Near-term spatial hydrologic forecasting in Everglades, USA for landscape planning and ecological forecasting

Pearlstine, Leonard G.; Beerens, James M.; Reynolds, Gregg; Haider, Saira M.; McKelvy, Mark; Suir, Kevin; Romañach, Stephanie S.; Nestler, Jennifer H.

doi:10.1016/j.envsoft.2020.104783

Cited by 10 publications

(6 citation statements)

References 42 publications

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“…Tidal hydrodynamics are primarily determined by predictable astronomic components, however, interactions with bathymetric features, such as reefs and marsh islands, can produce complex spatial patterns that are difficult to model (Pawlowicz et al, 2002). Similarly, seasonal watershed dynamics (e.g., groundwater and water availability) can be predicted from climate forecasts (Pearlstine et al, 2020); however, the severity and extent of flood and drought events and other factors, such as water use practices, are often difficult to predict but nonetheless determine streamflow and geomorphic characteristics (Milly et al, 2008; Tomer & Schilling, 2009). Modeling decisions must therefore be made regarding which underlying processes to represent explicitly, and which to consider as random environmental shocks or unobservable errors.…”

Section: Discussionmentioning

confidence: 99%

Quantifying uncertainty in coastal salinity regime for biological application using quantile regression

et al. 2023

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Salinity regimes in coastal ecosystems are highly dynamic and driven by complex geomorphic and hydrological processes. Estuarine biota are generally adapted to salinity fluctuation, but are vulnerable to salinity extremes. Characterizing coastal salinity regime for ecological studies therefore requires representing extremes of salinity ranges at time scales relevant to ecology (e.g., daily, monthly, and seasonally). Here, we propose a framework for modeling coastal salinity with these overall goals: (1) quantify uncertainty in salinity associated with important terrestrial and oceanographic drivers, (2) examine time scales of salinity response to river streamflow events, and (3) predict salinity continuously over space at key time scales. Salinity is modeled as quantile surfaces related to river discharge, tidal dynamics, wind, and spatial location, applied to Suwannee Sound estuary, FL, USA, where salinity has been monitored spatially since 1981. Each quantile level is regressed independently, and together they comprise a distribution of salinity uncertainty across space, with upper and lower quantiles describing salinity extremes. Effects of physical drivers on salinity are compared through four base models with various combinations of tide and wind variables, each including spatial coordinates and a single streamflow metric (in cubic meters per second). Multiple time scales of streamflow are considered by taking means across various periods, from 1 to 12 days, and at various lagged intervals prior to salinity sample, totaling 144 streamflow metrics. We found that the Suwannee coastal salinity regime is dynamic at multiple time scales and varies nonlinearly across space from the river effluence outward. Salinity increases nonlinearly with decreasing river flow rates below 200 m 3 /s, most prominently in the lower quantiles of salinity (τ = 0.05-0.25). Wind appears to have a stronger influence on salinity than astronomic tides for this estuary. The regression approach developed here can be applied to any coastal system that has sufficient spatial and temporal monitoring coverage to capture multiple flood and drought events.

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Section: Discussionmentioning

confidence: 99%

Quantifying uncertainty in coastal salinity regime for biological application using quantile regression

et al. 2023

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“…Our results are relevant to ecosystem‐based management (EBM) and multi‐species transition strategies (Gibble et al, 2020; USFWS, 2010) that aim to optimize suitable conditions across multiple species and habitats via interagency coordination. KiteNest is already integrated into larger multi‐species modeling efforts in the Everglades (Romañach & Pearlstine, 2022), including the EverForecast near‐term hydrologic forecasting application that provides multi‐species comparisons of different hydrologic scenarios (e.g., for alligators, apple snails, small fish, wading birds; Pearlstine et al, 2020; Romañach et al, 2022). Currently, an apple snail population model, EverSnail, is used in restoration planning (Darby et al, 2015) as a proxy for the snail kites.…”

