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Similar to most wetlands, the Florida Everglades landscape was altered to promote agriculture and human settlement, significantly altering the natural hydrologic regime. Once former agricultural land located within Everglades National Park (ENP), the Hole-in-the-Donut (HID) wetland restoration program became the first mitigation bank project in Florida. The HID program utilized a restoration technique of complete soil removal to effectively eradicate an invasive plant species. This research investigated the effects of the vegetation and soil removal on the hydrologic conditions of the HID, specifically evapotranspiration and water chemistry. Annual evapotranspiration rates were determined for the region using remotely sensed data and compared to the acres restored over a 15-year period. Groundwater and surface waters were collected from both inside the HID and from adjacent areas within ENP for major cations and anions and total nutrient concentrations. Evapotranspiration rates were found to decrease from a mean of 1083.4 mm year−1 in the year 2000 to 891.6 mm year−1 in 2014 as the restored area increased to 4893 acres. Concentrations of ions and nutrients were lower in groundwater and surface water within the restored areas compared to adjacent areas. We conclude that the lack of soil cover (along with reduced evapotranspiration rates) contributed to the lower ion and nutrient concentrations in the surface water and groundwater within the HID. 

Wetlands play a significant role in flood mitigation. Remote sensing technologies as an efficient and accurate approach have been widely applied to delineate wetlands. Supervised classification is conventionally applied for remote sensing technologies to improve the wetland delineation accuracy. However, performing supervised classification requires preparing the training data, which is also considered time-consuming and prone to human mistakes. This paper presents a deterministic topographic wetland index to delineate wetland inundation areas without performing supervised classification. The classic methods such as Normalized Difference Vegetation Index, Normalized Difference Water Index, and Topographic Wetness Index were chosen to compare with the proposed deterministic topographic method on wetland delineation accuracy. The ground truth sample points validated by Google satellite imageries from four different years were used for the assessment of the delineation overall accuracy. The results show that the proposed deterministic topographic wetland index has the highest overall accuracy (98.90%) and Kappa coefficient (0.641) among the selected approaches in this study. The findings of this paper will provide an alternative approach for delineating wetlands rapidly by using solely the LiDAR-derived Digital Elevation Model. 

Drought variability is associated with global oceanic and atmospheric teleconnections driven by, among others, the Pacific Decadal Oscillation (PDO), the Atlantic Multidecadal Oscillation (AMO), and El Niño–Southern Oscillation (ENSO). Climate teleconnections with a region’s rainfall, with drought and flooding implications, should be part of short- and long-term water management planning and operations. In this study, the link between drought and climatic drivers was assessed by using historical data from 110 years of regional rainfall in southern Florida and the Everglades. The objective was to evaluate historical drought and its link with global oceanic and atmospheric teleconnections. The Standardized Precipitation Index (SPI) assesses regional historical drought in 3-, 6-, 12-, 24-, 36-, 48-, and 60-month periods. Each of the SPIs was used to analyze the association of different magnitudes of drought with ENSO, AMO, and PDO. Historical drought evaluated in different time windows indicated that there is a wet and dry cycle in the regional hydrology, where the area is currently in the wet phase of the fluctuation since 1995 with some drought years in between. Regional historical rainfall anomaly and drought index relationships with each driver and combination of drivers were statistically evaluated. The impact of ENSO fluctuation is limited to short-period rainfall variability, whereas long-period influence is from AMO and PDO. 

The size and distribution of Phytoplankton populations are indicators of the ecological status of a water body. The chlorophyll-a (Chl-a) concentration is estimated as a proxy for the distribution of phytoplankton biomass. Remote sensing is the only practical method for the synoptic assessment of Chl-a at large spatial and temporal scales. Long-term records of ocean color data from the MODIS Aqua Sensor have proven inadequate to assess Chl-a due to the lack of a robust ocean color algorithm. Chl-a estimation in shallow and coastal water bodies has been a challenge and existing operational algorithms are only suitable for deeper water bodies. In this study, the Ocean Color 3M (OC3M) derived Chl-a concentrations were compared with observed data to assess the performance of the OC3M algorithm. Subsequently, a regression analysis between in situ Chl-a and remote sensing reflectance was performed to obtain a green-red band algorithm for coastal (case 2) water. The OC3M algorithm yielded an accurate estimate of Chl-a for deep ocean (case 1) water (RMSE = 0.007, r2 = 0.518, p < 0.001), but failed to perform well in the coastal (case 2) water of Chesapeake Bay (RMSE = 23.217, r2 = 0.009, p = 0.356). The algorithm developed in this study predicted Chl-a more accurately in Chesapeake Bay (RMSE = 4.924, r2 = 0.444, p < 0.001) than the OC3M algorithm. The study indicates a maximum band ratio formulation using green and red bands could improve the satellite estimation of Chl-a in coastal waters.