Abstract:

Climate change, energy system transitions, and socioeconomic change are compounding influences affecting the growth of electricity demand. While energy efficiency initiatives and distributed resources can address a significant amount of this demand, the United States will likely still need new utility-scale generation resources. The energy sector uses capacity expansion planning models to determine the aggregate need for new generation, but these models are typically at the state or regional scale and are not equipped to address the wide range of location- and technology-specific issues that are increasingly a factor in power plant siting. To help address these challenges, we have developed the Geospatial Raster Input Data for Capacity Expansion Regional Feasibility (GRIDCERF) data package, a high-resolution product to evaluate siting suitability for renewable and non-renewable power plants in the conterminous United States. GRIDCERF offers 265 suitability layers for use with 56 power plant technology configurations in a harmonized format that can be easily ingested by geospatially-enabled modeling software. It also provides pre-compiled technology-specific suitability layers and allows for user customization to robustly address science objectives when evaluating varying future conditions.

Accompanying GitHub repository:

The following GitHub repository contains the code used to generate the data in this archive: https://github.com/IMMM-SFA/vernon-etal_2023_scidata

Contents:

Note:

GRIDCERF does not provide the source data directly due to some license restrictions related for direct redistribution of the unaltered source data. However, the included file "gridcerf_source_data_description.csv" details the provenance associated with each source dataset and notes their individual licenses/disclaimers.

Common Rasters:

Suitability Layer Type and Source

GRIDCERF Raster Name

Bureau of Land Management (BLM) Surface Management Agency Areas33

gridcerf_blm_surface_management_agency_areas.tif

BLM National Landscape Conservation System (NLCS) - National Monuments34

gridcerf_blm_nlcs_national_monument_conus.tif

BLM NLCS - Outstanding Natural Areas35

gridcerf_blm_nlcs_outstanding_natural_areas_conus.tif

BLM NLCS - Wilderness36

gridcerf_blm_nlcs_wilderness_conus.tif

BLM NLCS - Wilderness Study Areas37

gridcerf_blm_nlcs_wilderness_study_areas_conus.tif

National Park Service (NPS) Class 1 airsheds38

gridcerf_class1_airsheds_conus.tif

NPS Administrative Boundaries39

gridcerf_nps_administrative_boundaries_conus.tif

NPS Historic Trails40

gridcerf_nps_historic_trails_conus.tif

NPS Scenic Trails41

gridcerf_nps_scenic_trails_conus.tif

U.S. Fish and Wildlife Service (USFWS) - Critical Habitat42

gridcerf_usfws_critical_habitat_conus.tif

USFWS - Special Designation43

gridcerf_usfws_special_designation_conus.tif

USFWS - Wild and Scenic River System44

gridcerf_usfws_national_wild_scenic_river_system_conus.tif

USFWS - National Realty Tracts45

gridcerf_usfws_national_realty_tracts_conus.tif

National Land Cover Dataset (NLCD) Wetlands46

gridcerf_nlcd_wetlands_conus.tif

U.S. Forest Service (USFS) Administrative Boundaries47

gridcerf_usfs_administrative_boundaries_conus.tif

USFS Wilderness Areas48

gridcerf_usfs_wilderness_areas_conus.tif

U.S. Geological Survey (USGS) National Wilderness Lands49

gridcerf_usgs_wilderness_areas_conus.tif

USGS Protected Areas of the U.S - Class 1&250

gridcerf_usgs_padus_class_1_to_2_conus.tif

U.S. State Protected Lands51

gridcerf_wdpa_state_protected_lands_conus.tif

Nature Conservancy lands52

gridcerf_wdpa_tnc_managed_lands_conus.tif

Technology-specific Rasters:

Suitability Layer Type and Source

GRIDCERF Raster Name

Bureau of Indian Affairs (BIA) Land Area Representations Dataset53

gridcerf_bia_land_area_representations_conus.tif

Slope 5% or less suitable20

gridcerf_srtm_slope_5pct_or_less.tif

Slope 10% or less suitable20

gridcerf_srtm_slope_10pct_or_less.tif

Slope 12% or less suitable20

gridcerf_srtm_slope_12pct_or_less.tif

Slope 20% or less suitable20

gridcerf_srtm_slope_20pct_or_less.tif

Airports (10-mile buffer)54

gridcerf_airports_10mi_buffer_conus.tif

Airports (3-mile buffer)54

gridcerf_airports_3mi_buffer_conus.tif

Proximity to Railroad and Navigable Waters (< 5 km) 55,56

gridcerf_usdot_railnodes_navwaters_within5km.tif

Coal Supply55–57

gridcerf_coalmines20km_railnodes5km_navwaters5km_conus.tif

United States Environmental Protection Agency (EPA) CO Non-attainment Areas58

gridcerf_epa_nonattainment_co_conus.tif

EPA NOx Non-attainment Areas58

gridcerf_epa_nonattainment_no2_conus.tif

EPA Ozone Non-attainment Areas58

gridcerf_epa_nonattainment_ozone_conus.tif

EPA Lead Non-attainment Areas58

gridcerf_epa_nonattainment_lead_conus.tif

EPA PM10 Non-attainment Areas58

gridcerf_epa_nonattainment_pm10_conus.tif

EPA PM2.5 Non-attainment Areas58

gridcerf_epa_nonattainment_pm2p5_conus.tif

EPA SOx Non-attainment Areas58

gridcerf_epa_nonattainment_so2_conus.tif

Earthquake Potential59

gridcerf_usgs_earthquake_pga_0.3_at_2pct_in_50yrs_conus.tif

Densely population areas11

gridcerf_densely_populated_ssp[2,3,5]_[year].tif

Densely population areas buffered by 25 miles11

gridcerf_densely_populated_ssp[2,3,5]_[year]_buff25mi.tif

Densely population areas – nuclear11

gridcerf_densely_populated_ssp[2,3,5]_[year]_nuclear.tif

National Hydrography Dataset (version 2; NHDv2)32

gridcerf_nhd2plus_surfaceflow_greaterthan[bin]mgd_buffer20km.tif

National Renewable Energy Laboratory (NREL) concentrating solar direct normal potential26

gridcerf_nrel_solar_csp_centralized_potential.tif

NREL photovoltaic potential26

gridcerf_nrel_solar_pv_centralized_potential.tif

NREL Wind Integration National Dataset (WIND) toolkit22

gridcerf_nrel_wind_development_potential_hubheight[080,110,140]_cf35.tif

Compiled Technology Rasters:

The list of layers that make up each compiled technology raster can be found in the "reference/compiled_layer_configuration.txt" file in this data archive.

