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Digital elevation models for the Everglades Depth Estimation Network with elevation uncertainty treatment (ver. 2.0, March 2025)
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. 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 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. In version 2.0, we resolved issues with overestimation of ground elevation, which led to underestimated water depths, in parts of Water Conservation Areas by using a land cover map and estimated depth at the time of light detection and ranging (lidar) data. For more information, see the processing steps.
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Digital elevation models for the Everglades Depth Estimation Network with elevation uncertainty treatment (ver. 2.0, March 2025)
공공데이터포털
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.
Digital Elevation Model (DEM) at 50 m resolution for the Everglades Depth Estimation Network (EDEN)
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Since its inception, the Everglades Depth Estimation Network (EDEN) has used the High Accuracy Elevation Dataset (HAED) digital elevation model (DEM) to provide scientists and managers with continuous water depth surfaces, derived from interpolated water stage, on a 400 X 400 meter grid. A new, high resolution LiDAR-based DEM is available through a collaboration between Everglades National Park (ENP) and the U.S. Geological Survey (USGS) 3D Elevation Program (3DEP). This new DEM covers the southern part of the EDEN extent, including a large area of ENP and a portion of southeast Big Cypress National Preserve. It is provided at a resolution of 0.5 meters but contains data voids in surface water areas where the LiDAR was unable to provide adequate return (which can be caused by submerged vegetation or high turbidity). We have used multiple methods to fill these data voids and create a continuous high-resolution product for scientists, managers, and other EDEN users in the Greater Everglades. The DEM provided here was aggregated to a 50 m resolution to maintain an easily downloadable file size.
EverWaders species distribution model development and output in the Greater Everglades from 2000-2009
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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.
EverWaders species distribution model development and output in the Greater Everglades from 2000-2009
공공데이터포털
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.
Hydrologic scenarios and ecological model output used to explore potential sea-level rise scenarios on ecological models used in Everglades restoration planning
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One of the largest and most expensive restoration efforts in the world is occurring in the Everglades, a sub-tropical freshwater wetland system located in southern Florida. This unique ecosystem supports several endemic and endangered species, provides flood control for Florida's large urban population, and provides water for both agriculture and drinking supply within the state. The Comprehensive Everglades Restoration Plan (CERP), authorized by Congress in 2000, guides federal, state, and local efforts to build the infrastructure necessary to bring more water into the Everglades and restore its ecological integrity. The Everglades encompasses the southern coast of Florida and restoration efforts are likely to be impacted by climate-induced sea-level rise. However, currently, many project planning studies do not formally incorporate the potential impacts of sea-level rise when evaluating restoration plan outcomes. Resource managers and project planners require methods and tools to confidently incorporate scenarios of sea-level rise into their evaluations. This effort demonstrates how incorporating sea-level rise scenarios into Everglades restoration project planning can help managers decide whether projects will maintain or improve the ecological integrity of this critical system and ensure water availability for wildlife and humans. The following model outputs were generated to explore how sea-level rise may impact ecological models: two sea-level rise scenarios (an intermediate scenario of 53 cm and a high scenario of 152 cm) using the BISECT hydrodynamic model, and the Everglades Vulnerability Analysis vegetation sub-model outputs generated using the baseline, intermediate, and high sea-level rise hydrologic scenarios. We also provide R code used to visualize the outputs.
Everglades Vulnerability Analysis (EVA) modeling scripts and output
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The Everglades Vulnerability Analysis (EVA) is a series of connected Bayesian networks that models the landscape-scale response of indicators of Everglades ecosystem health to changes in hydrology and salinity on the landscape. Using the uncertainty built into each network, it also produces surfaces of vulnerability in relation to user-defined ‘ideal’ outcomes. This dataset includes the code used to build the modules and generate outputs of module outcome probabilities and landscape vulnerability.
Everglades Vulnerability Analysis (EVA) modeling scripts and output
공공데이터포털
The Everglades Vulnerability Analysis (EVA) is a series of connected Bayesian networks that models the landscape-scale response of indicators of Everglades ecosystem health to changes in hydrology and salinity on the landscape. Using the uncertainty built into each network, it also produces surfaces of vulnerability in relation to user-defined ‘ideal’ outcomes. This dataset includes the code used to build the modules and generate outputs of module outcome probabilities and landscape vulnerability.
EverForecast hydrologic output for April 2020: a six-month water stage forecast for the Greater Everglades
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Operational ecological forecasting is an emerging field that leverages ecological models in a new, cross-disciplinary way, using a real-time or nearly real-time climate forecast to project near-term ecosystem states. These applications give decision-makers lead time to anticipate and manage state changes that degrade ecosystem functions or directly impact humans. The Everglades Forecasting model (EverForecast) is an operational water stage forecast providing 6-month forecasts of daily projected, spatially continuous stage values across the Water Conservation Areas, Big Cypress National Preserve, Everglades National Park, Big Cypress Seminole Indian Reservation, and Miccosukee Federal Indian Reservation and Leased Lands. The forecast provided here starts on April 13, 2020 and ends on October 12, 2020. It includes the central tendency from the spatial position analysis and the Monte Carlo simulation outputs and processes to generate these data.
Ground-surface elevation, vegetation, and land type within approximately 10 and 400 meters of 176 water-level gaging stations in the Greater Everglades, Florida 2005-10
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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.
Soil Depth Layer for the Greater Everglades
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The Everglades Vulnerability Analysis (EVA) is a series of connected, modular Bayesian networks that predict the response of several Everglades indicators of ecosystem health to changes in hydrology, salinity, and the landscape. We created a soil depth layer for use in the sawgrass peat module by using universal kriging to estimate soil depth at a 400-meter resolution for individual management areas across the Greater Everglades footprint. We compiled soil depth data from various sources totaling 687 coordinate point locations across all sampling years which ranged from 1964 to 2018. We calculated mean soil depth when multiple depths were documented at the same location. We examined soil depth within each management area for normality and used a cube root transformation on the data to normalize the distribution where needed. We also observed spatial trends in soil depth, and therefore we examined easting and northing as covariates when kriging. We used the ‘autoKrige’ function from the R package automap to fit variograms with easting and northing and to perform kriging. We selected the best fitting model, back-transformed, and bias corrected interpolated depths and used the kriged depths to create the soil depth layer.
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