Sea level rise

While repeated transgressive and regressive sea level cycles have shaped south Florida throughout geological history, modern rates of sea level rise pose a significant risk to the structure and function of the freshwater wetland ecosystems throughout the low-lying Everglades region. Current regionally corrected sea level projections for south Florida indicate a rise of 0.42m by 2050 and 1.15m by 2100, suggesting the salinization of previously freshwater areas of the Everglades is conceivable. As freshwater areas become increasingly exposed to saltwater they experience shifts in vegetation composition, soil microbial populations, plant productivity, and physical soil properties that ultimately result in a phenomenon called peat collapse. Recent work in the Everglades has sought to further explain the mechanisms of peat collapse, however the physical changes to the peat matrix induced by saltwater intrusion are still uncertain. Moreover, the combination of physical alterations to the peat matrix associated with peat collapse and shifts in wetland salinity regimes will also likely disrupt the current carbon gas dynamics of the Everglades.

The Louisiana coastal ecosystem is experiencing increasing threats from human flood control construction, sea-level rise (SLR), and subsidence. Louisiana lost about 4,833 km2 of coastal wetlands from 1932 to 2016, and concern exists whether remaining wetlands will persist while facing the highest rate of relative sea-level rise (RSLR) in the world. Restoration aimed at rehabilitating the ongoing and future disturbances is currently underway through the implementation of the Coastal Wetlands Planning Protection and Restoration Act of 1990 (CWPPRA). To effectively monitor the progress of projects in CWPPRA, the Coastwide Reference Monitoring System (CRMS) was established in 2006. To date, more than a decade of valuable coastal, environmental, and ground elevation data have been collected and archived. This dataset offers a unique opportunity to evaluate the wetland ground elevation dynamics by linking the Rod Surface Elevation Table (RSET) measurements with environmental variables like water salinity and biophysical variables like canopy coverage. This dissertation research examined the effects of the environmental and biophysical variables on wetland terrain elevation by developing innovative machine learning based models to quantify the contribution of each factor using the CRMS collected dataset. Three modern machine learning algorithms, including Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN), were assessed and cross-compared with the commonly used Multiple Linear Regression (MLR). The results showed that RF had the best performance in modeling ground elevation with Root Mean Square Error (RMSE) of 10.8 cm and coefficient of coefficient (r) = 0.74. The top four factors contributing to ground elevation are the distance from monitoring station to closest water source, water salinity, water elevation, and dominant vegetation height.

Over drainage due to water management practices, abundance of native and rare
species, and low-lying topography makes the coastal Everglades especially vulnerable to
Sea-Level Rise (SLR). Water depths have shown to have a significant relationship to
vegetation community composition and organization while also playing a crucial role in
vegetation health throughout the Everglades. Modeling potential habitat change and loss
caused by increased water depths due to SLR requires better vertical Root Mean Square
Error (RMSE) and resolution Digital Elevation Models (DEMs) and Water Table
Elevation Models (WTEMs). In this study, an object-based machine learning approach
was developed to correct LiDAR elevation data by integrating LiDAR point data, aerial
imagery, Real Time Kinematic (RTK)-Global Positioning Systems (GPS) and total
station survey data. Four machine learning modeling techniques were compared with the
commonly used bias-corrected technique, including Random Forest (RF), Support Vector
Machine (SVM), k-Nearest Neighbor (k-NN), and Artificial Neural Network (ANN). The k-NN and RF models produced the best predictions for the Nine Mile and Flamingo study
areas (RMSE = 0.08 m and 0.10 m, respectively). This study also examined four
interpolation-based methods along with the RF, SVM and k-NN machine learning
techniques for generating WTEMs. The RF models achieved the best results for the dry
season (RMSE = 0.06 m) and the wet season (RMSE = 0.07 m) WTEMs. Previous
research in Water Depth Model (WDM) generation in the Everglades focused on a
conventional-based approach where a DEM is subtracted from a WTEM. This study
extends the conventional-based WDM approach to a rigorous-based WDM technique
where Monte Carlo simulation is used to propagate probability distributions through the
proposed SLR depth model using uncertainties in the RF-based LiDAR DEM and
WTEMs, vertical datums and transformations, regional SLR and soil accretion rates. It is
concluded that a more rigorous-based WDM technique increases the integrity of derived
products used to support and guide coastal restoration managers and planners concerned
with habitat change under the challenge of SLR. Future research will be dedicated to the
extension of this technique to model both increased water depths and saltwater intrusion
due to SLR (saltwater inundation).

A comprehensive study is conducted to evaluate global sea levels for trends and variations due to climate change and variability by using non-parametric methods. Individual and coupled effects of inter-annual ENSO, decadal PDO, multi-decadal AMO, and quasi-decadal NAO on sea levels are evaluated. Combined influences of different phases (cool or warm) of PDO, AMO, and NAO influences and ENSO are also evaluated. The results from this study showed that sea level at 60% of the sites is increasing with time with all four oscillations impacting global sea levels. AMO warm phase individually and PDO warm combined with La-Niña phase contribute to higher sea levels throughout the world. Trends and variations in sea levels are noted to be spatially non-uniform. Understanding and quantifying climate variability influenced variations in sea levels and assessment of long-term trends enables protection of coastal regions of the world from sea level rise.