The following technology raster file names are self-descriptive in the format "gridcerf_.tif". Some technologies do not have a carbon capture or cooling type designation and will simply have technology specific considerations listed.

gridcerf_biomass_conventional_ccs_dry.tif gridcerf_biomass_conventional_ccs_oncethrough.tif gridcerf_biomass_conventional_ccs_recirculating.tif gridcerf_biomass_conventional_no-ccs_dry.tif gridcerf_biomass_conventional_no-ccs_oncethrough.tif gridcerf_biomass_conventional_no-ccs_pond.tif gridcerf_biomass_conventional_no-ccs_recirculating.tif gridcerf_biomass_igcc_no-ccs_dry.tif gridcerf_biomass_igcc_no-ccs_oncethrough.tif gridcerf_biomass_igcc_no-ccs_recirculating.tif gridcerf_biomass_igcc_with-ccs_dry.tif gridcerf_biomass_igcc_with-ccs_oncethrough.tif gridcerf_biomass_igcc_with-ccs_recirculating.tif gridcerf_coal_conventional_ccs_dry.tif gridcerf_coal_conventional_ccs_oncethrough.tif gridcerf_coal_conventional_ccs_recirculating.tif gridcerf_coal_conventional_no-ccs_dry.tif gridcerf_coal_conventional_no-ccs_oncethrough.tif gridcerf_coal_conventional_no-ccs_pond.tif gridcerf_coal_conventional_no-ccs_recirculating.tif gridcerf_coal_igcc_no-ccs_dry.tif gridcerf_coal_igcc_no-ccs_oncethrough.tif gridcerf_coal_igcc_no-ccs_recirculating.tif gridcerf_coal_igcc_with-ccs_dry.tif gridcerf_coal_igcc_with-ccs_oncethrough.tif gridcerf_coal_igcc_with-ccs_recirculating.tif gridcerf_gas_cc_ccs_dry.tif gridcerf_gas_cc_ccs_oncethrough.tif gridcerf_gas_cc_ccs_recirculating.tif gridcerf_gas_cc_no-ccs_dry.tif gridcerf_gas_cc_no-ccs_oncethrough.tif gridcerf_gas_cc_no-ccs_pond.tif gridcerf_gas_cc_no-ccs_recirculating.tif gridcerf_gas_turbine_dry.tif gridcerf_gas_turbine_oncethrough.tif gridcerf_gas_turbine_pond.tif gridcerf_gas_turbine_recirculating.tif gridcerf_nuclear_gen3_oncethrough.tif gridcerf_nuclear_gen3_pond.tif gridcerf_nuclear_gen3_recirculating.tif gridcerf_refinedliquids_cc_ccs_dry.tif gridcerf_refinedliquids_cc_ccs_oncethrough.tif gridcerf_refinedliquids_cc_ccs_recirculating.tif gridcerf_refinedliquids_cc_no-ccs_dry.tif gridcerf_refinedliquids_cc_no-ccs_oncethrough.tif gridcerf_refinedliquids_cc_no-ccs_recirculating.tif gridcerf_refinedliquids_ct_dry.tif gridcerf_refinedliquids_ct_oncethrough.tif gridcerf_refinedliquids_ct_pond.tif gridcerf_refinedliquids_ct_recirculating.tif gridcerf_solar_csp_centralized_dry-hybrid.tif gridcerf_solar_csp_centralized_recirculating.tif gridcerf_solar_pv_centralized.tif gridcerf_wind_onshore_hubheight080m.tif gridcerf_wind_onshore_hubheight110m.tif gridcerf_wind_onshore_hubheight140m.tif

Reference Data:

Important Note: This item is in mature support as of June 2024 and will be retired in December 2026. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. Areas protected from conversion include areas that are permanently protected and managed for biodiversity such as Wilderness Areas and National Parks. In addition to protected lands, portions of areas protected from conversion includes multiple-use lands that are subject to extractive uses such as mining, logging, and off-highway vehicle use. These areas are managed to maintain a mostly undeveloped landscape including many areas managed by the Bureau of Land Management and US Forest Service.The Protected Areas Database of the United States classifies lands into four GAP Status classes. This layer displays lands managed for biodiversity conservation (GAP Status 1 and 2) and multiple-use lands (GAP Status 3). Dataset SummaryPhenomenon Mapped: Protected and multiple-use lands (GAP Status 1, 2, and 3)Units: MetersCell Size: 30.92208102 metersSource Type: ThematicPixel Type: 8-bit unsigned integerData Coordinate System: WGS 1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: 50 United States plus Puerto Rico, the US Virgin Islands, Guam, Northern Mariana Islands and American Samoa.Source: USGS National Gap Analysis Program PAD-US version 3.0Publication Date: July 2022ArcGIS Server URL: https://landscape10.arcgis.com/arcgis/This layer displays protected areas from the Protected Areas Database of the United States version 3.0 created by the USGS National Gap Analysis Program. This layer displays areas managed for biodiversity where natural disturbances are allowed to proceed or are mimicked by management (GAP Status 1), areas managed for biodiversity where natural disturbance is suppressed (GAP Status 2), and multiple-use lands where extract activities are allowed (GAP Status 3). The source data for this layer are available here. A feature layer published from this dataset is also available.The polygon vector layer was converted to raster layers using the Polygon to Raster Tool using the National Elevation Dataset 1 arc second product as a snap raster.The service behind this layer was published with 8 functions allowing the user to select different views of the service. Other layers created from this service using functions include:USA Protected AreasUSA Unprotected AreasUSA Protected Areas - Gap Status 1-4USA Protected Areas - Gap Status 1USA Protected Areas - Gap Status 2USA Protected Areas - Gap Status 3USA Protected Areas - Gap Status 4What can you do with this layer? This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "Protected from Land Cover Conversion" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "Protected from Land Cover Conversion" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.

From the site: "This raster dataset has been created using the "Floodplains from the PA Explorer CD-ROM edition" for each county in the study area as originated by the Office of Remote Sensing for Earth Resources, Penn State University (see metadata entitled "Bucks FP.doc"). All areas designated in the shapefile were assigned a conservation value of 5. Conservation values were determined by experts gathered by Natural Lands Trust through SmartConservation®. This data set is one of several that have been combined to create an overall aquatic resources conservation value raster for the expanded piedmont ecoregion. Therefore the values were determined as a relative rank, comparable in value only to the other input aquatic resources data. Conservation value ranges from 1 - 10 with 10 being the highest value."

