Soil map units are the basic geographic unit of the Soil Survey Geographic Database (SSURGO). The SSURGO dataset is a compilation of soils information collected over the last century by the Natural Resources Conservation Service (NRCS). Map units delineate the extent of different soils. Data for each map unit contains descriptions of the soil’s components, productivity, unique properties, and suitability interpretations. Each soil type has a unique combination of physical, chemical, nutrient and moisture properties. Soil type has ramifications for engineering and construction activities, natural hazards such as landslides, agricultural productivity, the distribution of native plant and animal life and hydrologic and other physical processes. Soil types in the context of climate and terrain can be used as a general indicator of engineering constraints, agriculture suitability, biological productivity and the natural distribution of plants and animals. Data from thegSSURGO databasewas used to create this layer. To download ready-to-use project packages of useful soil data derived from the SSURGO dataset, please visit the USA SSURGO Downloader app. Dataset Summary Phenomenon Mapped:Soils of the United States and associated territoriesGeographic Extent:The 50 United States, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaCoordinate System:Web Mercator Auxiliary SphereVisible Scale:1:144,000 to 1:1,000Source:USDA Natural Resources Conservation Service Update Frequency:AnnualPublication Date:December 2024 What can you do with this layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS Online Add this layer to a map in the map viewer. The layer is limited to scales of approximately 1:144,000 or larger but avector tile layercreated from the same data can be used at smaller scales to produce awebmapthat displays across the full scale range. The layer or a map containing it can be used in an application.Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections and apply filters. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Change the layer’s style and filter the data. For example, you could set a filter forFarmland Class= "All areas are prime farmland" to create a map of only prime farmland.Add labels and set their propertiesCustomize the pop-upArcGIS Pro Add this layer to a 2d or 3d map. The same scale limit as Online applies in ProUse as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of theLiving Atlas of the Worldthat provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics. Data DictionaryAttributesKey fields from nine commonly used SSURGO tables were compiled to create the 173 attribute fields in this layer. Some fields were joined directly to the SSURGO Map Unit polygon feature class while others required summarization and other processing to create a 1:1 relationship between the attributes and polygons prior to joining the tables. Attributes of this layer are listed below in their order of occurrence in the attribute table and are organized by the SSURGO table they originated from and the processing methods used on them. Map Unit Polygon Feature Class Attribute TableThe fields in this table are from the attribute table of the Map Unit polygon feature class which provides the geographic extent of the map units. Area SymbolSpatial VersionMap Unit Symbol Map Unit TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the table using the Map Unit Key field. Map Unit NameMap Unit KindFarmland ClassInterpretive FocusIntensity of MappingIowa Corn Suitability Rating Legend TableThis table has 1:1 relationship with the Map Unit table and was joined using the Legend Key field. Project Scale Survey Area Catalog TableThe fields in this table have a 1:1 relationship with the polygons and were joined to the Map Unit table using the Survey Area Catalog Key and Legend Key fields. Survey Area VersionTabular Version Map Unit Aggregated Attribute TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the Map Unit attribute table using the Map Unit Key field. Slope Gradient - Dominant ComponentSlope Gradient - Weighted AverageBedrock Depth - MinimumWater Table Depth - Annual MinimumWater Table Depth - April to June MinimumFlooding Frequency - Dominant ConditionFlooding Frequency - MaximumPonding Frequency - PresenceAvailable Water Storage 0-25 cm - Weighted AverageAvailable Water Storage 0-50 cm - Weighted AverageAvailable Water Storage 0-100 cm - Weighted AverageAvailable Water Storage 0-150 cm - Weighted AverageDrainage Class - Dominant