Unplanned hysterectomy: a comprehensive analysis of race, ethnicity, sociodemographic factors, pregnancy complications, and cardiovascular disease risk factors.

OBJECTIVE: To understand the risk of unplanned hysterectomy (UH) in pregnant women better in association with maternal sociodemographic characteristics, cardiovascular disease (CVD) risk factors, and current pregnancy complications. DESIGN: Using Florida birth data from 2005 to 2014, we investigated the possible interactions between known risk factors of having UH, including maternal sociodemographic characteristics, maternal medical history, and other pregnancy complications. Logistic regression models were constructed. Adjusted odds ratios and 95% confidence intervals were reported. RESULTS: Several interactions were observed that significantly affected odds of UH. Compared to non-Hispanic White women, Hispanic minority women were more likely to have an UH. The overall risk of UH for women with preterm birth (<37 weeks) and concurrently had premature rupture of membranes (PRoM), uterine rupture, or a previous cesarean delivery was significantly higher than women who delivered to term and had no pregnancy complications. Women who delivered via cesarean who also had preeclampsia, PRoM, or uterine rupture had an overall increased risk of UH. Significantly decreased risk of UH was seen for Black women less than 20 years old, women of other minority races with either less than a high school degree or a college degree or greater, women of other minority races with PRoM, and women with preterm birth and diabetes compared to respective reference groups. CONCLUSIONS: Maternal race, ethnicity, CVD risk factors, and current pregnancy complications affect the risk of UH in pregnant women through complex interactions that would not be seen in unadjusted models of risk analysis.

Predicting reservoir sedimentation using multilayer perceptron - Artificial neural network model with measured and forecasted hydrometeorological data in Gibe-III reservoir, Omo-Gibe River basin, Ethiopia.

The estimation and prediction of the amount of sediment accumulated in reservoirs are imperative for sustainable reservoir sedimentation planning and management and to minimize reservoir storage capacity loss. The main objective of this study was to estimate and predict reservoir sedimentation using multilayer perceptron-artificial neural network (MLP-ANN) and random forest regressor (RFR) models in the Gibe-III reservoir, Omo-Gibe River basin. The hydrological and meteorological parameters considered for the estimation and prediction of reservoir sedimentation include annual rainfall, annual water inflow, minimum reservoir level, and reservoir storage capacity. The MLP-ANN and RFR models were employed to estimate and predict the amount of sediment accumulated in the Gibe-III reservoir using time series data from 2014 to 2022. ANN-architecture N4-100-100-1 with a coefficient of determination (R2) of 0.97 for the (80, 20) train-test approach was chosen because it showed better performance both in training and testing (validation) the model. The MLP-ANN and RFR models' performance evaluation was conducted using MAE, MSE, RMSE, and R2. The models' evaluation result revealed that the MLP-ANN model outperformed the RFR model. Regarding the train data simulation of MLP-ANN and RFR shown R2 (0.99) and RMSE (0.77); and R2 (0.97) and RMSE (1.80), respectively. On the other hand, the test data simulation of MLP-ANN and RFR demonstrated R2 (0.98) and RMSE (1.32); and R2 (0.96) and RMSE (2.64), respectively. The MLP-ANN model simulation output indicates that the amount of sediment accumulation in the Gibe-III reservoir will increase in the future, reaching 110 MT in 2030-2031, 130 MT in 2050-2051, and above 137 MTin 2071-2072.

Combined Use of Sentinel-1 SAR and Landsat Sensors Products for Residual Soil Moisture Retrieval over Agricultural Fields in the Upper Blue Nile Basin, Ethiopia.