From the site: "This raster dataset has been created using "Pennsylvania Land Cover dated 1992" developed by the EROS Data Center for EPA Federal Region III (see metadata entitled "Land Cover 1992 EPA Federal Region III.pdf") and Landscape Blocks as originated by The Nature Conservancy. Using an inside buffer of 18 meters on the Landscape Blocks a new shapefile was created in order to create a 30 meter area between the blocks to represent roads. Grasslands vegetation cover (Hay/Pasture) from the 1992 land Cover was reclassified and then the road buffered landscape blocks were combined with the raster to assign a value of "0" to areas in the Grasslands land cover that overlap with the road buffers. Acreage of the grasslands vegetation was calculated and the data was reclassified based on conservation value as follows: Acreage Conservation Value 0-25 0 25-160 1 160-250 3 250-400 4 >400 5 Conservation values were determined by experts gathered by Natural Lands Trust through SmartConservation®. This data set is one of several that have been combined to create an overall terrestrial resources conservation value raster for the expanded piedmont ecoregion. Therefore the values were determined as a relative rank, comparable in value only to the other input terrestrial resources data. Conservation value ranges from 1 - 10 with 10 being the highest value."

This raster dataset has been created using inputs including "Networked Streams of Pennsylvania" data as originated by Environmental Resources Research Institute (see metadata entitled "netstreams.htm" and "Pennsylvania Land Cover dated 1992" developed by the EROS Data Center for EPA Federal Region III (see metadata entitled "Land Cover 1992 EPA Federal Region III.pdf"). 100 foot buffers of the networked streams were assigned value based on the Strahler stream order and the potential aquatic habitat values assigned to the 1992 land cover. The average conservation value for riparian buffers of 1-2 ordered streams, 3-5 ordered streams, and 6-12 ordered streams were then calculated for the small watersheds (see metadata entitled "smallsheds.xml"). Each of the above groups were then weighted and added together and quantiled into 10 classes to create an overall Riparian Buffer Quality by small watershed data set. Conservation values were determined by SmartConservation (registerd trademark) methodology. Conservation values were determined by experts gathered by Natural Lands Trust through SmartConservation®. This data set is one of several that have been combined to create an overall aquatic resources conservation value raster for the Expanded Piedmont Ecoregion in Pennsylvania. Therefore the values were determined as a relative rank, comparable in value only to the other input aquatic resources data. Conservation value ranges from 1 - 10 with 10 being the highest value.

In the United States, areas that are protected from development and managed for biodiversity conservation include Wilderness Areas, National Parks, National Wildlife Refuges, and Wild & Scenic Rivers. Understanding the geographic distribution of these protected areas and their level of protection is an important part of landscape-scale planning. The Protected Areas Database of the United States classifies lands into four GAP Status classes. This layer displays the two highest levels of protection GAP Status 1 and 2. These two classes are commonly referred to as protected areas.Dataset SummaryPhenomenon Mapped: Areas protected from development and managed to maintain biodiversity (GAP Status 1 and 2)Units: MetersCell Size: 30.92208102 metersSource Type: DiscretePixel Type: 8-bit unsigned integerData Coordinate System: WGS 1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: 50 United States plus Puerto Rico, the US Virgin Islands, the Northern Mariana Islands and other Pacific Ocean Islands.Source: USGS National Gap Analysis Program PAD-US version 2.1Publication Date: September 2020ArcGIS Server URL: https://landscape10.arcgis.com/arcgis/This layer displays protected areas from the Protected Areas Database of the United States version 2.1 created by the USGS National Gap Analysis Program. This layer displays GAP Status 1, areas managed for biodiversity where natural disturbances are allowed to proceed or are mimicked by management, and GAP Status 2, areas managed for biodiversity where natural disturbance is suppressed. The source data for this layer are available here. A feature layer published from this dataset is also available. The polygon vector layer was converted to raster layers using the Polygon to Raster Tool using the National Elevation Dataset 1 arc second product as a snap raster.The service behind this layer was published with 8 functions allowing the user to select different views of the service. Other layers created from this service using functions include:USA Protected from Land Cover ConversionUSA Unprotected AreasUSA Protected Areas - Gap Status 1-4USA Protected Areas - Gap Status 1USA Protected Areas - Gap Status 2USA Protected Areas - Gap Status 3USA Protected Areas - Gap Status 4What can you do with this Layer? This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "Protected Areas" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "Protected Areas" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.

This raster dataset has been created using the attribute field "Value" from the "Impervious Surface Area for Southeast Pennsylvania, 1985" data as originated by Toby Carlson, Penn State University (see metadata entitled "pa1985isaa_se.htm"). The average of impervious surface was then calculated for the small watersheds (see metadata entitled "smallsheds.xml"). Conservation Values were assigned based on the average impervious surface as follows: Avg. Impervious Surface Value 0-6 10 7-7 9 8-8 8 9-9 7 10-10 6 11-12 5 13-14 4 15-16 3 17-18 2 19-20 1 21-100 0 This data set was created solely to calculate the impervious cover change between 1985 and 2000. Conservation values were determined by experts gathered by Natural Lands Trust through SmartConservation®. This data set is one of several that have been combined to create an overall aquatic resources conservation value raster for the expanded piedmont ecoregion. Therefore the values were determined as a relative rank, comparable in value only to the other input aquatic resources data. Conservation value ranges from 1 - 10 with 10 being the highest value.

