This dataset is called the Gridded SSURGO (gSSURGO) Database and is derived from the Soil Survey Geographic (SSURGO) Database. SSURGO is generally the most detailed level of soil geographic data developed by the National Cooperative Soil Survey (NCSS) in accordance with NCSS mapping standards. The tabular data represent the soil attributes, and are derived from properties and characteristics stored in the National Soil Information System (NASIS). The gSSURGO data were prepared by merging traditional SSURGO digital vector map and tabular data into a Conterminous US-wide extent, and adding a Conterminous US-wide gridded map layer derived from the vector, plus a new value added look up (valu) table containing "ready to map" attributes. The gridded map layer is offered in an ArcGIS file geodatabase raster format. The raster and vector map data have a Conterminous US-wide extent. The raster map data have a 30 meter cell size. Each cell (and polygon) is linked to a map unit identifier called the map unit key. A unique map unit key is used to link to raster cells and polygons to attribute tables, including the new value added look up (valu) table that contains additional derived data. The value added look up (valu) table contains attribute data summarized to the map unit level using best practice generalization methods intended to meet the needs of most users. The generalization methods include map unit component weighted averages and percent of the map unit meeting a given criteria. The Gridded SSURGO dataset was created for use in national, regional, and state-wide resource planning and analysis of soils data. The raster map layer data can be readily combined with other national, regional, and local raster layers, e.g., National Land Cover Database (NLCD), the National Agricultural Statistics Service (NASS) Crop Data Layer, or the National Elevation Dataset (NED).

The gSSURGO dataset provides detailed soil survey mapping in raster format with ready-to-map attributes organized in statewide tiles for desktop GIS. gSSURGO is derived from the official Soil Survey Geographic (SSURGO) Database. SSURGO generally has the most detailed level of soil geographic data developed by the National Cooperative Soil Survey (NCSS) in accordance with NCSS mapping standards. The tabular data represent the soil attributes and are derived from properties and characteristics stored in the National Soil Information System (NASIS).

The gSSURGO data were prepared by merging the traditional vector-based SSURGO digital map data and tabular data into statewide extents, adding a statewide gridded map layer derived from the vector layer, and adding a new value-added look up table (valu) containing ready-to-map attributes. The gridded map layer is in an ArcGIS file geodatabase in raster format, thus it has the capacity to store significantly more data and greater spatial extents than the traditional SSURGO product. The raster map data have a 10-meter cell size that approximates the vector polygons in an Albers Equal Area projection. Each cell (and polygon) is linked to a map unit identifier called the map unit key. A unique map unit key is used to link the raster cells and polygons to attribute tables.

For more information, see the gSSURGO webpage: https://www.nrcs.usda.gov/resources/data-and-reports/description-of-gridded-soil-survey-geographic-gssurgo-database

This dataset is called the Gridded SSURGO (gSSURGO) Database and is derived from the Soil Survey Geographic (SSURGO) Database. SSURGO is generally the most detailed level of soil geographic data developed by the National Cooperative Soil Survey (NCSS) in accordance with NCSS mapping standards. The tabular data represent the soil attributes, and are derived from properties and characteristics stored in the National Soil Information System (NASIS). The gSSURGO data were prepared by merging traditional SSURGO digital vector map and tabular data into State-wide extents, and adding a State-wide gridded map layer derived from the vector, plus a new value added look up (valu) table containing "ready to map" attributes. The gridded map layer is offered in an ArcGIS file geodatabase raster format. The raster and vector map data have a State-wide extent. The raster map data have a 10 meter cell size that approximates the vector polygons in an Albers Equal Area projection. Each cell (and polygon) is linked to a map unit identifier called the map unit key. A unique map unit key is used to link to raster cells and polygons to attribute tables, including the new value added look up (valu) table that contains additional derived data. The value added look up (valu) table contains attribute data summarized to the map unit level using best practice generalization methods intended to meet the needs of most users. The generalization methods include map unit component weighted averages and percent of the map unit meeting a given criteria. Resources in this dataset:Resource Title: gSSURGO downloads Page. File Name: Web Page, url: https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcs142p2_053628#value Download gSSURGO Databases

Other resources include introduction to gSSURGO, User Guide (PDF; 4.22 MB), SSURGO/gSSURGO ArcTools, Valu1 (Value Added Look Up) Table, Metadata, Recommended Data Citations, Technical Information, Sample gSSURGO Map Themes

