated with new Landsat images on a daily basis.

Created for you to visualize our planet and understand how the Earth has changed over time, the Esri Landsat Explorer app provides the power of Landsat satellites, which gather data beyond what the eye can see. Use this app to draw on Landsat's different bands to better explore the planet's geology, vegetation, agriculture, and cities. Additionally, access the entire Landsat archive to visualize how the Earth's surface has changed over the last forty years.

Quick access to the following band combinations and indices is provided:

• Agriculture : Highlights agriculture in bright green; Bands 6, 5, 2
• Natural Color : Sharpened with 15m panchromatic band; Bands 4, 3, 2 +8
• Color Infrared : Healthy vegetation is bright red; Bands 5, 4 ,3
• SWIR (Short Wave Infrared) : Highlights rock formations; Bands 7, 6, 4
• Geology : Highlights geologic features; Bands 7, 6, 2
• Bathymetric : Highlights underwater features; Bands 4, 3, 1
• Panchromatic : Panchromatic images at 15m; Band 8
• Vegetation Index : Normalized Difference Vegetation Index(NDVI); (Band 5 - Band 4)/(Band 5 + Band 4)
• Moisture Index : Normalized Difference Moisture Index (NDMI); (Band 5 - Band 6)/(Band 5 + Band 6)
• SAVI : Soil Adjusted Veg. Index); Offset + Scale*(1.5*(Band 5 - Band 4)/(Band 5 + Band 4 + 0.5))
• Water Index : Offset + Scale*(Band 3 - Band 6)/(Band 3 + Band 6)
• Burn Index : Offset + Scale*(Band 5 - Band 7)/(Band 5 + Band 7)
• Urban Index : Offset + Scale*(Band 5 - Band 6)/(Band 5 + Band 6)

Click anywhere on earth to see how the water balance is changing over time. This app is based on data from GLDAS version 2.1, which uses weather observations like temperature, humidity, and rainfall to run the Noah land surface model. This model estimates how much of the rain becomes runoff, how much evaporates, and how much infiltrates into the soil. These output variables, calculated every three hours, are aggregated into monthly averages, giving us a record of the hydrologic cycle going all the way back to January 2000.

Because the model is run with 0.25 degree spatial resolution (~30 km), these data should only be used for regional analysis. A specific farm or other small area might experience very different conditions than the region around it, especially because human influences like irrigation are not included.

This app can also be seen as a useful template for sharing other climate datasets. If you would like to customize it for your own organization, or use it as a starting point for your own scientific application, the source code is available on github for anyone to use.

This dataset is the NOAA NCEI SRTM15+ v2.0 (29 March 2019) clipped to the area containing and immediately surrounding the Gulf of Mexico.To provide an improved mapping of the seafloor fabric globally, NOAA NCEI have used available sounding data along with an improved global marine gravity model to develop at grid at 15 arcsecond resolution (~500 m). Land elevations are based on the best available data from SRTM, ASTER digital elevation models while the ice topography of Greenland and Antarctica is based on CryoSat-2 and IceSat. Ocean bathymetry is based on bathymetric predictions from the latest global gravity model from CryoSat-2 and Jason-1 along with 494 million carefully edited depth soundings at 15 arcsecond resolution.NOAA NCEI have used the bathymetry grid along with the improved gravity to construct a global map of abyssal hill amplitude and orientations and compare the orientations with predictions from seafloor age gradient analysis. Areas of disagreement reveal propagating rifts, microplates, and tectonic reorganizations. This SRTM15_PLUS provides the foundational bathymetry layer for Google Earth and is freely available at NOAA NCEI ftp site (topex.ucsd.edu).