Important Note: This item is in mature support as of June 2024 and will be retired in December 2026. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.The Protected Areas Database of the United States provides a comprehensive map of lands protected by government agencies and private land owners. This database combines federal lands with information on state and local government lands and conservation easements on private lands to create a powerful resource for land-use planning.Dataset SummaryPhenomenon Mapped: Areas mapped in the Protected Areas Data base of the United States (GAP Status 1-4)Units: MetersCell Size: 30.92208102 metersSource Type: ThematicPixel Type: 8-bit unsigned integerData Coordinate System: WGS 1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: 50 United States plus Puerto Rico, the US Virgin Islands, Guam, Northern Mariana Islands and American Samoa.Source: USGS National Gap Analysis Program PAD-US version 3.0Publication Date: July 2022ArcGIS Server URL: https://landscape10.arcgis.com/arcgis/This layer displays lands mapped in Protected Areas Database of the United States version 3.0 created by the USGS National Gap Analysis Program. This layer displays all four GAP Status classes: GAP Status 1 - Areas managed for biodiversity where natural disturbances are allowed to proceedGAP Status 2 - Areas managed for biodiversity where natural disturbance is suppressedGAP Status 3 - Areas protected from land cover conversion but subject to extractive uses such as logging and miningGAP Status 4 - Areas with no known mandate for protectionThe source data for this layer are available here. A feature layer published from this dataset is also available. The polygon vector layer was converted to raster layers using the Polygon to Raster Tool using the National Elevation Dataset 1 arc second product as a snap raster.The service behind this layer was published with 8 functions allowing the user to select different views of the service. Other layers created from this service using functions include:USA Protected AreasUSA Protected from Land Cover ConversionUSA Unprotected AreasUSA Protected Areas - Gap Status 1USA Protected Areas - Gap Status 2USA Protected Areas - Gap Status 3USA Protected Areas - Gap Status 4What can you do with this Layer? This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "Protected Areas" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "Protected Areas" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.

Soil is the foundation of life on earth. More living things by weight live in the soil than upon it. It determines what crops we can grow, what structures we can build, what forests can take root.This layer contains the physical soil variable percent clay (clay).Within the subset of soil that is smaller than 2mm in size, also known as the fine earth portion, clay is defined as particles that are smaller than 0.002mm, making them only visible in an electron microscope. Clay soils contain low amounts of air, and water drains through them very slowly.This layer is a general, medium scale global predictive soil layer suitable for global mapping and decision support. In many places samples of soils do not exist so this map represents a prediction of what is most likely in that location. The predictions are made in six depth ranges by soilgrids.org, funded by ISRIC based in Wageningen, Netherlands.Each 250m pixel contains a value predicted for that area by soilgrids.org from best available data worldwide. Data for percent clay are provided at six depth ranges from the surface to 2 meters below the surface. Each variable and depth range may be accessed in the layer's multidimensional properties.Dataset SummaryPhenomenon Mapped: Proportion of clay particles (< 0.002 mm) in the fine earth fraction in g/100g (%)Cell Size: 250 metersPixel Type: 32 bit float, converted from online data that is 16 Bit Unsigned IntegerCoordinate System: Web Mercator Auxiliary Sphere, projected via nearest neighbor from goode's homolosine land (250m)Extent: World land area except AntarcticaVisible Scale: All scales are visibleNumber of Columns and Rows: 160300, 100498Source: Soilgrids.orgPublication Date: May 2020Data from the soilgrids.org mean predictions for clay were used to create this layer. You may access the percent clay in one of six depth ranges. To select one choose the depth variable in the multidimensional selector in your map client.Mean depth (cm)Actual depth range of data-2.50-5cm depth range-105-15cm depth range-22.515-30cm depth range-4530-60cm depth range-8060-100cm depth range-150100-200cm depth rangeWhat can you do with this Layer?This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map: In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "world soils soilgrids" in the search box and browse to the layer. Select the layer then click Add to Map. In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "world soils soilgrids" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions.This layer is part of the Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.More information about soilgrids layersAnswers to many questions may be found at soilgrids.org (ISRIC) frequently asked questions (faq) page about the data.To make this layer, Esri reprojected the expected value of ISRIC soil grids from soilgrids' source projection (goode's land WKID 54052) to web mercator projection, nearest neighbor, to facilitate online mapping. The resolution in web mercator projection is the same as the original projection, 250m. But keep in mind that the original dataset has been reprojected to make this web mercator version.This multidimensional soil collection serves the mean or expected value for each soil variable as calculated by soilgrids.org. For all other distributions of the soil variable, be sure to download the data directly from soilgrids.org. The data are available in VRT format and may be converted to other image formats within ArcGIS Pro.Accessing this layer's companion uncertainty layerBecause data quality varies worldwide, the uncertainty of the predicted value varies worldwide. A companion uncertainty layer exists for this layer which you can use to qualify the values you see in this map for analysis. Choose a variable and depth in the multidimensional settings of your map client to access the companion uncertainty layer.

Important Note: This item is in mature support as of June 2024 and will be retired in December 2026. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.In the United States, areas that are protected from development and managed for biodiversity conservation include Wilderness Areas, National Parks, National Wildlife Refuges, and Wild & Scenic Rivers. Understanding the geographic distribution of these protected areas and their level of protection is an important part of landscape-scale planning. The Protected Areas Database of the United States classifies lands into four GAP Status classes. This layer displays the two highest levels of protection GAP Status 1 and 2. These two classes are commonly referred to as protected areas.Dataset SummaryPhenomenon Mapped: Areas protected from development and managed to maintain biodiversity (GAP Status 1 and 2)Units: MetersCell Size: 30.92208102 metersSource Type: ThematicPixel Type: 8-bit unsigned integerData Coordinate System: WGS 1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: 50 United States plus Puerto Rico, the US Virgin Islands, Guam, Northern Mariana Islands and American Samoa.Source: USGS National Gap Analysis Program PAD-US version 3.0Publication Date: July 2022ArcGIS Server URL: https://landscape10.arcgis.com/arcgis/This layer displays protected areas from the Protected Areas Database of the United States version 3.0 created by the USGS National Gap Analysis Program. This layer displays GAP Status 1, areas managed for biodiversity where natural disturbances are allowed to proceed or are mimicked by management, and GAP Status 2, areas managed for biodiversity where natural disturbance is suppressed. The source data for this layer are available here. A feature layer published from this dataset is also available. The polygon vector layer was converted to raster layers using the Polygon to Raster Tool using the National Elevation Dataset 1 arc second product as a snap raster.The service behind this layer was published with 8 functions allowing the user to select different views of the service. Other layers created from this service using functions include:USA Protected from Land Cover ConversionUSA Unprotected AreasUSA Protected Areas - Gap Status 1-4USA Protected Areas - Gap Status 1USA Protected Areas - Gap Status 2USA Protected Areas - Gap Status 3USA Protected Areas - Gap Status 4What can you do with this Layer? This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "Protected Areas" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "Protected Areas" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.