The objective of this paper is to investigate the potential of sentinel-1 SAR sensor products and the contribution of soil roughness parameters to estimate volumetric residual soil moisture (RSM) in the Upper Blue Nile (UBN) basin, Ethiopia. The backscatter contribution of crop residue water content was estimated using Landsat sensor product and the water cloud model (WCM). The surface roughness parameters were estimated from the Oh and Baghdadi models. A feed-forward artificial neural network (ANN) method was tested for its potential to translate SAR backscattering and surface roughness input variables to RSM values. The model was trained for three inversion configurations: (i) SAR backscattering from vertical transmit and vertical receive (SAR VV) polarization only; (ii) using SAR VV and the standard deviation of surface heights ( h r m s ), and (iii) SAR VV, h r m s , and optimal surface correlation length ( l e f f ). Field-measured volumetric RSM data were used to train and validate the method. The results showed that the ANN soil moisture estimation model performed reasonably well for the estimation of RSM using the single input variable of SAR VV data only. The ANN prediction accuracy was slightly improved when SAR VV and the surface roughness parameters ( h r m s and l e f f ) were incorporated into the prediction model. Consequently, the ANN's prediction accuracy with root mean square error (RMSE) = 0.035 cm3/cm3, mean absolute error (MAE) = 0.026 cm3/cm3, and r = 0.73 was achieved using the third inversion configuration. The result implies the potential of Sentinel-1 SAR data to accurately retrieve RSM content over an agricultural site covered by stubbles. The soil roughness parameters are also potentially an important variable to soil moisture estimation using SAR data although their contribution to the accuracy of RSM prediction is slight in this study. In addition, the result highlights the importance of combining Sentinel-1 SAR and Landsat images based on an ANN approach for improving RSM content estimations over crop residue areas.

Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration.

Prediction of the groundwater nitrate concentration is of utmost importance for pollution control and water resource management. This research aims to model the spatial groundwater nitrate concentration in the Marvdasht watershed, Iran, based on several artificial intelligence methods of support vector machine (SVM), Cubist, random forest (RF), and Bayesian artificial neural network (Baysia-ANN) machine learning models. For this purpose, 11 independent variables affecting groundwater nitrate changes include elevation, slope, plan curvature, profile curvature, rainfall, piezometric depth, distance from the river, distance from residential, Sodium (Na), Potassium (K), and topographic wetness index (TWI) in the study area were prepared. Nitrate levels were also measured in 67 wells and used as a dependent variable for modeling. Data were divided into two categories of training (70%) and testing (30%) for modeling. The evaluation criteria coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and Nash-Sutcliffe efficiency (NSE) were used to evaluate the performance of the models used. The results of modeling the susceptibility of groundwater nitrate concentration showed that the RF (R2 = 0.89, RMSE = 4.24, NSE = 0.87) model is better than the other Cubist (R2 = 0.87, RMSE = 5.18, NSE = 0.81), SVM (R2 = 0.74, RMSE = 6.07, NSE = 0.74), Bayesian-ANN (R2 = 0.79, RMSE = 5.91, NSE = 0.75) models. The results of groundwater nitrate concentration zoning in the study area showed that the northern parts of the case study have the highest amount of nitrate, which is higher in these agricultural areas than in other areas. The most important cause of nitrate pollution in these areas is agriculture activities and the use of groundwater to irrigate these crops and the wells close to agricultural areas, which has led to the indiscriminate use of chemical fertilizers by irrigation or rainwater of these fertilizers is washed and penetrates groundwater and pollutes the aquifer.

A Comprehensive Review on Water Quality Parameters Estimation Using Remote Sensing Techniques.

Remotely sensed data can reinforce the abilities of water resources researchers and decision makers to monitor waterbodies more effectively. Remote sensing techniques have been widely used to measure the qualitative parameters of waterbodies (i.e., suspended sediments, colored dissolved organic matter (CDOM), chlorophyll-a, and pollutants). A large number of different sensors on board various satellites and other platforms, such as airplanes, are currently used to measure the amount of radiation at different wavelengths reflected from the water's surface. In this review paper, various properties (spectral, spatial and temporal, etc.) of the more commonly employed spaceborne and airborne sensors are tabulated to be used as a sensor selection guide. Furthermore, this paper investigates the commonly used approaches and sensors employed in evaluating and quantifying the eleven water quality parameters. The parameters include: chlorophyll-a (chl-a), colored dissolved organic matters (CDOM), Secchi disk depth (SDD), turbidity, total suspended sediments (TSS), water temperature (WT), total phosphorus (TP), sea surface salinity (SSS), dissolved oxygen (DO), biochemical oxygen demand (BOD) and chemical oxygen demand (COD).

