Hydrological dynamics between a coastal aquifer and the adjacent estuarine system, Biscayne Bay, South Florida

Geochemical and geophysical approaches have been used to investigate the freshwater and saltwater dynamics in the coastal Biscayne Aquifer and Biscayne Bay. Stable isotopes of oxygen and hydrogen, and concentrations of Sr2+ and Ca2+ were combined in two geochemical mixing models to provide estimates of the various freshwater inputs (precipitation, canal water, and groundwater) to Biscayne Bay and the coastal canal system in South Florida. Shallow geophysical electromagnetic and direct current resistivity surveys were used to image the geometry and stratification of the saltwater mixing zone in the near coastal (less than 1km inland) Biscayne Aquifer. The combined stable isotope and trace metal models suggest a ratio of canal input-precipitation-groundwater of 38%–52%–10% in the wet season and 37%–58%–5% in the dry season with an error of 25%, where most (20%) of the error was attributed to the isotope regression model, while the remaining 5% error was attributed to the Sr2+/Ca2+ mixing model. These models suggest rainfall is the dominate source of freshwater to Biscayne Bay. For a bay-wide water budget that includes saltwater and freshwater mixing, fresh groundwater accounts for less than 2% of the total input. A similar Sr 2+/Ca2+ tracer model indicates precipitation is the dominate source in 9 out of 10 canals that discharge into Biscayne Bay. The two-component mixing model converged for 100% of the freshwater canal samples in this study with 63% of the water contributed to the canals coming from precipitation and 37% from groundwater inputs ±4%. There was a seasonal shift from 63% precipitation input in the dry season to 55% precipitation input in the wet season. The three end-member mixing model converged for only 60% of the saline canal samples possibly due to non-conservative behavior of Sr2+ and Ca2+ in saline groundwater discharging into the canal system. Electromagnetic and Direct Current resistivity surveys were successful at locating and estimating the geometry and depth of the freshwater/saltwater interface in the Biscayne Aquifer at two near coastal sites. A saltwater interface that deepened as the survey moved inland was detected with a maximum interpreted depth to the interface of 15 meters, approximately 0.33 km inland from the shoreline. ^

Mitigating congestion by integrating time forecasting and realtime information aggregation in cellular networks

An iterative travel time forecasting scheme, named the Advanced Multilane Prediction based Real-time Fastest Path (AMPRFP) algorithm, is presented in this dissertation. This scheme is derived from the conventional kernel estimator based prediction model by the association of real-time nonlinear impacts that caused by neighboring arcs’ traffic patterns with the historical traffic behaviors. The AMPRFP algorithm is evaluated by prediction of the travel time of congested arcs in the urban area of Jacksonville City. Experiment results illustrate that the proposed scheme is able to significantly reduce both the relative mean error (RME) and the root-mean-squared error (RMSE) of the predicted travel time. To obtain high quality real-time traffic information, which is essential to the performance of the AMPRFP algorithm, a data clean scheme enhanced empirical learning (DCSEEL) algorithm is also introduced. This novel method investigates the correlation between distance and direction in the geometrical map, which is not considered in existing fingerprint localization methods. Specifically, empirical learning methods are applied to minimize the error that exists in the estimated distance. A direction filter is developed to clean joints that have negative influence to the localization accuracy. Synthetic experiments in urban, suburban and rural environments are designed to evaluate the performance of DCSEEL algorithm in determining the cellular probe’s position. The results show that the cellular probe’s localization accuracy can be notably improved by the DCSEEL algorithm. Additionally, a new fast correlation technique for overcoming the time efficiency problem of the existing correlation algorithm based floating car data (FCD) technique is developed. The matching process is transformed into a 1-dimensional (1-D) curve matching problem and the Fast Normalized Cross-Correlation (FNCC) algorithm is introduced to supersede the Pearson product Moment Correlation Co-efficient (PMCC) algorithm in order to achieve the real-time requirement of the FCD method. The fast correlation technique shows a significant improvement in reducing the computational cost without affecting the accuracy of the matching process.