This raster dataset has been created using the National Wetlands Inventory originated by the US Fish & Wildlife Service. Conservation values were determined by SmartConservationTM methodology developed by Natural Lands Trust using the attribute field "Attribute" as follows: Old Values New Values U 0 FL; FL/US; RB; RS; SB; UB; US; BB 1 AB; AB/UB; UB/AB; AB/OW; FL/OW; OW 2 UB/EM; UB/FO 4 EM; EM/AB; EM/FL; EM/FO; EM/OW; EM/SS; EM/UB; 10 FO; FO/EM; FO/OW; FO/SS; FO/UB; 10 SS; SS/EM; SS/FO; SS/OW; SS/UB 10 Conservation values were determined by experts gathered by Natural Lands Trust through SmartConservation®. This data set is one of several that have been combined to create an overall aquatic resources conservation value raster for the expanded piedmont ecoregion. Therefore the values were determined as a relative rank, comparable in value only to the other input aquatic resources data. Conservation value ranges from 1 - 10 with 10 being the highest value.

The Federal Flood Risk Management Standard (FFRMS) floodplain was developed by the Federal Emergency Management Agency (FEMA) to improve the resilience of communities and federal assets against the impacts of flooding. Through a series of Federal executive orders, the FFRMS was established (EOs 11988 and 13690); cancelled in 2017 (EO 13807); re-established in 2021 (EO 14030) and also mapped as the FFRMS floodplain; and then cancelled again in 2025 (EO 14148). This FFRMS floodplain data set was provided by FEMA at the request of Maryland Department of the Environment and is the same data available through the Flood Standard Support Tool (https://floodstandard.climate.gov/).3-meter DEM resolution terrain resolution used for this mapping. In some cases, 1-meter DEM data may have been re-sampled to a 3-meter DEM resolution. FEMA did this to create a consistent FFRMS data set nationwide. The primary sources of data used by FEMA to develop the FFRMS floodplain were USGS’s The National Map for terrain and FEMA’s National Flood Hazard Layer for regulatory water surface elevations.In Riverine areas, the FFRMS floodplain is derived from the National Flood Hazard Layer using the methodology developed by the Maryland Department of the Environment for production of the Riverine CRAB using a linear interpolation applied between two riverine cross sections from the National Flood Hazard Layer (FEMA 100-year floodplain). A freeboard (2-feet for the 2-foot freeboard map, and 3-feet for the 3-foot freeboard map) was added to the 100-year floodplain water surface elevation at each riverine cross section in the National Flood Hazard Layer data set. The new elevations were then interpolated to create a freeboard elevation gradient. This elevation gradient was used to map the horizontal extents of the FFRMS floodplain boundary based on topographic data. Additional details can be found here: https://www.fema.gov/sites/default/files/documents/fema_ffrms-data-methodology.pdfThe methodology to develop the coastal FFRMS floodplain (water surface) raster utilized the coastal static Base Flood Elevation values from the National Flood Hazard Layer as the water surface. To create an expanded surface, these water surface values were collected at uniformly spaced points along the boundary of the effective coastal 1%-annual-chance flood mapping and expanded inland utilizing Thiessen polygons. The Thiessen polygons were converted to a raster matching the spatial resolution of the terrain DEM, and the difference between the water surface elevation raster and the terrain was computed. Areas where the water surface raster value was higher than the terrain raster value indicate the area of flooding. Each freeboard value (2-foot and 3-foot) was mapped independently in whole foot increments utilizing the prior, lower freeboard value boundary of flooding to establish an expanded surface. One limitation of this methodology is that wave runup was modeled for the 100-year floodplain but not remodeled at the higher water surface elevation conditions.Please note that the CRAB data set shows inundation depths derived as the height difference between the DEM and the water surface elevation, whereas the FFRMS shows water surface elevations only. Their horizontal extents may be compared, but users should be careful comparing any color coding or depth values provided in the two applications.

Hydric soils are soils that form under conditions of saturation, flooding, or ponding long enough during the growing season to develop anaerobic conditions in the upper part of the soil. Hydric soils are poorly or very poorly drained and under natural conditions, these soils are either saturated or inundated long enough during the growing season to support the growth and reproduction of wetland vegetation. Hydric soils are part of the legal definition for wetlands in the United States and are used to identify wetland areas that require a permit issued by the Army Corps of Engineers under Section 404 of the Clean Water Act prior to any ground disturbing activities. For more information on hydric soils see the Natural Resources Conservation Service’s publication Field Indicators of Hydric Soils in the United States. Dataset SummaryPhenomenon Mapped: Hydric soilsGeographic Extent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Northern Mariana Islands, Republic of Palau, Republic of the Marshall Islands, Federated States of Micronesia, and American Samoa.Projection: Web Mercator Auxiliary SphereData Coordinate System: WKID 5070 USA Contiguous Albers Equal Area Conic USGS version (contiguous US, Puerto Rico, US Virgin Islands), WKID 3338 WGS 1984 Albers (Alaska), WKID 4326 WGS 1984 Decimal Degrees (Guam, Republic of the Marshall Islands, Northern Mariana Islands, Republic of Palau, Federated States of Micronesia, American Samoa, and Hawaii).Units: PercentCell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerSource: Natural Resources Conservation ServiceUpdate Frequency: AnnualPublication Date: December 2024 Data from the gNATSGO database was used to create the layer. This layer is derived from the 30m rasters produced by the Natural Resources Conservation Service (NRCS). The value for hydric class is derived from the gSSURGO map unit aggregated attribute table field Hydric Classification - Presence (hydclprs). What can you do with this layer?This layer is suitable for both visualization and analysis acrossthe ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application. Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "hydric" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "hydric" in the search box, browse to the layer then click OK. In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro. Online you can filter the layer to show subsets of the data using the filter button and the layer"s built-in raster functions. The ArcGIS Living Atlas of the World provides an easy way to explore many otherbeautiful and authoritative maps on hundreds of topics like this one. Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