This dataset contains the common Map Unit attributes for each polygon within the gSSURGO database plus NRCS derived attributes from a data summary table called the National Valu Added Look Up (valu) Table #1. It is comprised of 57 pre-summarized or "ready to map" derived soil survey geographic database attributes including soil organic carbon, available water storage, crop productivity indices, crop root zone depths, available water storage within crop root zone depths, drought vulnerable soil landscapes, and potential wetland soil landscapes. Related metadata values for themes are included. These attribute data are pre-summarized to the map unit level using best practice generalization methods intended to meet the needs of most users. The generalization methods include map unit component weighted averages and percent of the map unit meeting a given criteria. These themes were prepared to better meet the mapping needs of users of soil survey information and can be used with both SSURGO and Gridded SSURGO (gSSURGO) datasets. Gridded SSURGO (gSSURGO) Database is derived from the official Soil Survey Geographic (SSURGO) Database. SSURGO is generally the most detailed level of soil geographic data developed by the National Cooperative Soil Survey (NCSS) in accordance with NCSS mapping standards. The tabular data represent the soil attributes, and are derived from properties and characteristics stored in the National Soil Information System (NASIS). The gSSURGO data were prepared by merging traditional SSURGO digital vector map and tabular data into State-wide extents, and adding a State-wide gridded map layer derived from the vector, plus a new value added look up (valu) table containing "ready to map" attributes. The gridded map layer is offered in an ArcGIS file geodatabase raster format. The raster and vector map data have a State-wide extent. The raster map data have a 10 meter cell size that approximates the vector polygons in an Albers Equal Area projection. Each cell (and polygon) is linked to a map unit identifier called the map unit key. A unique map unit key is used to link to raster cells and polygons to attribute tables, including the new value added look up (valu) table that contains additional derived data.VALU Table Content:The map unit average Soil Organic Carbon (SOC) values are given in units of g C per square meter for eleven standard layer or zone depths. The average thickness of soil map unit component horizons used in these layer/zone calcuations is also included. The standard layers include: 0-5cm, 5-20cm, 20-50cm, 50-100cm, 100-150cm, and 150-150+cm (maximum reported soil depth). The standard zones include: 0-5cm (also a standard layer), o-20cm, 0-30cm, 0-100cm, and 0-150+cm (full reported soil depth). Zero cm represents the soil surface.The map unit average Available Water Storage (AWS) values are given in units of millimeters for eleven standard layer or zone depths. The average thickness of soil map unit component horizons used in these layer/zone calcuations is also included. The standard layers include: 0-5cm, 5-20cm, 20-50cm, 50-100cm, 100-150cm, and 150-150+cm (maximum reported soil depth). The standard zones include: 0-5cm (also a standard layer), 0-20cm, 0-30cm, 0-100cm, and 0-150+cm (full reported soil depth). Zero cm represents the soil surface.The map unit average National Commodity Crop Productivity Index (NCCPI) values (low index values indicate low productivity and high index values indicate high productivity) are provided for major earthy components. NCCPI values are included for corn/soybeans, small grains, and cotton crops. Of these crops, the highest overall NCCPI value is also identified. Earthy components are those soil series or higher level taxa components that can support crop growth. Major components are those soil components where the majorcompflag = 'Yes' in the SSURGO component table. A map unit percent composition for earthy major components is provided. See Dobos, R. R., H. R. Sinclair, Jr, and M. P. Robotham. 2012. National Commodity Crop Productivity Index (NCCPI) User Guide, Version 2. USDA-NRCS. Available at: ftp://ftp-fc.sc.egov.usda.gov/NSSC/NCCPI/NCCPI_user_guide.pdfThe map unit average root zone depth values for commodity crops are given in centimeters for major earthy components. Criteria for root-limiting soil depth include: presence of hard bedrock, soft bedrock, a fragipan, a duripan, sulfuric material, a dense layer, a layer having a pH of less than 3.5, or a layer having an electrical conductivity of more than 12 within the component soil profile. If no root-restricting zone is identified, a depth of 150 cm is used to approximate the root zone depth (Dobos et al., 2012). The map unit average available water storage within the root zone depth for major earthy components value is given in millimeters.Drought vulnerable soil landscapes comprise those map units that have available water storage within the root zone for commodity crops that is less than or equal to 6 inches (152 mm) expressed as "1" for a drought vulnerable soil landscape map unit or "0" for a nondroughty soil landscape map unit or NULL for miscellaneous areas (includes water bodies).The potential wetland soil landscapes (PWSL version 1) information is given as the percentage of the map unit (all components) that meet the criteria for a potential wetland soil landscape. See table column (field) description for criteria details. If water was determined to account for 80 or greater percent of a map unit, a value of 999 was used to indicate a water body. This is not a perfect solution, but is helpful to identifying a general water body class for mapping.The map unit sum of the component percentage representative values is also provided as useful metadata. For all valu table columns, NULL values are presented where data are incomplete or not available. How NoData or NULL values and incomplete data were handled during VALU table SOC and AWS calculations:The gSSURGO calculations for SOC and AWS as reported in the VALU table use the following data checking and summarization rules. The guiding principle was to only use the official data in the SSURGO database, and not to make assumptions in case there were some data entry errors. However, there were a few exceptions to this principle if there was a good reason for a Null value in a critical variable, or to accommodate the data coding conventions used in some soil surveys.Horizon depths considerations:If the depth to the top of the surface horizon was missing, but otherwise the horizon depths were all okay, then the depth to the top of the surface horizon (hzdept_r) was set to zero.If the depth to the bottom of the last horizon was missing, and the horizon represented bedrock or had missing bulk density, the depth to the bottom was set to equal to the depth to the top of the same horizon (hzdepb_r = hzdept_r), effectively giving the horizon zero thickness (and thus zero SOC or AWS), but not blocking calculation of other horizons in the profile due to horizon depth errors.Other types of horizon depth errors were considered uncorrectable, and led to all horizon depths for the component being set to a NoData value, effectively eliminating the component from the analysis. The errors included gaps or overlaps in the horizon depths of the soil profile, other cases of missing data for horizon depths, including missing data for the bottom depth of the last horizon if the soil texture information did not indicate bedrock and a bulk density value was coded. The SOC or AWS values were effectively set to zero for components eliminated in this way, so the values at the map unit level could be an underestimate for some soils.Horizon rock fragment considerations:Part of the algorithm for calculating the SOC requires finding the volume of soil that is not rock. This requires three SSURGO variables that indicate rock fragments (fraggt10_r, frag3to10_r, and sieveno10_r). If the soil is not organic, and any of these are missing, then the ratio of the volume of soil fines to the total soil volume was set to “NoData†, and the SOC results were coded as “NoData†and effectively set to zero for the horizon. If the soil is organic, then it may be logical that no measurement of rock fragments was made, and default values for the “zero rock†situation was assumed for these variables (i.e., fraggt10_r = 0, frag3to10_r = 0, sieveno10_r = 100). Organic soils were identified by an “O†in the horizon designator or the texture code represented “Peat†, “Muck†or “Decomposed Plant Material†. If all three of the fragment variables were present, but indicated more than 100% rock, then 100% rock was assumed (zero volume of soil and thus zero for SOC). The rock fragment variables do not influence the AWS calculation because rock content is already accounted for in the available water capacity (awc_r) variable at the horizon level.Horizon to component summary:To summarize data from the horizon level to the component level, the evaluation proceeded downward from the surface. If a valid value for AWS could not be calculated for any horizon, then the result for that horizon and all deeper horizons was set to NoData. The same rule was separately applied to the SOC calculation, so it was possible to have results for SOC but not AWS, or vice versa.Component to mapunit summary:To summarize data from the component level to the map unit level, the component percentages must be valid. There are tests both of the individual component percentage (comppct_r) data, and also of the sum of the component percentages at the map unit level (mu_sum_comppct_r). For the gSSURGO VALU table, the following rules were applied for the individual components: 1) The comppct_r must be in the range from 0 to 100, inclusive. 2) Individual components with a comppct_r that was Null (nothing coded) were ignored. A zero comppct_r value excludes

These data depict the western United States Map Unit areas as defined by the USDA NRCS. Each Map Unit area contains information on a variety of soil properties and interpretations. The raster is to be joined to the .csv file by the field "mukey." We keep the raster and csv separate to preserve the full attribute names in the csv that would be truncated if attached to the raster. Once joined, the raster can be classified or analyzed by the columns which depict the properties and interpretations. It is important to note that each property has a corresponding component percent column to indicate how much of the map unit has the dominant property provided. For example, if the property "AASHTO Group Classification (Surface) 0 to 1cm" is recorded as "A-1" for a map unit, a user should also refer to the component percent field for this property (in this case 75). This means that an estimated 75% of the map unit has a "A-1" AASHTO group classification and that "A-1" is the dominant group. The property in the column is the dominant component, and so the other 25% of this map unit is comprised of other AASHTO Group Classifications. This raster attribute table was generated from the "Map Soil Properties and Interpretations" tool within the gSSURGO Mapping Toolset in the Soil Data Management Toolbox for ArcGIS™ User Guide Version 4.0 (https://www.nrcs.usda.gov/wps/PA_NRCSConsumption/download?cid=nrcseprd362255&ext=pdf) from GSSURGO that used their Map Unit Raster as the input feature (https://gdg.sc.egov.usda.gov/). The FY2018 Gridded SSURGO Map Unit Raster was created for use in national, regional, and state-wide resource planning and analysis of soils data. These data were created with guidance from the USDA NRCS. The fields named "*COMPPCT_R" can exceed 100% for some map units. The NRCS personnel are aware of and working on fixing this issue. Take caution when interpreting these areas, as they are the result of some data duplication in the master gSSURGO database. The data are considered valuable and required for timely science needs, and thus are released with this known error. The USDA NRCS are developing a data release which will replace this item when it is available. For the most up to date ssurgo releases that do not include the custom fields as this release does, see https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/home/?cid=nrcs142p2_053628#tools For additional definitions, see https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcs142p2_053627.