ConditionDrainage Class - WettestHydrologic Group - Dominant ConditionIrrigated Capability Class - Dominant ConditionIrrigated Capability Class - Proportion of Mapunit with Dominant ConditionNon-Irrigated Capability Class - Dominant ConditionNon-Irrigated Capability Class - Proportion of Mapunit with Dominant ConditionRating for Buildings without Basements - Dominant ConditionRating for Buildings with Basements - Dominant ConditionRating for Buildings with Basements - Least LimitingRating for Buildings with Basements - Most LimitingRating for Septic Tank Absorption Fields - Dominant ConditionRating for Septic Tank Absorption Fields - Least LimitingRating for Septic Tank Absorption Fields - Most LimitingRating for Sewage Lagoons - Dominant ConditionRating for Sewage Lagoons - Dominant ComponentRating for Roads and Streets - Dominant ConditionRating for Sand Source - Dominant ConditionRating for Sand Source - Most ProbableRating for Paths and Trails - Dominant ConditionRating for Paths and Trails - Weighted AverageErosion Hazard of Forest Roads and Trails - Dominant ComponentHydric Classification - Presence Rating for Manure and Food Processing Waste - Weighted Average Component Table – Dominant ComponentMap units have one or more components. To create a 1:1 join component data must be summarized by map unit. For these fields a custom script was used to select the component with the highest value for the Component Percentage Representative Value field (comppct_r). Ties were broken with the Slope Representative Value field (slope_r). Components with lower average slope were selected as dominant. If both soil order and slope were tied, the first value in the table was selected. Component Percentage - Low ValueComponent Percentage - Representative ValueComponent Percentage - High ValueComponent NameComponent KindOther Criteria Used to Identify ComponentsCriteria Used to Identify Components at the Local LevelRunoff ClassSoil loss tolerance factorWind Erodibility IndexWind Erodibility GroupErosion ClassEarth Cover 1Earth Cover 2Hydric ConditionHydric RatingAspect Range - Counter Clockwise LimitAspect - Representative ValueAspect Range - Clockwise LimitGeomorphic DescriptionNon-Irrigated Capability SubclassNon-Irrigated Unit Capability ClassIrrigated Capability SubclassIrrigated Unit Capability ClassConservation Tree Shrub GroupGrain Wildlife HabitatGrass Wildlife HabitatHerbaceous Wildlife HabitatShrub Wildlife HabitatConifer Wildlife HabitatHardwood Wildlife HabitatWetland Wildlife HabitatShallow Water Wildlife HabitatRangeland Wildlife HabitatOpenland Wildlife HabitatWoodland Wildlife HabitatWetland Wildlife HabitatSoil Slip PotentialSusceptibility to Frost HeavingConcrete CorrosionSteel CorrosionTaxonomic ClassTaxonomic OrderTaxonomic SuborderGreat GroupSubgroupParticle SizeParticle Size ModCation Exchange Activity ClassCarbonate ReactionTemperature ClassMoist SubclassSoil Temperature RegimeEdition of Keys to Soil Taxonomy Used to Classify SoilCalifornia Storie IndexComponent Key Component Table – Weighted AverageMap units may have one or more soil components. To create a 1:1 join, data from the Component table must be summarized by map unit. For these fields a custom script was used to calculate an average value for each map unit weighted by the Component Percentage Representative Value field (comppct_r). Slope Gradient - Low ValueSlope Gradient - Representative ValueSlope Gradient - High ValueSlope Length USLE - Low ValueSlope Length USLE - Representative ValueSlope Length USLE - High ValueElevation - Low ValueElevation - Representative ValueElevation - High ValueAlbedo - Low ValueAlbedo - Representative ValueAlbedo - High ValueMean Annual Air Temperature - Low ValueMean Annual Air Temperature - Representative ValueMean Annual Air Temperature - High ValueMean Annual Precipitation - Low ValueMean Annual Precipitation - Representative ValueMean Annual Precipitation - High ValueRelative Effective Annual Precipitation - Low ValueRelative Effective Annual Precipitation - Representative ValueRelative Effective Annual Precipitation - High ValueDays between Last and First Frost - Low ValueDays between Last and First Frost - Representative ValueDays between Last and First Frost - High ValueRange Forage Annual Potential Production - Low ValueRange Forage Annual Potential Production - Representative ValueRange Forage Annual Potential Production - High ValueInitial Subsidence - Low ValueInitial Subsidence - Representative ValueInitial Subsidence -