This dataset contains the global raster files (at 250 m resolution) associated with a manuscript that has been accepted at the journal Geology:The unexpected global distribution of Earth's sediment sources and sinksHarrison K. Martin1,* and Michael P. Lamb11Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, 91125, U.S.A.*hkm@caltech.eduThe paper describes a spatially continuous high-resolution (250 meter) global map of sediment source, bypass, and sink domains. If you use the data in your own research or projects, please include references to the paper above and to this dataset.This repository contains three main items: 1) the global raster map, 2) MATLAB code used to create the map, and 3) reduced intermediate data designed to work with the MATLAB code, so that users can recreate or modify the map locally without downloading and processing all of the original input data. The three items are described below. Most users will only need to download the global map (1).Item (1), the global map, is found in the .zip file: "mask_strat_241022.zip"Items (2) and (3), the MATLAB code and intermediate data files, are found in the .zip file: "Source-to-sink Map EE 241022 public.zip"1) Global raster map: The dataset consists of 60 GeoTIFF tiles, each 12,000 pixels by 12,000 pixels (or fewer for edge tiles). Each pixel is 250 meters by 250 meters. Tiles are in the WGS 84 / Equal Earth Greenwich projection (https://epsg.io/8857). For convenience, also included is a .vrt (Virtual Raster) file, which can be opened in your GIS software of choice to load all tiles at once. Tiles are saved as .tif files containing 8-bit integer values, and are compressed using the PackBits algorithm. This substantially reduces the filesize of the resulting dataset without any loss of information.This dataset was created using a combination of QGIS and Matlab, and the method is described in the supporting information of the above manuscript.Pixel values are as follows:0: Ocean (can be set as the noData value in your GIS software for easier visualization)1: Sink2: Bypass3: Source4: Missing Data2) MATLAB code:This code can be run to reproduce our results. It comes in a folder with three subdirectories used to read the inputs and write TIF raster outputs (same as (1) above) and, optionally, PNGs. There is also a .txt file in there with instructions to run the code. I tried to make it as simple as possible to run. I also tried to design it with scientific computing in mind, i.e., able to be run in reasonable time by lower performance computers. Considering it's making a global map, the memory requirements are fairly small. On my computer, it takes less than ten minutes to reproduce the global map.3) Intermediate files:A folder containing ten tiled intermediate datasets, described in the Supplemental Information of the Geology manuscript. This is the input that the MATLAB code in (2) reads. These files go into the "VRTs" folder in the same directory as the MATLAB code. These files are all standardized, compressed, tiled, rasterized at the right resolution and in the right CRS, etc. This folder, including the code, instructions, and intermediate files, is zipped to 188 MB compared to >5.5 GB if users were to download the original datasets themselves. Please feel free to reach out with any questions!- Harrison MartinPostdoctoral Scholar Research Associate in GeologyCaltechJune 24 2025hkm@caltech.eduhttps://harrison.studies.rocksEDIT (25/06/24): Made repository public, uploaded the code and intermediate files, and expanded the description accordingly.

The USDA National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) is an annual raster, geo-referenced, crop-specific land cover data layer produced using satellite imagery and extensive agricultural ground reference data. The program began in 1997 with limited coverage and in 2008 forward expanded coverage to the entire Continental United States. Please note that no farmer reported data are derivable from the Cropland Data Layer.

A high-resolution (10-meter per pixel) digital elevation model (DEM) was created for the Sacramento-San Joaquin Delta using both bathymetry and topography data. This DEM is the result of collaborative efforts of the U.S. Geological Survey (USGS) and the California Department of Water Resources (DWR). The base of the DEM is from a 10-m DEM released in 2004 and updated in 2005 (Foxgrover and others, 2005) that used Environmental Systems Research Institute(ESRI), ArcGIS Topo to Raster module to interpolate grids from single beam bathymetric surveys collected by DWR, the Army Corp of Engineers (COE), the National Oceanic and Atmospheric Administration (NOAA), and the USGS, into a continuous surface. The Topo to Raster interpolation method was specifically designed to create hydrologically correct DEMs from point, line, and polygon data (Environmental Systems Research Institute, Inc., 2015). Elevation contour lines were digitized based on the single beam point data for control of channel morphology during the interpolation process. Checks were performed to ensure that the interpolated surfaces honored the source bathymetry, and additional contours and(or) point data were added as needed to help constrain the data. The original data were collected in the tidal datum Mean Lower or Low Water (MLLW), or the National Geodetic Vertical Datum of 1929 (NGVD29). All data were converted to NGVD29. The 2005 USGS DEM was updated by DWR, first by converting the DEM to the current modern datum of National Geodetic Vertical Datum of 1988 (NGVD88) and then by following the methodology of the USGS DEM, established for the 2005 DEM (Foxgrover and others, 2005) for adding newly collected single and multibeam bathymetric data. They then included topographic data from lidar surveys, providing the first DEM that included the land/water interface (Wang and Ateljevich, 2012). The USGS further updated and expanded the DWR DEM with the inclusion of USGS interpolated sections of single beam bathymetry data collected by the COE and USGS scientists, expanding the DEM to include the northernmost areas of the Sacramento-San Joaquin Delta, and by making use of a two-meter seamless bathymetric/topographic DEM from the USGS EROS Data Center (2013) of the San Francisco Bay region. The resulting 10-meter USGS DEM encompasses the entirety of Suisun Bay, beginning with the Carquinez Strait in the west, east to California Interstate 5, north following the path of the Yolo Bypass and the Sacramento River up to Knights Landing, and the American River northeast to the Nimbus Dam, and south to areas around Tracy. The DEM incorporates the newest available bathymetry data at the time of release, as well as including, at minimum, a 100-meter band of available topography data adjacent to most shorelines. No data areas within the DEM are areas where no elevation data exists, either due to a gap in the land/water interface, or because lidar was collected over standing water that was then cut out of the DEM.

The Federal Flood Risk Management Standard (FFRMS) floodplain was developed by the Federal Emergency Management Agency (FEMA) to improve the resilience of communities and federal assets against the impacts of flooding. Through a series of Federal executive orders, the FFRMS was established (EOs 11988 and 13690); cancelled in 2017 (EO 13807); re-established in 2021 (EO 14030) and also mapped as the FFRMS floodplain; and then cancelled again in 2025 (EO 14148). This FFRMS floodplain data set was provided by FEMA at the request of Maryland Department of the Environment and is the same data available through the Flood Standard Support Tool (https://floodstandard.climate.gov/).3-meter DEM resolution terrain resolution used for this mapping. In some cases, 1-meter DEM data may have been re-sampled to a 3-meter DEM resolution. FEMA did this to create a consistent FFRMS data set nationwide. The primary sources of data used by FEMA to develop the FFRMS floodplain were USGS’s The National Map for terrain and FEMA’s National Flood Hazard Layer for regulatory water surface elevations.In Riverine areas, the FFRMS floodplain is derived from the National Flood Hazard Layer using the methodology developed by the Maryland Department of the Environment for production of the Riverine CRAB using a linear interpolation applied between two riverine cross sections from the National Flood Hazard Layer (FEMA 100-year floodplain). A freeboard (2-feet for the 2-foot freeboard map, and 3-feet for the 3-foot freeboard map) was added to the 100-year floodplain water surface elevation at each riverine cross section in the National Flood Hazard Layer data set. The new elevations were then interpolated to create a freeboard elevation gradient. This elevation gradient was used to map the horizontal extents of the FFRMS floodplain boundary based on topographic data. Additional details can be found here: https://www.fema.gov/sites/default/files/documents/fema_ffrms-data-methodology.pdfThe methodology to develop the coastal FFRMS floodplain (water surface) raster utilized the coastal static Base Flood Elevation values from the National Flood Hazard Layer as the water surface. To create an expanded surface, these water surface values were collected at uniformly spaced points along the boundary of the effective coastal 1%-annual-chance flood mapping and expanded inland utilizing Thiessen polygons. The Thiessen polygons were converted to a raster matching the spatial resolution of the terrain DEM, and the difference between the water surface elevation raster and the terrain was computed. Areas where the water surface raster value was higher than the terrain raster value indicate the area of flooding. Each freeboard value (2-foot and 3-foot) was mapped independently in whole foot increments utilizing the prior, lower freeboard value boundary of flooding to establish an expanded surface. One limitation of this methodology is that wave runup was modeled for the 100-year floodplain but not remodeled at the higher water surface elevation conditions.Please note that the CRAB data set shows inundation depths derived as the height difference between the DEM and the water surface elevation, whereas the FFRMS shows water surface elevations only. Their horizontal extents may be compared, but users should be careful comparing any color coding or depth values provided in the two applications.