Gridded SSURGO (gSSURGO) is similar to the standard product from the United States Department of Agriculture (USDA) Natural Resources Conservation Service (NRCS) Soil Survey Geographic (SSURGO) Database, but is in the Environmental Systems Research Institute, Inc. (ESRI®) file geodatabase format. A file geodatabase has the capacity to store significantly more data and thus greater spatial extents than the traditional SSURGO product. This allows for statewide or even Conterminous United States (CONUS) tiling of data. gSSURGO contains all of the original soil attribute tables in SSURGO. All spatial data are stored within the geodatabase instead of externally as separate shape files. Both SSURGO and gSSURGO are considered products of the National Cooperative Soil Survey (NCSS). An important addition to the new format is a 10-meter raster (MapunitRaster_10m) of the map unit soil polygons feature class, which provides statewide coverage in a single layer. The CONUS database includes a 30-meter raster because of size constraints. This new addition provides greater performance and important analysis capabilities to users of soils data. Statewide tiles consist of soil survey areas needed to provide full coverage for a given State. In order to create a true statewide soils layer, some clipping of excess soil survey area gSSURGO data may be required. The new format also includes a national Value Added Look Up (valu) Table that has several new “ready to map” attributes.Other Documents to Reference:gSSURGO FactsheetgSSURGO User Guide ArcMap version 2.4Soil Data Development Toolbox User Guide v5 for ArcMapgSSURGO Mapping Detailed GuidegSSURGO Valu1 table column descriptions

This digital data release consists of seven national data files of area- and depth-weighted averages of select soil attributes for every available county in the conterminous United States and the District of Columbia as of March 2014. The files are derived from Natural Resources Conservations Service’s (NRCS) Soil Survey Geographic database (SSURGO). The data files can be linked to the raster datasets of soil mapping unit identifiers (MUKEY) available through the NRCS’s Gridded Soil Survey Geographic (gSSURGO) database (http://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcs142p2_053628). The associated files, named DRAINAGECLASS, HYDRATING, HYDGRP, HYDRICCONDITION, LAYER, TEXT, and WTDEP are area- and depth-weighted average values for selected soil characteristics from the SSURGO database for the conterminous United States and the District of Columbia. The SSURGO tables were acquired from the NRCS on March 5, 2014. The soil characteristics in the DRAINAGE table are drainage class (DRNCLASS), which identifies the natural drainage conditions of the soil and refers to the frequency and duration of wet periods. The soil characteristics in the HYDRATING table are hydric rating (HYDRATE), a yes/no field that indicates whether or not a map unit component is classified as a "hydric soil". The soil characteristics in the HYDGRP table are the percentages for each hydrologic group per MUKEY. The soil characteristics in the HYDRICCONDITION table are hydric condition (HYDCON), which describes the natural condition of the soil component. The soil characteristics in the LAYER table are available water capacity (AVG_AWC), bulk density (AVG_BD), saturated hydraulic conductivity (AVG_KSAT), vertical saturated hydraulic conductivity (AVG_KV), soil erodibility factor (AVG_KFACT), porosity (AVG_POR), field capacity (AVG_FC), the soil fraction passing a number 4 sieve (AVG_NO4), the soil fraction passing a number 10 sieve (AVG_NO10), the soil fraction passing a number 200 sieve (AVG_NO200), and organic matter (AVG_OM). The soil characteristics in the TEXT table are percent sand, silt, and clay (AVG_SAND, AVG_SILT, and AVG_CLAY). The soil characteristics in the WTDEP table are the annual minimum water table depth (WTDEP_MIN), available water storage in the 0-25 cm soil horizon (AWS025), the minimum water table depth for the months April, May and June (WTDEPAMJ), the available water storage in the first 25 centimeters of the soil horizon (AWS25), the dominant drainage class (DRCLSD), the wettest drainage class (DRCLSWET), and the hydric classification (HYDCLASS), which is an indication of the proportion of the map unit, expressed as a class, that is "hydric", based on the hydric classification of a given MUKEY. (See Entity_Description for more detail). The tables were created with a set of arc macro language (aml) and awk (awk was created at Bell Labsin the 1970s and its name is derived from the first letters of the last names of its authors – Alfred Aho, Peter Weinberger, and Brian Kernighan) scripts. Send an email to mewieczo@usgs.gov to obtain copies of the computer code (See Process_Description.) The methods used are outlined in NRCS's "SSURGO Data Packaging and Use" (NRCS, 2011). The tables can be related or joined to the gSSURGO rasters of MUKEYs by the item 'MUKEY.' Joining or relating the tables to a MUKEY grid allows the creation of grids of area- and depth-weighted soil characteristics. A 90-meter raster of MUKEYs is provided which can be used to produce rasters of soil attributes. More detailed resolution rasters are available through NRCS via the link above.

The gridded National Soil Survey Geographic Database (gNATSGO) is a USDA-NRCS (Natural Resources Conservation Service) Soil & Plant Science Division (SPSD) composite ESRI file geodatabase that provides complete coverage of the best available soils information for all areas of the United States and Island Territories. It was created by combining data from the Soil Survey Geographic Database (SSURGO), State Soil Geographic Database (STATSGO2), and Raster Soil Survey Databases (RSS) into a single seamless ESRI file geodatabase. The gNATSGO database contains a 10-meter raster of the soil map units and 70 related tables of soil properties and interpretations. It is designed to work with the SPSD gSSURGO ArcTools. Users can create full coverage thematic maps and grids of soil properties and interpretations for large geographic areas, such as the extent of a State or the conterminous United States. SSURGO is the SPSD flagship soils database that has over 100 years of field-validated detailed soil mapping data. SSURGO contains soils information for more than 90 percent of the United States and island territories, but unmapped land remains. The current completion status of SSURGO mapping is displayed (PDF). STATSGO2 is a general soil map that has soils data for all of the United States and island territories, but the data is not as detailed as the SSURGO data. The Raster Soil Surveys (RSSs) are the next generation soil survey databases developed using advanced digital soil mapping methods. The first version of gNATSGO was created in 2019. It is composed primarily of SSURGO data, but STATSGO2 data was used to fill in the gaps. Three RSSs have been published as of 2019. These were merged into the gNATSGO after combining the SSURGO and STATSGO2 data. The extent of RSS is expected to increase in the coming years. Resources in this dataset:Resource Title: Website Pointer for Gridded National Soil Survey Geographic Database (gNATSGO). File Name: Web Page, url: https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcseprd1464625 The gNATSGO website provides an Overview slide presentation, Download links for gNATSGO databases (CONUS or States), ArcTools, Metadata, Technical Information, and Recommended Data Citations.

The "Gridded Soil Survey Geographic (gSSURGO) Database State-tile Package" product is derived from the Soil Survey Geographic (2.2) Database dated October 1, 2019. The gSSURGO data were prepared by merging SSURGO digital vector map and tabular data into State-wide extents, and adding a State-wide gridded map layer derived from the vector, plus a new value added look up (valu) table that contains “ready to map attributes”.