Rapid responses of vegetation to hydrological changes in Taylor Slough, Everglades National Park, Florida, USA

We analyzed the dynamics of freshwater marsh vegetation of Taylor Slough in eastern Everglades National Park for the 1979 to 2003 period, focusing on cover of individual plant species and on cover and composition of marsh communities in areas potentially influenced by a canal pump station (‘‘S332’’) and its successor station (‘‘S332D’’). Vegetation change analysis incorporated the hydrologic record at these sites for three intervals: pre-S332 (1961–1980), S332 (1980–1999), post-S332 (1999–2002). During S332 and post-S332 intervals, water level in Taylor Slough was affected by operations of S332 and S332D. To relate vegetation change to plot-level hydrological conditions in Taylor Slough, we developed a weighted averaging regression and calibration model (WA) using data from the marl prairies of Everglades National Park and Big Cypress National Preserve. We examined vegetation pattern along five transects. Transects 1–3 were established in 1979 south of the water delivery structures, and were influenced by their operations. Transects 4 and 5 were established in 1997, the latter west of these structures and possibly under their influence. Transect 4 was established in the northern drainage basin of Taylor Slough, beyond the likely zones of influence of S332 and S332D. The composition of all three southern transects changed similarly after 1979. Where muhly grass (Muhlenbergia capillaris var. filipes) was once dominant, sawgrass (Cladium jamaicense), replaced it, while where sawgrass initially predominated, hydric species such as spikerush (Eleocharis cellulosa Torr.) overtook it. Most of the changes in species dominance in Transects 1–3 occurred after 1992, were mostly in place by 1995–1996, and continued through 1999, indicating how rapidly vegetation in seasonal Everglades marshes can respond to hydrological modifications. During the post-S332 period, these long-term trends began reversing. In the two northern transects, total cover and dominance of both muhly grass and sawgrass increased from 1997 to 2003. Thus, during the 1990’s, vegetation composition south of S332 became more like that of long hydroperiod marshes, but afterward it partially returned to its 1979 condition, i.e., a community characteristic of less prolonged flooding. In contrast, the vegetation change along the two northern transects since 1997 showed little relationship to hydrologic status.

Forecasting Responses of Seagrass Distributions to Changing Water Quality Using Monitoring Data

Extensive data sets on water quality and seagrass distributions in Florida Bay have been assembled under complementary, but independent, monitoring programs. This paper presents the landscape-scale results from these monitoring programs and outlines a method for exploring the relationships between two such data sets. Seagrass species occurrence and abundance data were used to define eight benthic habitat classes from 677 sampling locations in Florida Bay. Water quality data from 28 monitoring stations spread across the Bay were used to construct a discriminant function model that assigned a probability of a given benthic habitat class occurring for a given combination of water quality variables. Mean salinity, salinity variability, the amount of light reaching the benthos, sediment depth, and mean nutrient concentrations were important predictor variables in the discriminant function model. Using a cross-validated classification scheme, this discriminant function identified the most likely benthic habitat type as the actual habitat type in most cases. The model predicted that the distribution of benthic habitat types in Florida Bay would likely change if water quality and water delivery were changed by human engineering of freshwater discharge from the Everglades. Specifically, an increase in the seasonal delivery of freshwater to Florida Bay should cause an expansion of seagrass beds dominated by Ruppia maritima and Halodule wrightii at the expense of the Thalassia testudinum-dominated community that now occurs in northeast Florida Bay. These statistical techniques should prove useful for predicting landscape-scale changes in community composition in diverse systems where communities are in quasi-equilibrium with environmental drivers.

Hydrological Conditions Control P Loading and Aquatic Metabolism in an Oligotrophic, Subtropical Estuary

Using high-resolution measures of aquatic ecosystem metabolism and water quality, we investigated the importance of hydrological inputs of phosphorus (P) on ecosystem dynamics in the oligotrophic, P-limited coastal Everglades. Due to low nutrient status and relatively large inputs of terrestrial organic matter, we hypothesized that the ponds in this region would be strongly net heterotrophic and that pond gross primary production (GPP) and respiration (R) would be the greatest during the “dry,” euhaline estuarine season that coincides with increased P availability. Results indicated that metabolism rates were consistently associated with elevated upstream total phosphorus and salinity concentrations. Pulses in aquatic metabolism rates were coupled to the timing of P supply from groundwater upwelling as well as a potential suite of hydrobiogeochemical interactions. We provide evidence that freshwater discharge has observable impacts on aquatic ecosystem function in the oligotrophic estuaries of the Florida Everglades by controlling the availability of P to the ecosystem. Future water management decisions in South Florida must include the impact of changes in water delivery on downstream estuaries.