description: The data sets consist of two files, an ESRI shape file with associated files and an ESRI export file, of a composite of soil maps for Miami-Dade County, Florida issued by the Soil Conservation Service in April, 1958. The data is at 1:40,000 scale. This update of the Miami-Dade County soils map released in 2001 includes an attribute table for the soils polygons included in the spatial data layer.; abstract: The data sets consist of two files, an ESRI shape file with associated files and an ESRI export file, of a composite of soil maps for Miami-Dade County, Florida issued by the Soil Conservation Service in April, 1958. The data is at 1:40,000 scale. This update of the Miami-Dade County soils map released in 2001 includes an attribute table for the soils polygons included in the spatial data layer.

This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties. Please email GIS.Librarian@FloridaDEP.gov for additional information.

description: The data sets consist of two files, an ESRI shape file with associated files and an ESRI export file, of a composite of soil maps for Collier County, Florida issued by the Soil Conservation Service in March, 1954. The data is at 1:40,000 scale. This update of the Collier County soils map originally released in 2001 includes an attribute table for the soils polygons included in the spatial data layer.; abstract: The data sets consist of two files, an ESRI shape file with associated files and an ESRI export file, of a composite of soil maps for Collier County, Florida issued by the Soil Conservation Service in March, 1954. The data is at 1:40,000 scale. This update of the Collier County soils map originally released in 2001 includes an attribute table for the soils polygons included in the spatial data layer.

Soil map units from 1990's era soil maps were re-classed into 12 categories based on the depth of the seasonal high-water table, soil morphological characteristics, and geographical location.The Natural Soils Landscape Position project was undertaken to simplify the complex, multi-county soil survey data and to promote a clearer understanding of the relationships that exist between soil, hydrology, and vegetation community. The resulting product is a seamless spatial soil landscape layer within the boundaries of the SFWMD as well as a methodology that can be applied to other soils data to produce a similar result. This project was a cooperative effort between SFWMD and the NRCS. Funding was provided as part of the United Satets Environmental Protection agency's grant to the SFWMD for the Comprehensive Conservation Permitting and Mitigation Strategy Wetland Strategies under the assistance agreement ID number X9948864-96-0 to the South Florida Wetlands Conservation Strategy. Associated Technical Publication WS-06 (2001):Soil Classification Database: Categorization of County Soil Survey Data within the SFWMD Including Natural Soils Landscape Positions

Soil water and groundwater monitoring support the development of soil water retention curves by the University of Florida Institute of Food and Agricultural Sciences (IFAS) [Contractor] and the correlation of soil moisture data to groundwater levels and canal stages. As part of adaptive water management practices associated with this Project, water level increases are planned to occur by modifying stage at the S-18C structure. The increases are to be incremental in nature by 0.1 ft per step with a total increase of potentially 0.4 ft. It is anticipated that this rise in canal water elevation will influence groundwater levels in adjacent areas. This phenomenon has been reported in research conducted by the University of Florida (Ritter and Muñoz-Carpena, 2006, Journal of Hydrology).

One of the areas affected by project operations is an active agricultural area with a unique geographic location at the foot of the coastal bench. It is considerably lower in land elevation than surrounding farm areas just to the north. Changes in the water table can impact roots of local crops causing reductions in crop yields or complete loss of crops. There is concern on how the planned rise in water elevation in the canal (due to change in operation of S-18C) will influence the usability of adjacent land for agricultural purposes. Currently, agriculture located in the adjacent land is primarily composed of vegetable and ornamental nursery crops. Vegetables are predominantly grown during the dry season while ornamental nursery crops are produced year round. Nursery crops may be containerized or may be planted in the field.

There are three (3) main concerns to address regarding the change in canal elevation: 1) Will the rise in canal water level change soil moisture so that the land is not suitable for current uses? 2) Will the rise in canal water level continue to provide adequate flood protection? 3) How much time should be allowed to lower the water table if a large rainfall event is expected? Soil properties vary within this region, with three (3) dominant soil types: Biscayne gravelly marl, Krome very gravelly loam, and Chekika very gravelly loam. Determination of the soil water retention curve for each of these soils is critical to understanding their soil water holding capacity and drainage characteristics. The soil water retention curve provides a relationship between the soil suction or tension and the soil water volumetric content. The soil water retention curve is developed in the laboratory. This relationship is used to understand soil water dynamics in the field, where real-time soil moisture data at specific sites is collected using soil moisture sensors. The soil moisture data collected can then be correlated with measured groundwater level using hydrostatic assumptions and groundwater level data (Barquin et al., 2009; UF MS thesis). It is also possible to develop a relationship between groundwater level and canal level (Ritter and Muñoz-Carpena, 2006, Journal of Hydrology). The development of these hydrologic relationships depends on the collection and evaluation of quality, georeferenced soil moisture, groundwater level, and canal stage data.