SSURGO is generally the most detailed level of soil geographic data developed by the National Cooperative Soil Survey (NCSS) in accordance with NCSS mapping standards. The tabular data represent the soil attributes and are derived from properties and characteristics stored in the National Soil Information System (NASIS). The gSSURGO data were prepared by merging traditional SSURGO digital vector map and tabular data into State-wide extents, and adding a State-wide gridded map layer derived from the vector, plus a new value added look up (valu) table containing "ready to map” attributes. The gridded map layer is offered in an ArcGIS™ 10.0 file geodatabase raster format.

The raster and vector map data have a State-wide extent. The raster map data have a 10 meter cell size that approximates the vector polygons in an Albers Equal Area projection. Each cell (and polygon) is linked to a map unit identifier called the map unit key (MUKEY). A unique map unit key is used to link raster cells and polygons to attribute tables, including the new value added look up (valu) table that contains additional derived data.

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The Geospatial Data Gateway (GDG) provides access to a map library of over 100 high resolution vector and raster layers in the Geospatial Data Warehouse. It is the One Stop Source for environmental and natural resources data, at any time, from anywhere, to anyone. It allows you to choose your area of interest, browse and select data, customize the format, then review and download. This service is made available through a close partnership between the three Service Center Agencies (SCA); Natural Resources Conservation Service (NRCS), Farm Service Agency (FSA) and Rural Development (RD).

From gridded National Soil Survey Geographic Database (gNATSGO). Used Soil Data Development Toolbox > gSSURGO Mapping Toolset > Create Soil Map Tool, Exported Data Layer to TIFF, and Used Spatial Analyst > Reclass > Lookup Tool to create this data layer and display the HYDROLGRP_. Follow instructions in "How to Create an On-Demand Soil Property or Interpretation Grid from gNATSGO". Shows sSSURGO data for California. A - sand, loamy sand, sandy loam B - loam, silt, loam or silt C - sandy clay loam D - clay loam, silty clay loam, sandy clay, silty clay, or clay The gridded National Soil Survey Geographic Database (gNATSGO) is a USDA-NRCS Soil & Plant Science Division (SPSD) composite database that provides complete coverage of the best available soils information for all areas of the United States and Island Territories. It was created by combining data from the Soil Survey Geographic Database (SSURGO), State Soil Geographic Database (STATSGO2), and Raster Soil Survey Databases (RSS) into a single seamless ESRI file geodatabase. The state-wide gNATSGO databases contain a 10-meter raster of the soil map units and 70 related tables of soil properties and interpretations. It is designed to work with the SPSD gSSURGO ArcTools. Users can create full coverage thematic maps and grids of soil properties and interpretations for large geographic areas, such as the extent of a State or the conterminous United States. Please note that for the CONUS database, only a 30 meter raster is included. SSURGO is the SPSD flagship soils database that has over 100 years of field-validated detailed soil mapping data. SSURGO contains soils information for more than 90 percent of the United States and island territories, but unmapped land remains. Click here for the current completion status of SSURGO mapping. STATSGO2 is a general soil map that has soils data for all of the United States and island territories, but the data is not as detailed as the SSURGO data. The Raster Soil Surveys (RSSs) are the next generation soil survey databases developed using advanced digital soil mapping methods. https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcseprd1464625) Use the Create A Soil Map ArcTool from the gSSURGO Mapping Toolset in the Soil Data Development Toolbox to make a TIFF data layer (Instructions: https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcseprd1464625#grid). Make a Hydrological Soils Group Map, and display it using the Hydrolgrp_ attribute. NotesThe SPSD refreshes all published soil databases annually. gNATSGO will be included in the refresh cycle, which will provide a new up-to-date version of the database each year. gNATSGO is an ESRI file geodatabase. The soil map units are delivered only as a 10-meter raster version and are uniquely identified by the mukey, which is included in the attribute table. No vectorized version of the soil map units is included in gNATSGO. The database has 70 tables that contain soil attributes, and relationship classes are built into the database to define relationships among tables. The raster can be joined to the Mapunit and Muaggatt tables in the MUKEY field. The database contains a feature class called SAPOLYGON. The “source” field in this feature class indicates whether the data was derived from SSURGO, STATSGO2, or an RSS. A gNATSGO database was created for the conterminous United States and for each state or island territory that does not have complete coverage in SSURGO or has a published RSS. If you encounter an ArcMap error when working with a gNATSGO dataset that reads “The number of unique values exceeds the limit” try increasing the maximum number of unique values to render in your Raster ArcMap Options. Specific instructions can be obtained here: https://support.esri.com/en/technical-article/000010117

When rain falls over land, a portion of it runs off into stream channels and storm water systems while the remainder infiltrates into the soil or returns to the atmosphere directly through evaporation.Physical properties of soil affect the rate that water is absorbed and the amount of runoff produced by a storm. Hydrologic soil group provides an index of the rate that water infiltrates a soil and is an input to rainfall-runoff models that are used to predict potential stream flow.For more information on using hydrologic soil group in hydrologic modeling see the publication Urban Hydrology for Small Watersheds (Natural Resources Conservation Service, United States Department of Agriculture, Technical Release–55).Dataset SummaryPhenomenon Mapped: Soil hydrologic groupUnits: ClassesCell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerData Coordinate System: USA Contiguous Albers Equal Area Conic USGS version (contiguous US, Puerto Rico, US Virgin Islands), WGS 1984 Albers (Alaska), Hawaii Albers Equal Area Conic (Hawaii), Western Pacific Albers Equal Area Conic (Guam, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American Samoa)Mosaic Projection: Web Mercator Auxiliary SphereExtent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaSource: Natural Resources Conservation ServicePublication Date: December 2021ArcGIS Server URL: https://landscape11.arcgis.com/arcgis/Data from the gNATSGO database was used to create the layer for the contiguous United States, Alaska, Puerto Rico, and the U.S. Virgin Islands. The remaining areas were created with the gSSURGO database (Hawaii, Guam, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American Samoa).This layer is derived from the 30m (contiguous U.S.) and 10m rasters (all other regions) produced by the Natural Resources Conservation Service (NRCS). The value for hydrologic group is derived from the gSSURGO map unit aggregated attribute table field Hydrologic Group - Dominant Conditions (hydgrpdcd).The seven classes of hydrologic soil group followed by definitions:Group A - Group A soils consist of deep, well drained sands or gravelly sands with high infiltration and low runoff rates.Group B - Group B soils consist of deep well drained soils with a moderately fine to moderately coarse texture and a moderate rate of infiltration and runoff.Group C - Group C consists of soils with a layer that impedes the downward movement of water or fine textured soils and a slow rate of infiltration.Group D - Group D consists of soils with a very slow infiltration rate and high runoff potential. This group is composed of clays that have a high shrink-swell potential, soils with a high water table, soils that have a clay pan or clay layer at or near the surface, and soils that are shallow over nearly impervious material.Group A/D - Group A/D soils naturally have a very slow infiltration rate due to a high water table but will have high infiltration and low runoff rates if drained.Group B/D - Group B/D soils naturally have a very slow infiltration rate due to a high water table but will have a moderate rate of infiltration and runoff if drained.Group C/D - Group C/D soils naturally have a very slow infiltration rate due to a high water table but will have a slow rate of infiltration if drained.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 "soil hydrologic group" 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 "soil hydrologic group" 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 other beautiful and authoritative maps on hundreds of topics like this one.