Forecasting enrollment in differential assessment programs using cellular automata

Urban growth models have been used for decades to forecast urban development in metropolitan areas. Since the 1990s cellular automata, with simple computational rules and an explicitly spatial architecture, have been heavily utilized in this endeavor. One such cellular-automata-based model, SLEUTH, has been successfully applied around the world to better understand and forecast not only urban growth but also other forms of land-use and land-cover change, but like other models must be fed important information about which particular lands in the modeled area are available for development. Some of these lands are in categories for the purpose of excluding urban growth that are difficult to quantify since their function is dictated by policy. One such category includes voluntary differential assessment programs, whereby farmers agree not to develop their lands in exchange for significant tax breaks. Since they are voluntary, today’s excluded lands may be available for development at some point in the future. Mapping the shifting mosaic of parcels that are enrolled in such programs allows this information to be used in modeling and forecasting. In this study, we added information about California’s Williamson Act into SLEUTH’s excluded layer for Tulare County. Assumptions about the voluntary differential assessments were used to create a sophisticated excluded layer that was fed into SLEUTH’s urban growth forecasting routine. The results demonstrate not only a successful execution of this method but also yielded high goodness-of-fit metrics for both the calibration of enrollment termination as well as the urban growth modeling itself.

A Hydrological Budget (2002–2008) for a Large Subtropical Wetland Ecosystem Indicates Marine Groundwater Discharge Accompanies Diminished Freshwater Flow

Water budget parameters are estimated for Shark River Slough (SRS), the main drainage within Everglades National Park (ENP) from 2002 to 2008. Inputs to the water budget include surface water inflows and precipitation while outputs consist of evapotranspiration, discharge to the Gulf of Mexico and seepage losses due to municipal wellfield extraction. The daily change in volume of SRS is equated to the difference between input and outputs yielding a residual term consisting of component errors and net groundwater exchange. Results predict significant net groundwater discharge to the SRS peaking in June and positively correlated with surface water salinity at the mangrove ecotone, lagging by 1 month. Precipitation, the largest input to the SRS, is offset by ET (the largest output); thereby highlighting the importance of increasing fresh water inflows into ENP for maintaining conditions in terrestrial, estuarine, and marine ecosystems of South Florida.

Possible climate change impacts on the hydrological and vegetative character of Everglades National Park, Florida

The increasing threat of global climate change is predicted to have immense influences on ecosystems worldwide, but could be particularly severe to vulnerable wetland environments such as the Everglades. This work investigates the impact global climate change could have on the hydrologic and vegetative makeup of Everglades National Park (ENP) under forecasted emissions scenarios. Using a simple stochastic model of aboveground water levels driven by a fluctuating rainfall input, we link across ENP a location's mean depth and percent time of inundation to the predicted changes in precipitation from climate change. Changes in the hydrologic makeup of ENP are then related to changes in vegetation community composition through the use of relationships developed between two publically available datasets. Results show that under increasing emissions scenarios mean annual precipitation was forecasted to decrease across ENP leading to a marked hydrologic change across the region. Namely, areas were predicted to be shallower in average depth of standing water and inundated less of the time. These hydrologic changes in turn lead to a shift in ENP's vegetative makeup, with xeric vegetative communities becoming more numerous and hydric vegetative communities becoming scarcer. Noticeably, the most widespread of vegetative communities, sawgrass, decreases in abundance under increasing emissions scenarios. These results are an important indicator of the effects climate change may have on the Everglades region and raise important management implications for those seeking to restore this area to its historical hydrologic and vegetative condition.

Hydrological drivers of wetland vegetation community distribution within Everglades National Park, Florida

The influence of hydrological dynamics on vegetation distribution and the structuring of wetland environments is of growing interest as wetlands are modified by human action and the increasing threat from climate change. Hydrological properties have long been considered a driving force in structuring wetland communities. We link hydrological dynamics with vegetation distribution across Everglades National Park (ENP) using two publicly available datasets to study the probability structure of the frequency, duration, and depth of inundation events along with their relationship to vegetation distribution. This study is among the first to show hydrologic structuring of vegetation communities at wide spatial and temporal scales, as results indicate that the percentage of time a location is inundated and its mean depth are the principal structuring variables to which individual communities respond. For example, sawgrass, the most abundant vegetation type within the ENP, is found across a wide range of time inundated percentages and mean depths. Meanwhile, other communities like pine savanna or red mangrove scrub are more restricted in their distribution and found disproportionately at particular depths and inundations. These results, along with the probabilistic structure of hydropatterns, potentially allow for the evaluation of climate change impacts on wetland vegetation community structure and distribution.