The objectives of this Project are:

1) To collect soil water content and groundwater level data in three (3) dominant soil types in the affected agricultural area near the C-111 canal. 2) To evaluate collected data to determine if relationships can be developed between groundwater level, canal level and soil water content

Land resource areas used in the United States, Caribbean, and Pacific Basin Major Land Resource Areas (MLRA) Geographic Database serve as the geospatial expression of the map products presented and described in Agriculture Handbook 296 (2022). Land resource categories historically used at state and national levels are land resource units, major land resource areas, and land resource regions (National Soil Survey Handbook, Part 649; Land Resource Hierarchy). Although Agriculture Handbook 296 (AH 296) does not describe land resource units (LRUs) directly, they are the basic units from which major land resource areas are determined. They are also the basic units for state land resource maps. LRUs are commonly but not necessarily coextensive with state general soil map units. LRUs generally are several thousand acres in size. A unit can be one continuous area or several separate areas that are near each other. In 2005, these areas were designated as common resource areas (CRAs) within the Natural Resources Conservation Service (NRCS). Like LRUs, common resource areas are not described in AH 296 and are not shown on the national mapbut are mentioned for historical purposes. Major land resource areas are geographically associated land resource units at a broader scale and higher hierarchical level than LRUs. Land resource regions (LRR) are a group of geographically associated major land resource areasat the highest hierarchical level shown at the continental scale. Identification of these large areas is important in statewide agricultural planning and has value in interstate, regional, and national planning.In AH 296, major land resource areas are generally designated by numbers and identified by a descriptive geographic name. Examples are MLRA 1 (Northern Pacific Coast Range, Foothills, and Valleys), MLRA 154 (South-Central Florida Ridge), and MLRA 230 (Yukon-Kuskokwim Highlands). Some MLRAs are designated by a letter in addition to a number because a previously established MLRA had been divided into smaller, more homogeneous areas, for example, MLRAs 102A, 102B, and 102C. Other MLRAs, especially smaller ones approaching the LRU scale, have been recombined. The use of numbers and letters to identify the newly created MLRAs requires fewer changes in existing information in records and in databases. A few MLRAs consist of two or more parts separated for short distances by other land resource areas. In some places one of the parts is widely separated from the main body of the MLRA and is in an adjoining LRR. The description of the respective MLRA also applies to these outlying parts. The spatial illustration of the MLRAs has been smoothed for the contiguous United States and Alaska to better reflect the scale at which the MLRA resource attributes (climate, soils, land use, vegetation, geology, and physiography) were aggregated for delineation.

This dataset has been prepared under cooperative agreement between the Agricultural Experimental State of the University of Florida, and the Soil Conservation Service, U.S. Department of Agriculture, with informal cooperation of staff members of the Geological Survey, U.S. Department of the Interior, and of the Everglades Drainage District. The Everglades Project of the Soil Conservation Service was set up for research and demonstration work in soil conservation in the Everglades region to determine the various types of land in the Everglades Drainage District. For more background information please refer to: https://sofia.usgs.gov/exchange/1948_sfwmd_soils_map/background.html

Mapping soil quality (nutrients, carbon quality, process dynamics) has recently been undertaken as part of an effort to characterize baseline conditions for performance assessment of Everglades recovery activities. While large scale maps are useful for characterization of spatial pattern, several layers of uncertainty limit use as measures of performance and restoration progress. In particular, large scale maps (Greater Everglades) make specific assumptions about short range variability that are not well quantified. Nugget variance (variability in space over short separation distances) directly confounds use of baseline map products because future spatial sampling will not, in practicality, be at identical locations. If nugget variability is high, then significant uncertainty about ecosystem change arises from not knowing if observed differences arise from intrinsic ecosystem processes or from responses to human management. Our primary objective is to determine the extent to which spatial variability and sampling uncertainty confound ecological change detection. We will use hierarchically nested sampling of soils to establish nugget variability so that change through time can be assigned as observational uncertainty or management response. A related issue for mapping soil nutrients is greatly improving our understanding of the role of ecosystems in regulating nutrient conditions. Previous mapping efforts regarded space as the primary co-variate with soil nutrients, when there are numerous reasons to expect that ecological type and status are more important descriptors (with spatial autocorrelation playing a secondary, though still significant role). Our second objective is to partition variability in soil nutrient conditions in space and time, by ecosystem type, ecological status, and proximity to canals. We anticipate sampling of soils on a fine scale which traverse ecological transitions will help develop predictive models of the role of ecosystem type, geographic setting and, eventually, spatial structuring in regulating soil processes.