When rain falls over land a portion of it runs off into stream channels and storm water systems while the remainder is absorbed into the soil or returns to the atmosphere directly through evaporation.Physical properties of soil affect the rate that water is absorbed and the amount of runoff produced by a storm. Hydrologic soil group provides an index of the rate that water infiltrates a soil and is an input to rainfall-runoff models that are used to predict potential stream flow.For more information on using hydrologic soil group in hydrologic modeling see the publication Urban Hydrology for Small Watersheds (Natural Resources Conservation Service, United States Department of Agriculture, Technical Release–55).Dataset SummaryThis layer provides access to an image service with a cell size of 30 meters. It is derived from the 2014 version of the gSSURGO 30m raster (contiguous 48 States and Washington D.C.) and 10m raster (all other regions) produced by the Natural Resources Conservation Service (NRCS). The value for hydrologic soil group was derived from the gSSURGO Map Unit Aggregate Attribute table field Hydrologic Group - Dominant Conditions (hydgrpdcd).This 30m resolution layer covers most of the continental United States, portions of Alaska, and Hawaii, Puerto Rico, the U.S. Virgin Islands, and several Pacific Islands including Guam and Saipan. The layer was created from the 2014 SSURGO snapshot.The seven classes of hydrologic soil group are:Group A soils have a high infiltration rate and low runoff. These soils consist of deep, well drained sands or gravelly sands and have a high rate of water transmission.Group B soils have a moderate infiltration rate. This group consists chiefly of deep well drained soils with a moderately fine to moderately coarse texture and a moderate rate of water transmission.Group C soils have a slow infiltration rate. This group consists of soils with a layer that impedes the downward movement of water or fine textured soils and a slow rate of water transmission.Group D soils have a very slow infiltration rate and high runoff potential. This group is composed of clays that have a high shrink-swell potential, soils with a high water table, soils that have a claypan or clay layer at or near the surface, and soils that are shallow over nearly impervious material. These soils have a very slow rate of water transmission.If a soil is placed in group D because of a high water table it may be assigned to a dual hydrologic group: A/D, B/D, or C/D. The first letter of the pair represents the soil’s group if drained and the D represents the natural condition.For more information on soil hydrologic groups see the Natural Resources Conservation Service's National Engineering Handbook.The original gSSURGO dataset is available from the NRCS’s Geospatial Data Gateway.Link to source metadata

The dataset consists of three raster GeoTIFF files describing the following soil properties in the US: available water capacity, field capacity, and soil porosity. The input data were obtained from the gridded National Soil Survey Geographic (gNATSGO) Database and the Gridded Soil Survey Geographic (gSSURGO) Database with Soil Data Development tools provided by the Natural Resources Conservation Service. The soil characteristics derived from the databases were Available Water Capacity (AWC), Water Content (one-third bar) (WC), and Bulk Density (one-third bar) (BD) aggregated as weighted average values in the upper 1 m of soil. AWC and WC layers were converted to mm/m to express respectively available water capacity and field capacity in 1 m of soil, and BD layer was used to produce soil porosity raster assuming that the average particle density of soils is equal to 2.65 g/cm3. For each soil property, soil maps with CONUS, Alaska, and Hawaii geographic coverages were derived from separate databases and combined into one file. To replace no data values within a raster, we used data values statistically derived from neighboring cell values. The final product is provided in a GeoTIFF format and therefore can be easily integrated into raster-based models requiring estimates of soil characteristics in the US.

This data collection consists of landuse and soil rasters andcn grind for Cyber Training Class week 12 works. Initial codes were provided by the instructor Dr. Venkatesh Merwade.

The landuse raster file is a reclassified National Land Cover Dataset (NLCD) raster. Original landuse raster file contains integer values 11-95 to represent different types of land cover (e.g. 11 represents open water). It was reclassified based on a .csv file that contains original value ranges and new values.

The soil raster was created from Gridded Soil Survey Geographic (gSSURGO) database file. It contains multiple features that represent different soil properties. One field named "HSG_Index" gives information about hydrologic soil group (1-HSG A, 2-HSG B, 3-HSG C, 4-HSG D).

The CN grind is generated based on the landuse raster file, the soil raster file, and a curve number lookup table. The curve number lookup table contains combinations of landuse value and soil HSG_Index value combinations and respective CN value.

All calculations were completed though python codes using Jupyter notebook with Anaconda 5.1 on MyGeoHub.