Developing Benthic Class Specific, Chlorophyll-a Retrieving Algorithms for Optically-Shallow Water Using SeaWiFS.

This study evaluated the ability to improve Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) chl-a retrieval from optically shallow coastal waters by applying algorithms specific to the pixels' benthic class. The form of the Ocean Color (OC) algorithm was assumed for this study. The operational atmospheric correction producing Level 2 SeaWiFS data was retained since the focus of this study was on establishing the benefit from the alternative specification of the bio-optical algorithm. Benthic class was determined through satellite image-based classification methods. Accuracy of the chl-a algorithms evaluated was determined through comparison with coincident in situ measurements of chl-a. The regionally-tuned models that were allowed to vary by benthic class produced more accurate estimates of chl-a than the single, unified regionally-tuned model. Mean absolute percent difference was approximately 70% for the regionally-tuned, benthic class-specific algorithms. Evaluation of the residuals indicated the potential for further improvement to chl-a estimation through finer characterization of benthic environments. Atmospheric correction procedures specialized to coastal environments were recognized as areas for future improvement as these procedures would improve both classification and algorithm tuning.

A comparison of various artificial intelligence approaches performance for estimating suspended sediment load of river systems: a case study in United States.

Accurate and reliable suspended sediment load (SSL) prediction models are necessary for planning and management of water resource structures. More recently, soft computing techniques have been used in hydrological and environmental modeling. The present paper compared the accuracy of three different soft computing methods, namely, artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), coupled wavelet and neural network (WANN), and conventional sediment rating curve (SRC) approaches for estimating the daily SSL in two gauging stations in the USA. The performances of these models were measured by the coefficient of correlation (R), Nash-Sutcliffe efficiency coefficient (CE), root-mean-square error (RMSE), and mean absolute percentage error (MAPE) to choose the best fit model. Obtained results demonstrated that applied soft computing models were in good agreement with the observed SSL values, while they depicted better results than the conventional SRC method. The comparison of estimation accuracies of various models illustrated that the WANN was the most accurate model in SSL estimation in comparison to other models. For example, in Flathead River station, the determination coefficient was 0.91 for the best WANN model, while it was 0.65, 0.75, and 0.481 for the best ANN, ANFIS, and SRC models, and also in the Santa Clara River, amounts of this statistical criteria was 0.92 for the best WANN model, while it was 0.76, 0.78, and 0.39 for the best ANN, ANFIS, and SRC models, respectively. Also, the values of cumulative suspended sediment load computed by the best WANN model were closer to the observed data than the other models. In general, results indicated that the WANN model could satisfactorily mimic phenomenon, acceptably estimate cumulative SSL, and reasonably predict peak SSL values.

Assessing the Risk of Heart Attack: A Bayesian Kernel Machine Regression Analysis of Heavy Metal Mixtures.

Background: The assessment of heavy metals' effects on human health is frequently limited to investigating one metal or a group of related metals. The effect of heavy metals mixture on heart attack is unknown. Methods: This study applied the Bayesian kernel machine regression model (BKMR) to the 2011-2016 National Health and Nutrition Examination Survey (NHANES) data to investigate the association between heavy metal mixture exposure with heart attack. 2972 participants over the age of 20 were included in the study. Results: Results indicate that heart attack patients have higher levels of cadmium and lead in the blood and cadmium, cobalt, and tin in the urine, while having lower levels of mercury, manganese, and selenium in the blood and manganese, barium, tungsten, and strontium in the urine. The estimated risk of heart attack showed a negative association of 0.0030 units when all the metals were at their 25th percentile compared to their 50th percentile and a positive association of 0.0285 units when all the metals were at their 75th percentile compared to their 50th percentile. The results suggest that heavy metal exposure, especially cadmium and lead, may increase the risk of heart attacks. Conclusions: This study suggests a possible association between heavy metal mixture exposure and heart attack and, additionally, demonstrates how the BKMR model can be used to investigate new combinations of exposures in future studies.

Sensors Best Paper Award 2013.

Since 2011, Sensors has instituted an annual award to recognize outstanding papers that are related to sensing technologies and applications and meet the aims, scope and high standards of this journal. This year, nominations were made by the Section Editor-in-Chiefs of Sensors from among all the papers published in 2009. Reviews and full research articles were considered separately. We are pleased to announce that the following eight papers have won the Sensors Best Paper Award in 2013.