In this course, you will explore a variety of open-source technologies for working with geosptial data, performing spatial analysis, and undertaking general data science. The first component of the class focuses on the use of QGIS and associated technologies (GDAL, PROJ, GRASS, SAGA, and Orfeo Toolbox). The second component of the class introduces Python and associated open-source libraries and modules (NumPy, Pandas, Matplotlib, Seaborn, GeoPandas, Rasterio, WhiteboxTools, and Scikit-Learn) used by geospatial scientists and data scientists. We also provide an introduction to Structured Query Language (SQL) for performing table and spatial queries. This course is designed for individuals that have a background in GIS, such as working in the ArcGIS environment, but no prior experience using open-source software and/or coding. You will be asked to work through a series of lecture modules and videos broken into several topic areas, as outlined below. Fourteen assignments and the required data have been provided as hands-on opportunites to work with data and the discussed technologies and methods. If you have any questions or suggestions, feel free to contact us. We hope to continue to update and improve this course. This course was produced by West Virginia View (http://www.wvview.org/) with support from AmericaView (https://americaview.org/). This material is based upon work supported by the U.S. Geological Survey under Grant/Cooperative Agreement No. G18AP00077. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey. After completing this course you will be able to: apply QGIS to visualize, query, and analyze vector and raster spatial data. use available resources to further expand your knowledge of open-source technologies. describe and use a variety of open data formats. code in Python at an intermediate-level. read, summarize, visualize, and analyze data using open Python libraries. create spatial predictive models using Python and associated libraries. use SQL to perform table and spatial queries at an intermediate-level.

Important Note: This item is in mature support as of April 2024 and will be retired in December 2026. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.The combined processes of evaporation and transpiration, known as evapotranspiration (ET), plays a key role in the water cycle. Precipitation that falls on land can either run off in streams and rivers, soak into the ground, or return to the atmosphere through evapotranspiration. Water that evaporates returns directly to the atmosphere while water that is transpired is taken up by plant roots and lost to the atmosphere through the leaves.Evapotranspiration data can be used to calculate regional water and energy balance and soil water status and provides key information for water resource management. Potential evapotranspiration, the amount of ET that would occur if soil moisture were not limited, is a purely meteorological characteristic, based on air temperature, solar radiation, and wind speed. Actual evapotranspiration also depends on water availability, so it might occur at very close to the potential rate in a rainforest, but be much lower in a desert despite the higher potential there.Dataset SummaryPhenomenon Mapped: EvapotranspirationUnits: Millimeters per yearCell Size: 927.6623821756539 metersSource Type: ContinuousPixel Type: 16-bit unsigned integerData Coordinate System: Web Mercator Auxiliary SphereExtent: Global Source: University of Montana Numerical Terradynamic Simulation GroupPublication Date: March 10, 2015ArcGIS Server URL: https://landscape6.arcgis.com/arcgis/This layer provides access to a 1km cell sized raster of average annual evaporative loss from the land surface, measured in mm/year. Data are from the MOD16 Global Evapotranspiration Product, which is derived from MODIS imagery by a team of researchers at the University of Montana. This algorithm, which involves estimating land surface temperature and albedo and using them to solve the Penman-Monteith equation, is not valid over urban or barren land so these are shown as NoData, as is any open water. For all other pixels, the algorithm was used to estimate evapotranspiration for every 8-day period from 2000 to 2014 and these estimates have been averaged together to come up with the annual normal. You can also get access to the monthly totals using the MODIS Toolbox.What can you do with this Layer? This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "evapotranspiration" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "evapotranspiration" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.