This data set includes the relative production scenarios for blue grama [4.15(Temp) -0.3(Precip) - 0.15(Temp)^2 + 0.08]; this is the model from Epstein, et al. (1998). Soil texture (percent by weight) came from the Earth Systems Science Center (2008) which provided processed soils data from NRCS (gSSURGO), mean annual temperature (Celsius) and/or mean annual precipitation (millimeters) came from contemporary (1981 - 2010) estimates (Maurer et al. 2002) or a GCM. Global Climate Models (GCM) providing scenarios included: warmer-wetter scenario (CESM1-BGC, RCP4.5, Neale et al., 2010), warmer drier scenario (GISS-E2-R, RCP4.5, Schmidt, 2014), hotter-wetter scenario (Miroc-ESM, RCP8.5, Watanabe et al., 2011), and hotter-drier scenario (ACCESS 1-0, RCP8.5, Collier and Uhe, 2012). The results were binned into 7 classes based on breaks in the data and comparison with field observations.Climate change has been identified as a high-priority threat to grasslands by the Great Plains Landscape Conservation Cooperative (GPLCC) and as a priority change agent for grasslands in the Southern Great Plains Rapid Ecoregional Assessment by the Bureau of Land Management. The area of interest includes four level III ecoregions: the High Plains, Central Great Plains, Southwestern Tablelands, and the Nebraska Sand Hills. To address this priority information need for multiple stakeholders, we evaluated the potential vulnerability of four grassland communities (shortgrass, mixed-grass, and tallgrass prairies, and semiarid grasslands) using four climate change scenarios (representing hotter-drier, hotter-wetter, warmer-drier, and warmer-wetter conditions, relative to contemporary conditions). We used relative above-ground productivity models (Epstein et al., 1998) to evaluate the potential for change in productivity for each grassland community using mean annual precipitation and temperature for the contemporary climate (1981-2010) and the four climate scenarios (2016-2045), and the percent of sand, silt, and clay from the dominant soils component from the Natural Resource Conservation Service (Earth System Science Center, 2008). We selected two indicator species for each community: shortgrass prairie: blue grama (Bouteloua gracilis) and buffalo grass (Bouteloua dactyloides); mixedgrass prairie: sideoats grama (Bouteloua curtipendula) and little bluestem (Schizachyrium scoparium); tallgrass prairie: big bluestem (Andropogon gerardii) and Indiangrass (Sorghastrum nutans); and semiarid grasslands: black grama (Bouteloua eriopoda) and tobosagrass (Pleuraphis mutica). For each indicator species, we evaluated the potential change in relative productivity for each climate scenario compared to the contemporary climate. We used standard deviations to classify the differences between predicted productivity relative to the contemporary predicted productivity to evaluate whether the distributions of the indicator species were expected to remain stable, decrease, or expand for each scenario.Spatial data representing the estimated relative productivity of grassland species in the Southern Great Plains are provided as a 1-square kilometer gridded surface (raster dataset). This information will help to address priority management questions for grassland conservation in the GPLCC and Southern Great Plains regions and can be used to inform other regional-level land management decisions.Collier, Mark, and Uhe, Peter, 2012, CMIP5 datasets from the ACCESS1.0 and ACCESS1.3 coupled climate models: Centre for Australian Weather and Climate Research Technical Report No. 059, 25 p.Earth System Science Center, 2008, Soil fraction data: College of Earth and Mineral Sciences at The Pennsylvania State University, accessed January 7, 2016, at http://www.soilinfo.psu.edu/index.cgi?soil_data&conus&data_cov&fract&datasets&alb.Epstein, H.E., Lauenroth, W.K., Burke, I.C., and Coffin, D.P., 1998, Regional productivities of plant species in the Great Plains of the United States: Plant Ecology, v. 134, p. 173-195.Maurer, E.P., Wood, A.W., Adam, J.C., Lettenmaier, D.P., and Nijssen, B., 2002, A long-term hydrologically-based data set of land surface fluxes and states for the conterminous United States: Journal of Climate, v. 15, no. 22, p. 3237-3251.Natural Resources Conservation Service [NRCS], Surface Soils Geographic Database [gSSURGO], United States Department of Agriculture Natural Resources Conservation Service, at https://catalog.data.gov/dataset/gridded-soil-survey-geographic-gssurgo-10-database-for-the-conterminous-united-states-10-m.Neale, R.B.; Chen, Chih-Chieh; Gettelman, Andrew; Lauritzen, P.H.; Park, Sungsu; Williamson, D.L.; Conley, A.J.; Garcia, Rolando; Kinnison, Doug; Lamarque, Jean-Francois; Marsh, Dan; Mills, Mike; Smith, A.K.; Tilmes, Simone; Vitt, Francis; Morrison, Hugh; Cameron-Smith, Philip; Collins, W.D.; Iacono, M.J.; Easter, R.C.; Ghan, S.J.; Liu, Xiaohong; Rasch, P.J.; Taylor, M.A., 2010, Description of the NCAR Community Atmosphere Model (CAM 5.0): National Center for Atmospheric Research Technical Note NCAR/TN-486+STR, 274 p.Schmidt, G.A., M. Kelley, L. Nazarenko, R. Ruedy, G.L. Russell, I. Aleinov, M. Bauer, S.E. Bauer, M.K. Bhat, R. Bleck, V. Canuto, Y.-H. Chen, Y. Cheng, T.L. Clune, A. Del Genio, R. de Fainchtein, G. Faluvegi, J.E. Hansen, R.J. Healy, N.Y. Kiang, D. Koch, A.A. Lacis, A.N. LeGrande, J. Lerner, K.K. Lo, E.E. Matthews, S. Menon, R.L. Miller, V. Oinas, A.O. Oloso, J.P. Perlwitz, M.J. Puma, W.M. Putman, D. Rind, A. Romanou, M. Sato, D.T. Shindell, S. Sun, R.A. Syed, N. Tausnev, K. Tsigaridis, N. Unger, A. Voulgarakis, M.-S. Yao, and J. Zhang, 2014: Configuration and assessment of the GISS ModelE2 contributions to the CMIP5 archive. J. Adv. Model. Earth Syst., 6, no. 1, 141-184, doi:10.1002/2013MS000265.Watanabe, S., Hajima, T., Sudo, K., Nagashima, T., Takemura, T., Okajima, H., Nozawa, T., Kawase, H., Abe, M., Yokohata, T., Ise, T., Sato, H., Kato, E., Takata, K., Emori, S., and Kawamiya, M., 2011, MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments, Geosci. Model Dev., 4, 845-872, https://doi.org/10.5194/gmd-4-845-2011.