The data set consists of evapotranspiration measurements made at the USGS Dead River forested wetland climate station beginning November 21, 2009 and ending February 29, 2016. Annual ET rates corrected to a near-surface energy-budget varied from 1448 mm (2012) to 1614 mm (2010). The eddy-covariance method was used, with high-frequency sensors installed above the forest canopy to measure sensible and latent heat fluxes. Ancillary meteorological data are also included in the data set: net radiation, soil temperature and moisture, air temperature, relative humidity, wind speed and direction, rainfall, and ground-water levels. Data were collected at 30-minute resolution, with evapotranspiration corrected to the near-surface energy-budget at a daily timescale. The study was conducted at an undeveloped mixed hardwood swamp within Dead River Wilderness Park, located at the end of Dead River Rd., west of U.S. Highway 301, 27 km northeast of Tampa in Hillsborough County, Florida (latitude 28 07’ 43” N longitude 82 15’ 44” W, Section 13, Township 27S, Range 20E). Study instruments were installed in November 2009. The 46-m instrument tower was located about 90 m northeast of the final pullout before the parking area, which is 2.6 km from the park entrance gate on Dead River Rd. The station is within the Hillsborough River floodplain, and is inundated at high river stages (Lewelling, 2004). The site is classified as a freshwater palustrine forested broad-leaf deciduous wetland that is seasonally flooded (U.S. Fish and Wildlife Service, 2010). Dominant tree species are cabbage palm (Sabal palmetto), red maple (Acer rubrum), sweet-gum (Liquidambar styraciflua), live oak (Quercus virginiana), elm (Ulmus americana), with occasional slash pine, magnolia, and hickory. Canopy height is 20-24 m. The site is located near the middle of the floodplain at least 1 km away from different land cover types in all directions. The floodplain extends farther to the northeast and southwest along the path of the river. The soils at the site are Chobee sandy loam, frequently flooded, which are nearly level, very poorly drained bottomland soils with a surface layer of black sandy loam. The subsoil is dark to very dark gray mottled sandy clay loam (Soil Conservation Service, 1989). Clay dominates in the subsoil to the east of the station based on holes dug for the tower guy cable bases. Lewelling, B.R., 2004. Extent of areal inundation of riverine wetlands along five river systems in the Upper Hillsborough River Watershed, west-central Florida: U.S. Geological Survey Scientific Investigations Report 2004-5133, 49 p., plus appendixes. Soil Conservation Service, 1989. Soil survey of Hillsborough County, Florida: U.S. Department of Agriculture, Soil Conservation Service, 168 p. U.S. Fish and Wildlife Service, 2010. National Wetlands Inventory website: U.S. Department of Interior, Washington, D.C. http://www.fws.gov/wetlands/

This resource contains the SWAT-MODFLOW model for the Santa Fe River of North Central Florida used in the Floridan Aquifer Collaborative Engagement for Sustainability (FACETS) project. The FACETS project was funded by the USDA National Institute of Food and Agriculture (Award Number: 2017-68007-26319) to promote the economic sustainability of agriculture and silviculture in North Florida and South Georgia while protecting water quantity, quality, and habitat in the Upper Floridan Aquifer and the springs and rivers it feeds (https://floridanwater.research.ufl.edu/). SWAT-MODFLOW couples the Soil and Water Assessment Tool (SWAT) to the U.S. Geological Survey modular finite-difference flow model (MODFLOW) to produce an integrated surface-groundwater model (https://swat.tamu.edu/software/swat-modflow/). Within SWAT-MODFLOW, SWAT handles most surface and soil processes, MODFLOW handles groundwater processes, and both models interact to simulate stream flows.

The SWAT portion of this model was developed using USGS digital elevation models, the 2017 Statewide Land Use / Land Cover map of the Florida Department of Environmental Protection (FDEP), Florida Department of Health septic tank data, STATSGO soil maps, the Public Land Survey System, and NLDAS weather data. Agricultural and silvicultural production land uses and management practices implemented within SWAT were co-developed with stakeholders in a participatory modeling process (PMP) and included row crops (corn-peanut and corn-carrot-peanut rotations) forage crops (bermudagrass hay and pasture), and production forestry (slash pine). Additional land uses implemented in SWAT included urban, low-density residential, septic tanks, rapid infiltration basins, fertilized lawns, natural grass, wetlands, and open water. The MODFLOW portion of the model was developed from the larger North Florida Southeast Georgia (NFSEG) MODFLOW model (version 1.0) as developed by the St John’s River and Suwannee River Water Management Districts. A detailed description of the complete model development process can be found in a document within this resource.