Dynamics of particles and phosphorus in canals of the Lower Everglades, Florida, USA.

Water flow (discharge) can affect water quality by influencing the concentration and transport of waterborne contaminants. The effects of discharge on phosphorus (P) and particle concentrations in managed canals, were described using concentration-discharge (C-Q) relationships, accumulation of suspended and settling particles, and the physicochemical characteristics of these particles and bed sediments. Piecewise regression analysis on C-Q relationships revealed slope inflections that denoted thresholds, where P-behavior changed from low to high discharge. The C-Q relationships generally showed higher concentrations at higher discharges. In three of the four Lower Everglades canals studied, long-term (1995-2019) lower temporal resolution data (daily to weekly) was adequate to describe the influence of discharge on P concentrations. However, in one site, the L-29 Canal, higher temporal resolution data (minutes to hours over weeks), derived from acoustic sensors, was necessary to produce C-Q relationships. In the L-29 Canal, discharge affected the transport, settling, and sediment accrual at distances from the S333 inflow structure. Sediment traps showed higher discharge led to a greater accumulation of suspended particles that were transported and settled farther downstream. Generally, downstream surface sediments in the L-29 Canal had greater organic matter, lower bulk density and higher TP than those of the upstream site, reflecting long-term effects of discharge. Understanding the effects of discharge on particles and associated nutrients, especially at discharge thresholds that lead to concentration increases, can inform the operation of managed canals to reduce contaminant loading to downstream sensitive ecosystems.

How suitable are satellite rainfall estimates in simulating high flows and actual evapotranspiration in MelkaKunitre catchment, Upper Awash Basin, Ethiopia?

Understanding the suitability of Satellite Rainfall Estimates (SREs) in simulating high flows and Actual Evapotranspiration (AET) is crucial for developing flood monitoring systems. Therefore, this study aims to assess i) the suitability of SREs in simulating both high flows and AET for different levels of model complexity, and ii) the effect of streamflow calibration on simulating AET for different rainfall inputs in Melkakunitre catchment, Upper Awash Basin, Ethiopia. Three state-of-the-art SREs (TRMM 3B42v7, IMERG v06B, and TAMSAT v3) were used and their usefulness in simulating high flows (Q5), daily streamflow, and wet season flows (from June to September) was assessed using the HBV-light model for the period 2003-2015. The model was set up for two levels of complexity: with and without considering the effect of orography on rainfall and temperature. Moreover, the water balance derived AET was compared against three remotely sensed AET products, MOD 16A2, GLEAM v3, and SSEBob, so as to examine the effect of streamflow calibration on AET simulation. Results show that rainfall inputs and model complexity have a strong impact on simulating streamflow and AET. For all rainfall forcing datasets, the performance of the hydrological model improves when we consider the effects of orography on rainfall and temperature. The IMERG v06B and TAMSAT v3 products showed the highest and least performances in simulating all the three flow conditions, respectively. Moreover, the MODIS-AET is the best remotely sensed AET product in reproducing the water balance-derived AET for all rainfall inputs except TAMSAT v3. The HBV-light model parameters calibrated with streamflow provided better results for simulating AET as well. On average, the usefulness of the IMERG v06B product for simulating high flows and AET is outstanding and can be thus used for developing flood monitoring and management systems in the study catchment.

Crop production response to soil moisture and groundwater depletion in the Nile Basin based on multi-source data.

