The Everglades Depth Estimation Network (EDEN) is an integrated network of water-level gages, interpolation models, web applications, and decision support tools that generates daily water-level data and derived hydrologic data across the freshwater part of south Florida's Greater Everglades. EDEN provides continuous daily water-level and depth surfaces on a 400-meter grid using an interpolation algorithm, a network of over 200 gaging stations, and a digital elevation model (DEM). The water-level surfaces cover an area of 9,132 square kilometers and the water depth surfaces cover an area of 7,491 square kilometers. For a subset of gaging stations, ground elevation measurements were taken to better understand the elevation in the area surrounding the gaging station. The mean, maximum, and minimum ground elevation measurements are provided for the area within a 10-meter radius of the water level gaging station. The major vegetation community type was also recorded. Within a 400-meter radius, a secondary vegetation community type was recorded when possible, along with mean, maximum, and minimum ground elevation measurements.

The surface elevation table (SET)-marker horizon (MH) approach (SET-MH, together) is a method for quantifying surface elevation change through measurements of surface and subsurface processes that control wetland soil elevation. This dataset combines SET-MH data from five different U.S. Geological Survey efforts to monitor surface elevation change in the coastal wetlands of the Greater Everglades region of south Florida. Data from these efforts have been used in the publications by Cahoon and Lynch (1997), Whelan et al. (2005, 2009), Smith et al. (2009), McKee (2011), Breithaupt et al. (2020), Feher et al. (2020), Howard et al. (2020), and Osland et al. (2020). Although some of these data have previously been released on ScienceBase as individual datasets (see Feher et al. 2019, Howard et al. 2019, Cormier et al. 2020, and Lynch et al. 2020), the dataset presented here combines these individual data releases into one single product and also includes new data that were collected after the original release of these data. These data were combined with additional SET-MH data provided by collaborators at the South Florida Water Management District, Florida International University, U.S. Fish and Wildlife Service, and U.S. National Park Service to provide a regional synthesis of available data regarding surface elevation change dynamics in coastal wetlands of the Greater Everglades.

The Everglades National Park vegetation mapping project is part of the Comprehensive Everglades Restoration Plan (CERP). It is a cooperative effort between the South Florida Water Management District (SFWMD), the United States Army Corps of Engineers (USACE), and the National Park Service Vegetation Mapping Inventory Program (NPS VMI). The goal of this project is to produce a spatially and thematically accurate vegetation map of Everglades National Park (EVER) prior to the completion of restoration efforts associated with CERP. This spatial product will serve as a record of baseline vegetation conditions for the purpose of: (1) documenting changes to the spatial extent, pattern, and proportion of plant communities within EVER as they respond to hydrologic modifications resulting from the implementation of the CERP; and (2) providing vegetation and land-cover information to NPS park managers and scientists for use in resource management, research, and monitoring. The vegetation map of EVER covers an area of 4,482.2 square kilometers (1.108 million acres [ac]) and consists of four mapping regions: Region 1 – Shark River Slough/Long Pine Key; Region 2 – The Southeast Saline Everglades; Region 3 – The Southwest Coastal Everglades; and Region 4 – The Northwest Coastal Everglades. Region 1 was mapped by the SFWMD and USACE while Regions 2-4 were mapped by the South Florida Caribbean Network (SFCN). Photo-interpretation on the map was performed by superimposing a 50 × 50-meter (164 × 164-feet [ft] or 0.25 hectare [0.61 ac]) grid cell vector matrix over stereoscopic, 30 centimeters (11.8 inches) spatial resolution, color-infrared aerial imagery, acquired by the SFWMD in 2009, on a digital photogrammetric workstation. Photo-interpreters identified the dominant community in each cell by applying majority-rule algorithms, recognizing community-specific spectral signatures, and referencing an extensive ground-truth database. The dominant vegetation community within each grid cell was classified using a hierarchical classification system developed for this project. Additionally, photo-interpreters categorized the absolute cover of invasive species and cattails (Typha sp.) detected as either: Sparse (10–49%), Dominant (50–89%), or Monotypic (90–100%).

Soil porewater (30cm and 60cm depth) was sampled for specific conductance, salinity and temperature in the southwest coastal Everglades, Everglades National Park from 1997-2012 at four sampling locations. Principal sampling location (HR) was located adjacent the Harney River and had five sampling sites (~ 60m apart) along a 300m N-S transect in a coastal mangrove fringe forest sampled from 1997-2011. Porewater was sampled from 2002-2012 at three secondary locations: Tarpon Bay (TB), Shark River (SR) and Shark Slough (SS). At each of these sampling locations, there were at least three 30cm and three 60cm porewater sampling pipes.