Calibration of the model was conducted using a Bayesian Sample-Importance-Resample method. Data used in the model calibration included: 1) USGS discharge data (Stations 02322500, 02322700, 02322800, and 02321500); 2) USGS operational Simplified Surface Energy Balance (SSEBop) actual evapotranspiration; and 3) Upper Floridan Aquifer potentiometric surfaces from FDEP. The calibration period of the model was 2010-2018 and the validation period was 1980-2009.

The data set consists of evapotranspiration measurements made at the USGS Dead River forested wetland climate station beginning November 21, 2009 and ending February 29, 2016. Annual ET rates corrected to a near-surface energy-budget varied from 1448 mm (2012) to 1614 mm (2010). The eddy-covariance method was used, with high-frequency sensors installed above the forest canopy to measure sensible and latent heat fluxes. Ancillary meteorological data are also included in the data set: net radiation, soil temperature and moisture, air temperature, relative humidity, wind speed and direction, rainfall, and ground-water levels. Data were collected at 30-minute resolution, with evapotranspiration corrected to the near-surface energy-budget at a daily timescale. The study was conducted at an undeveloped mixed hardwood swamp within Dead River Wilderness Park, located at the end of Dead River Rd., west of U.S. Highway 301, 27 km northeast of Tampa in Hillsborough County, Florida (latitude 28 07’ 43” N longitude 82 15’ 44” W, Section 13, Township 27S, Range 20E). Study instruments were installed in November 2009. The 46-m instrument tower was located about 90 m northeast of the final pullout before the parking area, which is 2.6 km from the park entrance gate on Dead River Rd. The station is within the Hillsborough River floodplain, and is inundated at high river stages (Lewelling, 2004). The site is classified as a freshwater palustrine forested broad-leaf deciduous wetland that is seasonally flooded (U.S. Fish and Wildlife Service, 2010). Dominant tree species are cabbage palm (Sabal palmetto), red maple (Acer rubrum), sweet-gum (Liquidambar styraciflua), live oak (Quercus virginiana), elm (Ulmus americana), with occasional slash pine, magnolia, and hickory. Canopy height is 20-24 m. The site is located near the middle of the floodplain at least 1 km away from different land cover types in all directions. The floodplain extends farther to the northeast and southwest along the path of the river. The soils at the site are Chobee sandy loam, frequently flooded, which are nearly level, very poorly drained bottomland soils with a surface layer of black sandy loam. The subsoil is dark to very dark gray mottled sandy clay loam (Soil Conservation Service, 1989). Clay dominates in the subsoil to the east of the station based on holes dug for the tower guy cable bases. Lewelling, B.R., 2004. Extent of areal inundation of riverine wetlands along five river systems in the Upper Hillsborough River Watershed, west-central Florida: U.S. Geological Survey Scientific Investigations Report 2004-5133, 49 p., plus appendixes. Soil Conservation Service, 1989. Soil survey of Hillsborough County, Florida: U.S. Department of Agriculture, Soil Conservation Service, 168 p. U.S. Fish and Wildlife Service, 2010. National Wetlands Inventory website: U.S. Department of Interior, Washington, D.C. http://www.fws.gov/wetlands/

In 2007, the U.S. Geological Survey began a low-density (1 site per 1,600 sq. km., 4857 sites) geochemical and mineralogical survey of soils of the conterminous United States. Sampling and analytical protocols were developed at a workshop in 2003, and pilot studies were conducted from 2004-2007 to test and refine these recommended protocols. The final sampling protocol for the national-scale survey included, at each site, a composite sample from a depth of 0 to 5 centimeters, a composite of the soil A horizon, and a deeper sample from the soil C horizon or, if the top of the C horizon was at a depth greater than 1 meter, from a depth of approximately 80 to 100 centimeters. The <2-millimeter fraction of each sample was analyzed for a suite of 45 major and trace elements by methods that yield the total, or near-total, elemental content. The major mineralogical components in the samples from the soil A and C horizons were determined by a quantitative X-ray diffraction method using Rietveld refinement. Sampling in the conterminous U.S. was completed in 2010 with chemical and mineralogical analyses completed in May, 2013. The resulting data set provides an estimate of the abundance and spatial distribution of chemical elements and minerals in soils of the conterminous U.S. and represents a baseline for soil geochemistry and mineralogy against which future changes may be recognized and quantified. This report (1) describes the sampling, sample preparation, and analytical methods used; (2) gives details of the quality control protocols used to monitor the quality of chemical and mineralogical analyses over approximately six years, and (3) makes available the soil geochemical and mineralogical data in downloadable tables.