This data set includes the relative production scenarios for big bluestem [3.08(Temp) -0.41(Precip)+0.14(Silt) - 0.16(Temp)^2 -31.9]; this is the model from Epstein, et al. (1998). Soil texture (percent by weight) came from the Earth Systems Science Center (2008) which provided processed soils data from NRCS (gSSURGO), mean annual temperature (Celsius) and/or mean annual precipitation (millimeters) came from contemporary (1981 - 2010) estimates (Maurer et al. 2002) or a GCM. Global Climate Models (GCM) providing scenarios included: warmer-wetter scenario (CESM1-BGC, RCP4.5, Neale et al., 2010), warmer drier scenario (GISS-E2-R, RCP4.5, Schmidt, 2014), hotter-wetter scenario (Miroc-ESM, RCP8.5, Watanabe et al., 2011), and hotter-drier scenario (ACCESS 1-0, RCP8.5, Collier and Uhe, 2012). The results were binned into 7 classes based on breaks in the data and comparison with field observations.Climate change has been identified as a high-priority threat to grasslands by the Great Plains Landscape Conservation Cooperative (GPLCC) and as a priority change agent for grasslands in the Southern Great Plains Rapid Ecoregional Assessment by the Bureau of Land Management. The area of interest includes four level III ecoregions: the High Plains, Central Great Plains, Southwestern Tablelands, and the Nebraska Sand Hills. To address this priority information need for multiple stakeholders, we evaluated the potential vulnerability of four grassland communities (shortgrass, mixed-grass, and tallgrass prairies, and semiarid grasslands) using four climate change scenarios (representing hotter-drier, hotter-wetter, warmer-drier, and warmer-wetter conditions, relative to contemporary conditions). We used relative above-ground productivity models (Epstein et al., 1998) to evaluate the potential for change in productivity for each grassland community using mean annual precipitation and temperature for the contemporary climate (1981-2010) and the four climate scenarios (2016-2045), and the percent of sand, silt, and clay from the dominant soils component from the Natural Resource Conservation Service (Earth System Science Center, 2008). We selected two indicator species for each community: shortgrass prairie: blue grama (Bouteloua gracilis) and buffalo grass (Bouteloua dactyloides); mixedgrass prairie: sideoats grama (Bouteloua curtipendula) and little bluestem (Schizachyrium scoparium); tallgrass prairie: big bluestem (Andropogon gerardii) and Indiangrass (Sorghastrum nutans); and semiarid grasslands: black grama (Bouteloua eriopoda) and tobosagrass (Pleuraphis mutica). For each indicator species, we evaluated the potential change in relative productivity for each climate scenario compared to the contemporary climate. We used standard deviations to classify the differences between predicted productivity relative to the contemporary predicted productivity to evaluate whether the distributions of the indicator species were expected to remain stable, decrease, or expand for each scenario.Spatial data representing the estimated relative productivity of grassland species in the Southern Great Plains are provided as a 1-square kilometer gridded surface (raster dataset). This information will help to address priority management questions for grassland conservation in the GPLCC and Southern Great Plains regions and can be used to inform other regional-level land management decisions.Collier, Mark, and Uhe, Peter, 2012, CMIP5 datasets from the ACCESS1.0 and ACCESS1.3 coupled climate models: Centre for Australian Weather and Climate Research Technical Report No. 059, 25 p.Earth System Science Center, 2008, Soil fraction data: College of Earth and Mineral Sciences at The Pennsylvania State University, accessed January 7, 2016, at http://www.soilinfo.psu.edu/index.cgi?soil_data&conus&data_cov&fract&datasets&alb.Epstein, H.E., Lauenroth, W.K., Burke, I.C., and Coffin, D.P., 1998, Regional productivities of plant species in the Great Plains of the United States: Plant Ecology, v. 134, p. 173-195.Maurer, E.P., Wood, A.W., Adam, J.C., Lettenmaier, D.P., and Nijssen, B., 2002, A long-term hydrologically-based data set of land surface fluxes and states for the conterminous United States: Journal of Climate, v. 15, no. 22, p. 3237-3251.Natural Resources Conservation Service [NRCS], Surface Soils Geographic Database [gSSURGO], United States Department of Agriculture Natural Resources Conservation Service, at https://catalog.data.gov/dataset/gridded-soil-survey-geographic-gssurgo-10-database-for-the-conterminous-united-states-10-m.Neale, R.B.; Chen, Chih-Chieh; Gettelman, Andrew; Lauritzen, P.H.; Park, Sungsu; Williamson, D.L.; Conley, A.J.; Garcia, Rolando; Kinnison, Doug; Lamarque, Jean-Francois; Marsh, Dan; Mills, Mike; Smith, A.K.; Tilmes, Simone; Vitt, Francis; Morrison, Hugh; Cameron-Smith, Philip; Collins, W.D.; Iacono, M.J.; Easter, R.C.; Ghan, S.J.; Liu, Xiaohong; Rasch, P.J.; Taylor, M.A., 2010, Description of the NCAR Community Atmosphere Model (CAM 5.0): National Center for Atmospheric Research Technical Note NCAR/TN-486+STR, 274 p.Schmidt, G.A., M. Kelley, L. Nazarenko, R. Ruedy, G.L. Russell, I. Aleinov, M. Bauer, S.E. Bauer, M.K. Bhat, R. Bleck, V. Canuto, Y.-H. Chen, Y. Cheng, T.L. Clune, A. Del Genio, R. de Fainchtein, G. Faluvegi, J.E. Hansen, R.J. Healy, N.Y. Kiang, D. Koch, A.A. Lacis, A.N. LeGrande, J. Lerner, K.K. Lo, E.E. Matthews, S. Menon, R.L. Miller, V. Oinas, A.O. Oloso, J.P. Perlwitz, M.J. Puma, W.M. Putman, D. Rind, A. Romanou, M. Sato, D.T. Shindell, S. Sun, R.A. Syed, N. Tausnev, K. Tsigaridis, N. Unger, A. Voulgarakis, M.-S. Yao, and J. Zhang, 2014: Configuration and assessment of the GISS ModelE2 contributions to the CMIP5 archive. J. Adv. Model. Earth Syst., 6, no. 1, 141-184, doi:10.1002/2013MS000265.Watanabe, S., Hajima, T., Sudo, K., Nagashima, T., Takemura, T., Okajima, H., Nozawa, T., Kawase, H., Abe, M., Yokohata, T., Ise, T., Sato, H., Kato, E., Takata, K., Emori, S., and Kawamiya, M., 2011, MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments, Geosci. Model Dev., 4, 845-872, https://doi.org/10.5194/gmd-4-845-2011.

This data set includes the relative production scenarios for black grama [0.37(Temp) - 0.06(Precip) + 0.24]; this is the model from Epstein, et al. (1998). Soil texture (percent by weight) came from the Earth Systems Science Center (2008) which provided processed soils data from NRCS (gSSURGO), mean annual temperature (Celsius) and/or mean annual precipitation (millimeters) came from contemporary (1981 - 2010) estimates (Maurer et al. 2002) or a GCM. Global Climate Models (GCM) providing scenarios included: warmer-wetter scenario (CESM1-BGC, RCP4.5, Neale et al., 2010), warmer drier scenario (GISS-E2-R, RCP4.5, Schmidt, 2014), hotter-wetter scenario (Miroc-ESM, RCP8.5, Watanabe et al., 2011), and hotter-drier scenario (ACCESS 1-0, RCP8.5, Collier and Uhe, 2012). The results were binned into 7 classes based on breaks in the data and comparison with field observations.Climate change has been identified as a high-priority threat to grasslands by the Great Plains Landscape Conservation Cooperative (GPLCC) and as a priority change agent for grasslands in the Southern Great Plains Rapid Ecoregional Assessment by the Bureau of Land Management. The area of interest includes four level III ecoregions: the High Plains, Central Great Plains, Southwestern Tablelands, and the Nebraska Sand Hills. To address this priority information need for multiple stakeholders, we evaluated the potential vulnerability of four grassland communities (shortgrass, mixed-grass, and tallgrass prairies, and semiarid grasslands) using four climate change scenarios (representing hotter-drier, hotter-wetter, warmer-drier, and warmer-wetter conditions, relative to contemporary conditions). We used relative above-ground productivity models (Epstein et al., 1998) to evaluate the potential for change in productivity for each grassland community using mean annual precipitation and temperature for the contemporary climate (1981-2010) and the four climate scenarios (2016-2045), and the percent of sand, silt, and clay from the dominant soils component from the Natural Resource Conservation Service (Earth System Science Center, 2008). We selected two indicator species for each community: shortgrass prairie: blue grama (Bouteloua gracilis) and buffalo grass (Bouteloua dactyloides); mixedgrass prairie: sideoats grama (Bouteloua curtipendula) and little bluestem (Schizachyrium scoparium); tallgrass prairie: big bluestem (Andropogon gerardii) and Indiangrass (Sorghastrum nutans); and semiarid grasslands: black grama (Bouteloua eriopoda) and tobosagrass (Pleuraphis mutica). For each indicator species, we evaluated the potential change in relative productivity for each climate scenario compared to the contemporary climate. We used standard deviations to classify the differences between predicted productivity relative to the contemporary predicted productivity to evaluate whether the distributions of the indicator species were expected to remain stable, decrease, or expand for each scenario.Spatial data representing the estimated relative productivity of grassland species in the Southern Great Plains are provided as a 1-square kilometer gridded surface (raster dataset). This information will help to address priority management questions for grassland conservation in the GPLCC and Southern Great Plains regions and can be used to inform other regional-level land management decisions.Collier, Mark, and Uhe, Peter, 2012, CMIP5 datasets from the ACCESS1.0 and ACCESS1.3 coupled climate models: Centre for Australian Weather and Climate Research Technical Report No. 059, 25 p.Earth System Science Center, 2008, Soil fraction data: College of Earth and Mineral Sciences at The Pennsylvania State University, accessed January 7, 2016, at http://www.soilinfo.psu.edu/index.cgi?soil_data&conus&data_cov&fract&datasets&alb.Epstein, H.E., Lauenroth, W.K., Burke, I.C., and Coffin, D.P., 1998, Regional productivities of plant species in the Great Plains of the United States: Plant Ecology, v. 134, p. 173-195.Maurer, E.P., Wood, A.W., Adam, J.C., Lettenmaier, D.P., and Nijssen, B., 2002, A long-term hydrologically-based data set of land surface fluxes and states for the conterminous United States: Journal of Climate, v. 15, no. 22, p. 3237-3251.Natural Resources Conservation Service [NRCS], Surface Soils Geographic Database [gSSURGO], United States Department of Agriculture Natural Resources Conservation Service, at https://catalog.data.gov/dataset/gridded-soil-survey-geographic-gssurgo-10-database-for-the-conterminous-united-states-10-m.Neale, R.B.; Chen, Chih-Chieh; Gettelman, Andrew; Lauritzen, P.H.; Park, Sungsu; Williamson, D.L.; Conley, A.J.; Garcia, Rolando; Kinnison, Doug; Lamarque, Jean-Francois; Marsh, Dan; Mills, Mike; Smith, A.K.; Tilmes, Simone; Vitt, Francis; Morrison, Hugh; Cameron-Smith, Philip; Collins, W.D.; Iacono, M.J.; Easter, R.C.; Ghan, S.J.; Liu, Xiaohong; Rasch, P.J.; Taylor, M.A., 2010, Description of the NCAR Community Atmosphere Model (CAM 5.0): National Center for Atmospheric Research Technical Note NCAR/TN-486+STR, 274 p.Schmidt, G.A., M. Kelley, L. Nazarenko, R. Ruedy, G.L. Russell, I. Aleinov, M. Bauer, S.E. Bauer, M.K. Bhat, R. Bleck, V. Canuto, Y.-H. Chen, Y. Cheng, T.L. Clune, A. Del Genio, R. de Fainchtein, G. Faluvegi, J.E. Hansen, R.J. Healy, N.Y. Kiang, D. Koch, A.A. Lacis, A.N. LeGrande, J. Lerner, K.K. Lo, E.E. Matthews, S. Menon, R.L. Miller, V. Oinas, A.O. Oloso, J.P. Perlwitz, M.J. Puma, W.M. Putman, D. Rind, A. Romanou, M. Sato, D.T. Shindell, S. Sun, R.A. Syed, N. Tausnev, K. Tsigaridis, N. Unger, A. Voulgarakis, M.-S. Yao, and J. Zhang, 2014: Configuration and assessment of the GISS ModelE2 contributions to the CMIP5 archive. J. Adv. Model. Earth Syst., 6, no. 1, 141-184, doi:10.1002/2013MS000265.Watanabe, S., Hajima, T., Sudo, K., Nagashima, T., Takemura, T., Okajima, H., Nozawa, T., Kawase, H., Abe, M., Yokohata, T., Ise, T., Sato, H., Kato, E., Takata, K., Emori, S., and Kawamiya, M., 2011, MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments, Geosci. Model Dev., 4, 845-872, https://doi.org/10.5194/gmd-4-845-2011.