Aim: Effective management decisions depend on knowledge of species distribution and habitat use. Maps generated from species distribution models are important in predicting previously unknown occurrences of protected species. However, if populations are seasonally dynamic or locally adapted, failing to consider population level differences could lead to erroneous determinations of occurrence probability and ineffective management. The study goal was to model the distribution of a species of special concern, Townsend’s big-eared bats (Corynorhinus townsendii), in California. We incorporate seasonal and spatial differences to estimate the distribution under current and future climate conditions. Methods: We built species distribution models using all records from statewide roost surveys and by subsetting data to seasonal colonies, representing different phenological stages, and to Environmental Protection Agency Level III Ecoregions to understand how environmental needs vary based on these factors. We projected species’ distribution for 2061-2080 in response to low and high emissions scenarios and calculated the expected range shifts. Results: The estimated distribution differed between the combined (full dataset) and phenologically-explicit models, while ecoregion-specific models were largely congruent with the combined model. Across the majority of models, precipitation was the most important variable predicting the presence of C. townsendii roosts. Under future climate scnearios, distribution of C. townsendii is expected to contract throughout the state, however suitable areas will expand within some ecoregions. Main conclusion: Comparison of phenologically-explicit models with combined models indicate the combined models better predict the extent of the known range of C. townsendii in California. However, life history-explicit models aid in understanding of different environmental needs and distribution of their major phenological stages. Differences between ecoregion-specific and statewide predictions of habitat contractions highlight the need to consider regional variation when forecasting species’ responses to climate change. These models can aid in directing seasonally explicit surveys and predicting regions most vulnerable under future climate conditions. Methods Study area and survey data The study area covers the U.S. state of California, which has steep environmental gradients that support an array of species (Dobrowski et al. 2011). Because California is ecologically diverse, with regions ranging from forested mountain ranges to deserts, we examined local environmental needs by modeling at both the state-wide and ecoregion scale, using U.S. Environmental Protection Agency (EPA) Level III ecoregion designations and there are thirteen Level III ecoregions in California (Table S1.1) (Griffith et al. 2016). Species occurrence data used in this study were from a statewide survey of C. townsendii in California conducted by Harris et al. (2019). Briefly, methods included field surveys from 2014-2017 following a modified bat survey protocol to create a stratified random sampling scheme. Corynorhinus townsendii presence at roost sites was based on visual bat sightings. From these survey efforts, we have visual occurrence data for 65 maternity roosts, 82 hibernation roosts (hibernacula), and 91 active-season non-maternity roosts (transition roosts) for a total of 238 occurrence records (Figure 1, Table S1.1). Ecogeographical factors We downloaded climatic variables from WorldClim 2.0 bioclimatic variables (Fick & Hijmans, 2017) at a resolution of 5 arcmin for broad-scale analysis and 30 arcsec for our ecoregion-specific analyses. To calculate elevation and slope, we used a digital elevation model (USGS 2022) in ArcGIS 10.8.1 (ESRI, 2006). The chosen set of environmental variables reflects knowledge on climatic conditions and habitat relevant to bat physiology, phenology, and life history (Rebelo et al. 2010, Razgour et al. 2011, Loeb and Winters 2013, Razgour 2015, Ancillotto et al. 2016). To trim the global environmental variables to the same extent (the state of California), we used the R package “raster” (Hijmans et al. 2022). We performed a correlation analysis on the raster layers using the “layerStats” function and removed variables with a Pearson’s coefficient > 0.7 (see Table 1 for final model variables). For future climate conditions, we selected three general circulation models (GCMs) based on previous species distribution models of temperate bat species (Razgour et al. 2019) [Hadley Centre Global Environment Model version 2 Earth Systems model (HadGEM3-GC31_LL; Webb, 2019), Institut Pierre-Simon Laplace Coupled Model 6th Assessment Low Resolution (IPSL-CM6A-LR; Boucher et al., 2018), and Max Planck Institute for Meteorology Earth System Model Low Resolution (MPI-ESM1-2-LR; Brovkin et al., 2019)] and two contrasting greenhouse concentration trajectories (Shared Socio-economic Pathways (SSPs): a steady decline pathway with CO2 concentrations of 360 ppmv (SSP1-2.6) and an increasing pathway with CO2 reaching around 2,000 ppmv (SSP5-8.5) (IPCC6). We modeled distribution for present conditions future (2061-2080) time periods. Because one aim of our study was to determine the consequences of changing climate, we changed only the climatic data when projecting future distributions, while keeping the other variables constant over time (elevation, slope). Species distribution modeling We generated distribution maps for total occurrences (maternity + hibernacula + transition, hereafter defined as “combined models”), maternity colonies , hibernacula, and transition roosts. To estimate the present and future habitat suitability for C. townsendii in California, we used the maximum entropy (MaxEnt) algorithm in the “dismo” R package (Hijmans et al. 2021) through the advanced computing resources provided by Texas A&M High Performance Research Computing. We chose MaxEnt to aid in the comparisons of state-wide and ecoregion-specific models as MaxEnt outperforms other approaches when using small datasets (as is the case in our ecoregion-specific models). We created 1,000 background points from random points in the environmental layers and performed a 5-fold cross validation approach, which divided the occurrence records into training (80%) and testing (20%) datasets. We assessed the performance of our models by measuring the area under the receiver operating characteristic curve (AUC; Hanley & McNeil, 1982), where values >0.5 indicate that the model is performing better than random, values 0.5-0.7 indicating poor performance, 0.7-0.9 moderate performance and values of 0.9-1 excellent performance (BCCVL, Hallgren et al., 2016). We also measured the maximum true skill statistic (TSS; Allouche, Tsoar, & Kadmon, 2006) to assess model performance. The maxTSS ranges from -1 to +1:values <0.4 indicate a model that performs no better than random, 0.4-0.55 indicates poor performance, (0.55-0.7) moderate performance, (0.7-0.85) good performance, and values >0.80 indicate excellent performance (Samadi et al. 2022). Final distribution maps were generated using all occurrence records for each region (rather than the training/testing subset), and the models were projected onto present and future climate conditions. Additionally, because the climatic conditions of the different ecoregions of California vary widely, we generated separate models for each ecoregion in an attempt to capture potential local effects of climate change. A general rule in species distribution modeling is that the occurrence points should be 10 times the number of predictors included in the model, meaning that we would need 50 occurrences in each ecoregion. One common way to overcome this limitation is through the ensemble of small models (ESMs) (Breiner et al. 2015., 2018; Virtanen et al. 2018; Scherrer et al. 2019; Song et al. 2019) included in ecospat R package (references). For our ESMs we implemented MaxEnt modeling, and the final ensemble model was created by averaging individual bivariate models by weighted performance (AUC > 0.5). We also used null model significance testing with to evaluate the performance of our ESMs (Raes and Ter Steege 2007). To perform null model testing we compared AUC scores from 100 null models using randomly generated presence locations equal to the number used in the developed distribution model. All ecoregion models outperformed the null expectation (p<0.002). Estimating range shifts For each of the three GCMs and each RCP scenario, we converted the probability distribution map into a binary map (0=unsuitable, 1=suitable) using the threshold that maximizes sensitivity and specificity (Liu et al. 2016). To create the final maps for each SSP scenario, we summed the three binary GCM layers and took a consensus approach, meaning climatically suitable areas were pixels where at least two of the three models predicted species presence (Araújo and New 2007, Piccioli Cappelli et al. 2021). We combined the future binary maps (fmap) and the present binary maps (pmap) following the formula fmap x 2 + pmap (from Huang et al., 2017) to produce maps with values of 0 (areas not suitable), 1 (areas that are suitable in the present but not the future), 2 (areas that are not suitable in the present but suitable in the future), and 3 (areas currently suitable that will remain suitable) using the raster calculator function in QGIS. We then calculated the total area of suitability, area of maintenance, area of expansion, and area of contraction for each binary model using the “BIOMOD_RangeSize” function in R package “biomod2” (Thuiller et al. 2021).