description: This project provides an integrated suite of vegetation and nutrient resource models of the land-margin ecosystem compatible with and undergirding other restoration models of hydrology and higher trophic levels identified as critical. This modeling project fills the gaps and needs of existing restoration models, ELM and ATLSS, for a vegetation and nutrient dynamics component and complements continuing empirical studies within the land-margin ecosystem of the Everglades restoration program. The proposed work has eight major objectives: 1. Re-measurement and analysis of mangrove permanent plots 10 years after the passage of Hurricane Andrew to verify forest structure models (SELVA-MANGRO) and to re-calibrate output accordingly. 2. Map historic marsh-mangrove ecotone boundaries in selected southwest Florida regions. 3. Survey land/water datums across the intertidal and develop tidal ebb/flow synoptic functions for incorporation into SELVA-MANGRO. 4. Site quality characterization across the mangrove landscape using ground surveys and research studies, aerial photography, and aerial videography. 5. Develop external SELVA-MANGRO model linkages and WEB-based access to SELVA-MANGRO for Everglades restoration evaluations. 6. Verify HYMAN (hydrology), NUMAN (nutrient/organic matter decomposition), and FORMAN (forest structure/primary productivity) unit ecological simulation models with application to Everglades restoration evaluations. 7. Link SALSA (Hydrology BOX model) to HYMAN and FORMAN models to develop a better link between vegetation response and hydrological fluxes to the Everglades system. 8. Conduct field and greenhouse studies on nutrient biogeochemistry and determine the effects of nutrients and hydroperiod on forest biomass allocation and soil formation.; abstract: This project provides an integrated suite of vegetation and nutrient resource models of the land-margin ecosystem compatible with and undergirding other restoration models of hydrology and higher trophic levels identified as critical. This modeling project fills the gaps and needs of existing restoration models, ELM and ATLSS, for a vegetation and nutrient dynamics component and complements continuing empirical studies within the land-margin ecosystem of the Everglades restoration program. The proposed work has eight major objectives: 1. Re-measurement and analysis of mangrove permanent plots 10 years after the passage of Hurricane Andrew to verify forest structure models (SELVA-MANGRO) and to re-calibrate output accordingly. 2. Map historic marsh-mangrove ecotone boundaries in selected southwest Florida regions. 3. Survey land/water datums across the intertidal and develop tidal ebb/flow synoptic functions for incorporation into SELVA-MANGRO. 4. Site quality characterization across the mangrove landscape using ground surveys and research studies, aerial photography, and aerial videography. 5. Develop external SELVA-MANGRO model linkages and WEB-based access to SELVA-MANGRO for Everglades restoration evaluations. 6. Verify HYMAN (hydrology), NUMAN (nutrient/organic matter decomposition), and FORMAN (forest structure/primary productivity) unit ecological simulation models with application to Everglades restoration evaluations. 7. Link SALSA (Hydrology BOX model) to HYMAN and FORMAN models to develop a better link between vegetation response and hydrological fluxes to the Everglades system. 8. Conduct field and greenhouse studies on nutrient biogeochemistry and determine the effects of nutrients and hydroperiod on forest biomass allocation and soil formation.

Geochemical and mineralogical maps along with a histogram, boxplot, and empirical cumulative distribution function plot for each element or mineral whose data are provided in DS-801

Map Direct focus to show Contamination locations. Please refer to https://prodapps.dep.state.fl.us/dep-clnup/ for more information. Originally created 04/26/2012, and moved to Map Direct Lite on 11/19/2014. Application URL updated 4/12/2024. Please contact GIS.Librarian@FloridaDEP.gov for more information.