Survival and growth responses of eight Everglades tree species along an experimental hydrological gradient on two tree island types

Questions: How are the early survival and growth of seedlings of Everglades tree species planted in an experimental setting on artificial tree islands affected by hydrology and substrate type? What are the implications of these responses for broader tree island restoration efforts? Location: Loxahatchee Impoundment Landscape Assessment (LILA), Boynton Beach, Florida, USA. Methods: An experiment was designed to test hydrological and substrate effects on seedling growth and survivorship. Two islands – a peat and a limestone-core island representing two major types found in the Everglades – were constructed in four macrocosms. A mixture of eight tree species was planted on each island in March of 2006 and 2007. Survival and height growth of seedlings planted in 2006 were assessed periodically during the next two and a half years. Results: Survival and growth improved with increasing elevation on both tree island substrate types. Seedlings' survival and growth responses along a moisture gradient matched species distributions along natural hydrological gradients in the Everglades. The effect of substrate on seedling performance showed higher survival of most species on the limestone tree islands, and faster growth on their peat-based counterparts. Conclusions: The present results could have profound implications for restoration of forests on existing landforms and artificial creation of tree islands. Knowledge of species tolerance to flooding and responses to different edaphic conditions present in wetlands is important in selecting suitable species to plant on restored tree islands

Forecasting future prevalence of type 2 diabetes mellitus in Syria

Background Type 2 diabetes mellitus (T2DM) is increasingly becoming a major public health problem worldwide. Estimating the future burden of diabetes is instrumental to guide the public health response to the epidemic. This study aims to project the prevalence of T2DM among adults in Syria over the period 2003–2022 by applying a modelling approach to the country’s own data. Methods Future prevalence of T2DM in Syria was estimated among adults aged 25 years and older for the period 2003–2022 using the IMPACT Diabetes Model (a discrete-state Markov model). Results According to our model, the prevalence of T2DM in Syria is projected to double in the period between 2003 and 2022 (from 10% to 21%). The projected increase in T2DM prevalence is higher in men (148%) than in women (93%). The increase in prevalence of T2DM is expected to be most marked in people younger than 55 years especially the 25–34 years age group. Conclusions The future projections of T2DM in Syria put it amongst countries with the highest levels of T2DM worldwide. It is estimated that by 2022 approximately a fifth of the Syrian population aged 25 years and older will have T2DM.

Space-for-Time Substitution Works in Everglades Ecological Forecasting Models

Space-for-time substitution is often used in predictive models because long-term time-series data are not available. Critics of this method suggest factors other than the target driver may affect ecosystem response and could vary spatially, producing misleading results. Monitoring data from the Florida Everglades were used to test whether spatial data can be substituted for temporal data in forecasting models. Spatial models that predicted bluefin killifish (Lucania goodei) population response to a drying event performed comparably and sometimes better than temporal models. Models worked best when results were not extrapolated beyond the range of variation encompassed by the original dataset. These results were compared to other studies to determine whether ecosystem features influence whether space-for-time substitution is feasible. Taken in the context of other studies, these results suggest space-for-time substitution may work best in ecosystems with low beta-diversity, high connectivity between sites, and small lag in organismal response to the driver variable.

Space-for-Time Substitution Works in Everglades Ecological Forecasting Models

Space-for-time substitution is often used in predictive models because long-term time-series data are not available. Critics of this method suggest factors other than the target driver may affect ecosystem response and could vary spatially, producing misleading results. Monitoring data from the Florida Everglades were used to test whether spatial data can be substituted for temporal data in forecasting models. Spatial models that predicted bluefin killifish (Lucania goodei) population response to a drying event performed comparably and sometimes better than temporal models. Models worked best when results were not extrapolated beyond the range of variation encompassed by the original dataset. These results were compared to other studies to determine whether ecosystem features influence whether space-for-time substitution is feasible. Taken in the context of other studies, these results suggest space-fortime substitution may work best in ecosystems with low beta-diversity, high connectivity between sites, and small lag in organismal response to the driver variable.

A study to adapt various sales forecasting methods for use by military club managers

The purpose of this study is to adapt and combine the following methods of sales forecasting: Classical Time-Series Decomposition, Operationally Based Data and Judgmental Forecasting for use by military club managers.