Geodesign Technologies conducted an initial assessment of the development likelihood and conservation priority for the Everglades Headwaters National Wildlife Refuge and Conservation Area study region in central Florida. Geodesign used two prior analyses as the basis for this assessment, both of which are at a statewide Florida scale. The University of Florida's CLIP3 (Critical Lands and Waters Identification Project 3.0; Oetting et. al 2014) was the basis for the biodiversity assessment, and their prior statewide scenario simulations (Vargas et al. 2014) were used as an indicator of likelihood of development under a suite of divergent statewide policies. References: 1. Oetting, J., T. Hoctor, and M. Volk. 2014. Critical Lands and Waters Identification Project (CLIP): Version 3.0. Technical Report - February 2014. 110 pp. 2. Vargas, J.C., Flaxman, and B. Fradkin. 2014. Landscape Conservation and Climate Change Scenarios for the State of Florida: A Decision Support System for Strategic Conservation. Summary for Decision Makers. GeoAdaptive LLC, Boston, MA and Geodesign Technologies Inc., San Francisco CA. 22 pp.

Timeseries data from 'EVERGLADES 5A IN C-111 BASIN NR HOMESTEAD, FL (USGS 251716080342100)' (gov_usgs_nwis_251716080342100)

The Everglades Depth Estimation Network (EDEN) produces daily depth estimates for the Greater Everglades. This data release includes geospatial data to produce depth estimates for the EDEN from updated digital elevation models. The data release includes three main types of data: 1) 10-m digital elevation models (DEMs) with elevation uncertainty treatment; 2) 50-m DEMs with elevation uncertainty treatment; and 3) spatial metadata for the DEMs used. Specifically, this dataset includes accuracy information by zone. These data address elevation error by using Monte Carlo simulations with 1,000 iterations with observations of elevation error in vegetated wetlands and assumptions error in vegetated non-wetland areas and non-vegetated areas. On a per-pixel basis, we created raster surfaces that represented the minimum elevation, maximum elevation, and percentiles (1 to 99). We determined the “best” elevation percentiles for each EDEN zone (Haider and others, 2020) based on the mean bias error, which was calculated for the difference between the USGS high-accuracy elevation dataset (HAED; Jones and Price, 2007) and the DEM. In this case, the percentile DEM with the mean bias error closest to zero for each zone was selected. All zones were combined to create a seamless mosaic. For each zone, upper and lower elevation estimates were determined based on a general rule that selected the percentile that was the farthest from the “best” percentile but had a mean bias error that was within (+/-) 5 cm. Areas in lower and upper estimate DEMs that have “NoData” values indicate that the there was no percentile that could be used to satisfy this rule. For example, a zone may not have a lower estimate if the “best” estimate was the minimum raster. A zone may not have an upper estimate if the next percentile had a mean bias error that was greater than 5 cm.

Restoration of the Florida Everglades, a substantial wetland ecosystem within the United States, is one of the largest ongoing restoration projects in the world. Decision-makers and managers within the Everglades ecosystem rely on ecological models forecasting indicator wildlife response to changes in the management of water flows within the system. One such indicator of ecosystem health, the presence of wading bird communities on the landscape, is currently assessed using three species distribution models that assume perfect detection and report output on different scales that are challenging to compare against one another. We sought to use current advancements in species distribution modeling to improve models of Everglades wading bird distribution. Using a joint species distribution model that accounted for imperfect detection, we modeled the presence of nine species of wading bird simultaneously in response to annual hydrologic conditions and landscape characteristics within the Everglades system. Our resulting model improved upon the previous model in three key ways: 1) the model predicts probability of occupancy for the nine species on a scale of 0-1, making the output more intuitive and easily comparable for managers and decision-makers that must consider the responses of several species simultaneously; 2) through joint species modeling, we were able to consider rarer species within the modeling that otherwise are detected in too few numbers to fit as individual models; and 3) the model explicitly allows detection probability of species to be less than 1 which can reduce bias in the site occupancy estimates. These improvements are essential as Everglades restoration continues and managers require models that consider the impacts of water management on key indicator wildlife such as the wading bird community.

Timeseries data from 'EVERGLADES 1 IN C-111 BASIN NR HOMESTEAD, FL (USGS 251946080254800)' (gov_usgs_nwis_251946080254800)

This dataset consists of a file geodatabase containing the digitized ecotone boundaries between mangroves and freshwater marsh in 2013 and 2019 at 14 systematically selected segments in Big Cypress National Preserve and Everglades National Park (southwest Florida, USA).