Environmentally sensitive areas mapping NHW (Normal High Water) for waterbodies in Tallahassee and Leon County, Florida. These features are updated with imagery, digital elevation models (Lidar), and field observations on and as needed basis. While some waterbody features like stormwater ponds are not considered environmentally sensative, they have been inincluded in this set of features as reference and because they do participate in the drainage sytems of Tallahassee and Leon County. Methodology: The initial data source was the original waterbody data from the 1988 ESA maps. Additionally, Hydro features Type=215 from 2009, 2012, and 2015 Landbase datasets are added. The newly added waterbodies are examined to determine if they normally hold water by comparing aerial photography. Sources used as reference for the delineation process are the 2009 DEM, 2012 DEM, and the 2015 DEM, Leon County Soil Survey, National Wetlands Inventory (NWI) features which have Water1 attribute classifications of F, G, or H, 1996 Additional data sources include , USGS Digital Ortho Quarter Quadrangle imagery, orthoimagery from 1996, 2009, 2012, and 2015, permit files, and field verification. All polygon features are attributed and assigned a confidence interval. 4=walked boundary; 3=field verified, 2=sources agree, 1=sources disagree. Confidence intervals can change as a result of further investigation. Supplemental Information Waterbody features were obtained through remote sensing and topographic analysis. Site specific study needed to verify the normal high water line.

High Quality Successional and Native Forests of Tallahassee and Leon County, Florida. This feature class was created as part of the Environmentally Sensitive Areas (ESA) Mapping Project.The Native and High Quality Successional Forests were mapped in GIS as part of a larger Environmentally Sensitive Areas (ESA) mapping project, which was a collaborative effort between the city and county growth management departments, the planning department and TLCGIS. This project was driven out of a need for better data that was standardized county-wide to support environmental resource planning, protection and regulation, etc. It was also needed to support public works and emergency management. Additionally, the Tallahassee-Leon County Comprehensive Plan Conservation Element Objectives and Policies required compilation and maintenance of maps of conservation and preservation features, effective 1990--the same year that the GIS Interlocal office was created. Prior to this in 1988, the original ESA data layers were mapped by environmental professionals and stormwater engineers working in what was then called the Leon County Department of Public Works. The data was drawn onto USGS quad maps (base) using mylar overlays (scale 24,000)--drainage basins were also mapped. It was supported by a Department of Community Affairs grant, and there was assistance from the FSU Geography Department. The Native and HQS Forests were identified using DOT Vegetation Inventory Maps, USFS Soils and Vegetation maps, some aerial photo review, and ground truthing.In 1996 a proposal was brought to the GIS Executive Committee by the departments as an ESA Remap Project that would map the layers (also Greenways) at larger scales of 1,200 and 2,400 using the new GIS base map. The project was funded by the TLCGIS. The layers were mapped by OPS environmental/biology students and graduates with oversight by the departments.Native and HQS Forests originally mapped on the quad maps were then heads up digitized into the GIS using base map data and two sets of digital aerial photos from '94 & '96. Hard copy aerials from '37 and '76 were also used for comparison. In the southeastern US mixed hardwood/beech-magnolia forests have a fairly distinct spectral signature on the false color infrared imagery which staff learned and were trained to recognize, with ground truthing where possible (property owners were called for access). The upland pine/longleaf and pine oak hickory signatures were not as easy to identify. The goal of the ESA Remap was to identify forests that had been continually forested since at least '37 (less so for longleaf forests where bulk of the diversity is in the groundcover). The soil and contour layers, with the aerials were used to find additional areas that hadn't been previously mapped. So, essentially it was a process of identifying a specific signature and also the environmental conditions (soils, slope, aspect, topography) that would have supported or allowed a forest type to have been protected throughout history. For example, beech-magnolia forests are often found on steep slopes that were historically difficult to access for logging or around wetland/floodplain areas that would have potentially limited logging due to saturated soils. Florida Natural Areas Inventory Element Occurrence data and Florida Fish and Wildlife Conservation Commission Closing the Gaps data were also used.

A point feature class of open DERM Contaminated sites within Miami-Dade County. See phase code for the status of the site. Contaminated Sites, identifies properties where environmental contamination has been documented in the soil or groundwater. Facilities get listed as a contaminated site by a DERM inspector who finds a violation on the property. Facilities that store potentially contaminated materials are permitted and/or tracked by DERM. A site is removed from the active contaminated sites layer/list when the site is found by DERM to be cleaned up.Updated: Weekly The data was created using: Projected Coordinate System: WGS_1984_Web_Mercator_Auxiliary_SphereProjection: Mercator_Auxiliary_Sphere