Data Release from the High-Flow Field Experiments to Inform Everglades Restoration: Experimental Data 2010 to 2018. Data were obtained from field sites located in the Everglades between two canals (L-67A and L-67C) from 2010 to 2018. During this time, five major controlled flow releases occurred by opening the culvert S152 on canal L-67A. Data consist of water velocity (continuous and discrete), water levels (continuous and discrete), suspended sediment concentration, load and flux (discrete), suspended phosphorus concentration, load and flux (discrete), grainsize distribution (continuous and discrete), biogeochemistry (discrete), water quality (continuous), temperature (continuous) and vegetation (discrete).

Data were collected between 2010 and 2022 in a research area of the Everglades known as the Decompartmentalization Physical Model (DPM), a wetland area in the central Everglades that includes canals and levees bordering Water Conservation Area 3A (WCA-3A) to the northwest and Water Conservation Area 3B (WCA-3B) to the southeast. During the twelve-year study period more than ten major controlled flow releases occurred by opening the S-152 culverts on canal L-67A that released experimental high flows through the wetland. Data consist of water levels (continuous and discrete), water velocity (continuous and discrete), bed shear stress (discrete), suspended sediment concentration (discrete), dissolved phosphorus concentration, load, and flux (discrete), suspended phosphorus concentration, load and flux (discrete), grainsize distribution (continuous and discrete), related biogeochemistry (discrete), water quality parameters (continuous), temperature profiles (continuous), microtopography (discrete), and vegetation community type and stem density (discrete).

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.

Flow and transport of sediment and phosphorus through the low-gradient wetlands of the Everglades, FL were characterized using hydrologic, biological, geomorphic, and biogeochemical data as inputs to several types of simulations. The data were collected in the DPM (Decompartmentalization Physical Model) experimental high-flow facility in the central Everglades, FL. The DPM includes wetlands, canals, and levees bordering Water Conservation Area 3A (WCA-3A) to the northwest and Water Conservation Area 3B to the southeast. We simulated the hydrologic flow of WCA-3A water though several closely spaced culverts beneath the L-67A levee into DPM as flow spreading in a radial pattern through the wetland. The radial flow simulations characterize interactions between total flow entering though culverts beneath the L-67A levee, flow spreading angle in the wetland, vegetation roughness, water slope, water depth, and water flow speed in the wetland. Also, we used the one-dimensional transport model (OTIS) model to simulate the conservative transport of water along a central flow path through the wetland using specific conductivity as an environmental tracer. Reactive transport simulations with OTIS used observations of suspended sediment concentration (SSC) and total phosphorus (TP) to quantify the rate of removal of suspended sediment and total phosphorus from flowing water. Gross primary productivity (GPP) and ecosystem respiration (ER) along the flow path was assessed by simulating transport and reactivity of dissolved oxygen using MATLAB scripts. Furthermore, at the downstream end of the DPM we made simple steady-state water and chemical mass balance calculations to assess water, suspended sediment, and total phosphorus leaving the DPM by transport though a downstream gap in the L67-C levee into WCA-3B.

Ecological models facilitate evaluation and assessment of alternative approaches to restore the Greater Everglades ecosystem. The models of particular interest to the South Florida Water Management District for planning for the Everglades Agricultural Area (EAA) Reservoir were: (1) Cape Sable Seaside Sparrow Marl Prairie Indicator, (2) Florida apple snail (native) population model (EverSnail), (3) Wader Distribution Evaluation Modeling (WADEM), (4) Small-sized freshwater fish density, and (5) American alligator production probability (i.e., habitat suitability index (HSI)). We ran these models using hydrologic conditions (provided by the South Florida Water Management District, see Process Steps section below) for baseline and future conditions for the EAR.

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 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.

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

Ecological models facilitate evaluation of alternative approaches to restore the Greater Everglades ecosystem. However, the provision of useful and accessible models is a challenge because there is often a disconnect between model output and its use by decision makers. Joint Ecosystem Modeling (JEM) meets this challenge by providing ecological model output tailored to management decisions. Ecological models (i.e., ecological planning tools) were developed and used by JEM during the Central Everglades Planning Project to evaluate potential effects to natural resources in the impacted areas. There is a desire by the planning agencies and bureaus involved in the Western Everglades Restoration Project (WERP) to use these same tools for WERP evaluations of alternative restoration plans. The models of particular interest to the WERP Ecological Subteam are: (1) Marl Prairie Habitat Suitability Index in conjunction with the (2) Cape Sable Seaside Sparrow Helper, (3) (native) Florida apple snail population model (EverSnail), (4) Wading bird distribution and evaluation models (WADEM), (5) Small-sized freshwater fish density with days since drydown (DSD) metric, and (6) Alligator Habitat Suitability Index (HSI). This is round 5 of ecological modeling for the WERP.

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. This release provides the code to update the vegetation module of EVA, validate the updated module, and provides the process and outputs of a sensitivity analysis of the module. Key updates include expanding the number of vegetation classes predicted from 6 to 11 classes, simplifying the inputs to the module, and increasing the number of vegetation observations used to parameterize the network. The validation of the module includes the process to calculate receiver operating characteristic curves and their associated area under the curve values, multi-class Brier scores, and classification error loss from a 10-fold cross-validation on the network. The sensitivity analyses explore the period of record under scenarios of altered hydrology or salinity and determine the most likely vegetation outcome given the proportion of states within the period of record being explored.