Context: Plant communities are undergoing compositional changes that affect ecosystem function. These changes are not always uniform across the landscape due to heterogenous topographic and edaphic conditions. To predict areas most at risk of change, it is necessary to identify the landscape drivers affecting plant abundance. Objectives: Annual plants are increasing across the Wwestern USA, largely driven by non-native annual invasions. Here, we quantified change in annual plant abundance and identified landscape factors contributing to that change over the past 35 years. Methods: We focused on Willamette Valley (Oregon) grasslands because they represent a new example in this phenomenon. To understand the spatiotemporal patterns of annual plant abundances between 1986 and 2020, we combined a remote-sensing vegetation cover dataset from the rangeland analysis platform with gridded soils data and topographic variables. We determined the rate of change in percent cover for e..., df_RAP.csv: Yearly (1986-2020) vegetation cover data of annual and perennial forbs and grasses for the Willamette Valley were downloaded as TIFF files (30-m resolution) from the Rangeland Analysis Platform's Google Earth Engine catalog. To eliminate forested and other non-grassland areas, we masked RAP data to grassland/herbaceous, pasture/hay, and shrub/scrub classifications in the National Land Cover Database 2019 Land Cover dataset using the raster package in R. Prior to this, we had previously resampled the land cover dataset from 10-m to 30-m resolution and reprojected to WGS 1984 using ArcMap 10.5. For each year in the 1986-2020 RAP dataset, we calculated new rasters for total herbaceous cover by summing the annual and perennial layers. We then filtered to cells with ≥20% average total herbaceous cover across the 35 years to avoid areas which may have skewed estimates due to low herbaceous cover in general (e.g., grassland borders near forests). Finally, we filtered to the 16 site..., , , # Data from: Spatiotemporal patterns of rising annual plant abundance in grasslands of the Willamette Valley, Oregon (USA)

This README file from: Spatiotemporal patterns of rising annual plant abundance in grasslands of the western Pacific Northwest, USA, was generated on 2022-07-06 by Paul B. Reed

GENERAL INFORMATION

1. Title of Dataset: Data from: Spatiotemporal patterns of rising annual plant abundance in grasslands of the western Pacific Northwest, USA
2. Author Information A. Principal Investigator Contact Information Name: Paul B. Reed Institution: Institute for Applied Ecology Address: 4950 SW Hout St Corvallis, OR 97333 Email: paulreed@appliedeco.org
3. Date of data collection: 2022-02-01
4. Geographic location of data collection: Willamette Valley, Oregon, USA
5. Information about funding sources that supported the collection of the data: USDA-NIFA postdoctoral fellowship award # 2021-67034-35136

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Albedo measures the reflectivity of an object. Surfaces that are black reflect little light and have low albedo values while white surfaces reflect most of the light striking them and have high albedo values. Albedo is measured on a scale of 0 (no light reflected) to 1 (100% of light reflected). Albedo is measured using a scale of 0 (no light reflected) to 1 (100% of the light is reflected). Divide each integer"s raw pixel value by one hundred to find its representative albedo value. Thus, a pixel with the value of 24 represents an albedo value of 0.24 while a pixel with the value of 38 represents the albedo value 0.38. Dataset SummaryPhenomenon Mapped: Soil albedoGeographic 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: NoneCell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerSource: Natural Resources Conservation ServiceUpdate Frequency: AnnualPublication Date: December 2024 Typical albedo values:Fresh asphalt 0.04Worn asphalt 0.12Confier forest 0.08 – 0.15Deciduous trees 0.15 – 0.18Bare soil 0.17Green grass 0.25Desert sand 0.4New concrete 0.55Ocean ice 0.5 – 0.7Fresh snow 0.8-0.9 Albedo is used in climate and water cycle models. Estimates of evapotranspiration rate and prediction of soil water balances require albedo values. Soil hydrology models that are part of water quality and resource assessment programs also require albedo. 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 soil albedo is derived from the gSSURGO component table field Albedo Dry - Representative Value (albedodry_r). The value in this layer is the average value for all components of each map unit weighted by component percent (comppct_r). 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 "albedo" 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 "albedo" 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 other beautiful 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.