Section: Discussionmentioning

confidence: 99%

Nest‐site selection model for endangered Everglade snail kites to inform ecosystem restoration

et al. 2023

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The once vast and interconnected Everglades wetland ecosystem in Florida underwent a 50% reduction in area in the 1900s, resulting in a highly compartmentalized and managed system where ecological restoration is ongoing. Everglade snail kites (Rostrhamus sociabilis plumbeus, hereafter snail kites) are federally endangered wetland specialists with a single population in Florida, USA. Predictive models can help inform restoration and natural resource management decision‐making regarding both the quantity and timing of water delivered through the hundreds of water management structures in the Everglades. We developed a spatially explicit real‐time nest‐site selection model, KiteNest, using environmental predictors of nesting for snail kites in south Florida. The results of our modeling indicate that hydrology, percent canopy cover, and proximity to recently burned areas were the most important factors associated with nest‐site selection for snail kites. Water depths between 75 and 100 cm, water recession rates between 0 and 1.25 cm/day, percent canopy covers <20%, and areas <10 km from recently burned habitat were associated with the greatest likelihood of nest‐site selection. KiteNest is applicable to natural resource management decisions in the Everglades and may be useful independently or in conjunction with other ecological models for restoration decision support.

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“…2), and in line with recent vegetation surveys conducted in CSSS subpopulations (discussed below; Sah et al 2021). Improvements to marl prairie under the COP are expected in the northeastern area of subpopulation A and the area of subpopulation F. Minor improvements under the COP are expected in the areas of subpopulations B and C, with expected minor declines in suitability in the areas of subpopulations D and E. The MPI is also incorporated into the EverForecast application that simulates near-future water levels in the Everglades (Pearlstine et al 2020) and models potential impacts on a suite of species and habitats (Haider et al 2021b). EverForecast can be used by natural resource managers to evaluate trade-offs in a multi-species and habitat management context (Romañach et al 2022) and can inform short-term decisions in water operations under the COP.…”

Section: Water Operationsmentioning

confidence: 99%

Endangered Cape Sable seaside sparrow ecology: actions towards recovery through landscape-scale ecosystem restoration

Benscoter

Romañach

2022

Endang. Species. Res.

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Understanding the ecology of endangered taxa and the factors affecting their population growth and decline is imperative for their recovery. In the southeastern USA, the Everglades wetland ecosystem supports a high diversity of species and communities, including many endemic and imperiled taxa, such as the federally endangered Cape Sable seaside sparrow Ammospiza maritima mirabilis (CSSS). The Everglades, once a completely connected wetland with a slow-moving sheet flow of water, is now compartmentalized into separated wetland units where water distribution is managed year-round. The CSSS is affected by, and at the crux of, many Everglades ecosystem restoration decisions. The CSSS faces conservation challenges, including limited habitat availability, low population numbers, dispersal limitations, and constraints on suitable breeding conditions owing to wetland water levels. Despite these challenges, ecological knowledge of the factors affecting CSSS population numbers in the context of ongoing ecosystem-level restoration can help inform protection of this bird while restoring the Everglades. Existing research shows target hydroperiods between 90 and 210 days, a minimum of 90 consecutive dry days during the breeding season, and non-breeding season fires approximately every 5-10 years may aid in CSSS recovery. There are numerous tools and models to support habitat and water management for the CSSS, and the most recent ecosystem-level water operations plan for the Everglades indicates potential for increased CSSS habitat. Here, we provide a review on the ecology of the CSSS, factors affecting population decline, and ecosystem-level restoration actions that may aid in CSSS recovery.

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Near-term spatial hydrologic forecasting in Everglades, USA for landscape planning and ecological forecasting

Cited by 10 publications

References 42 publications

Quantifying uncertainty in coastal salinity regime for biological application using quantile regression

Quantifying uncertainty in coastal salinity regime for biological application using quantile regression

Nest‐site selection model for endangered Everglade snail kites to inform ecosystem restoration

Endangered Cape Sable seaside sparrow ecology: actions towards recovery through landscape-scale ecosystem restoration

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