Soil moisture (SM) and groundwater (GW) depletion triggered by anthropogenic and natural climate change are influencing food security via crop production per capita decrease in the Nile River Basin (NRB). However, to the best of our understanding, the causes and impact of SM and GW depletion have not been studied yet comprehensively in the NRB. In this study, GW is derived from the Gravity Recovery and Climate Experiment (GRACE) mission, and SM was estimated using the Triple Collocation Analysis (TCA). SM/GW depletion causes were evaluated via the Land Use Land Cover (LULC) and rainfall/temperature change analysis, whereas impact analysis focused on crop production per capita reduction (food insecurity) during SM depletion. The major findings of this study are 1) TCA analyzed SM show a decreasing trend (-0.06 mm/yr) in agricultural land while increasing (+0.21 mm/yr) in forest land, 2) LULC analysis indicated a vast increment of agricultural land (+9%) and bareland (+9%) although the decreasing pattern of forest (-1.5%) and shrubland (-6.9%) during 1990-2019; 3) the impact of SM depletion on crop production per capita caused food insecurity during a drought year, 4) agriculture drought indices and crop production per capita show high correlations (R2 = 0.86 to 0.60) demonstrated that Vegetation Supply Water Index (VSWI) could provide strategic warning of drought impacts on rainfed agricultural regions. In conclusion, SM and GW depletions are mainly caused by human-induced and climate change factors imposing food insecurity challenges in the NRB coupled with increasing temperature and excessive water extraction for irrigation. Therefore, it is highly recommended to rethink and reverse SM/GW depletion causing factors to sustain food security in NRB and similar basins.

Hydrological drought impacts on water storage variations: a focus on the role of vegetation changes in the East Africa region. A systematic review.

Drought is one of the most challenging climatic events. Recently, the drought influence in East Africa (EA) total water storage (TWS) is a serious problem, particularly in arid areas with modified natural vegetation relying on water deficit, garnered extensive research interest. Hydro-climatological and vegetation indices and remote sensing datasets derived from Gravity Recovery Climate Experiment (GRACE) mission datasets reveal good performance in analyzing hydrological drought influences in water storage. Over the last decades, studies were considered successful in monitoring the drought influence in the region TWS potential. However, several challenges remained unsolved, hindering the hydrological drought mitigation strategies. This review deals with an overview of drought impact monitoring targeted at the TWS variation with the response of vegetation change for sustainable drought mitigation. To improve the flexibility and adaptive capacities of the water deficit problem, we aim to provide an overview of drought impacts on TWS in the region to redefine the hydro-climatological and vegetation drought indices and improve the understanding of drought impact through remote sensing datasets. This review presents the challenges and prospects and offers a conclusion. Although, we hope that the review can facilitate further study regarding future hydrological drought projection in the development of several scientific research in the field.

Long-Term Assessment of Surface Water Quality in a Highly Managed Estuary Basin.

Anthropogenic developments in coastal watersheds cause significant ecological changes to estuaries. Since estuaries respond to inputs on relatively long time scales, robust analyses of long-term data should be employed to account for seasonality, internal cycling, and climatological cycles. This study characterizes the water quality of a highly managed coastal basin, the St. Lucie Estuary Basin, FL, USA, from 1999 to 2019 to detect spatiotemporal differences in the estuary's water quality and its tributaries. The estuary is artificially connected to Lake Okeechobee, so it receives fresh water from an external basin. Monthly water samples collected from November 1999 to October 2019 were assessed using principal component analysis, correlation analysis, and the Seasonal Kendall trend test. Nitrogen, phosphorus, color, total suspended solids, and turbidity concentrations varied seasonally and spatially. Inflows from Lake Okeechobee were characterized by high turbidity, while higher phosphorus concentrations characterized inflows from tributaries within the basin. Differences among tributaries within the basin may be attributed to flow regimes (e.g., significant releases vs. steady flow) and land use (e.g., pasture vs. row crops). Decreasing trends for orthophosphate, total phosphorus, and color and increasing trends for dissolved oxygen were found over the long term. Decreases in nutrient concentrations over time could be due to local mitigation efforts. Understanding the differences in water quality between the tributaries of the St. Lucie Estuary is essential for the overall water quality management of the estuary.

Simulated wetland conservation-restoration effects on water quantity and quality at watershed scale.

Wetlands are one of the most important watershed microtopographic features that affect hydrologic processes (e.g., routing) and the fate and transport of constituents (e.g., sediment and nutrients). Efforts to conserve existing wetlands and/or to restore lost wetlands require that watershed-level effects of wetlands on water quantity and water quality be quantified. Because monitoring approaches are usually cost or logistics prohibitive at watershed scale, distributed watershed models such as the Soil and Water Assessment Tool (SWAT), enhanced by the hydrologic equivalent wetland (HEW) concept developed by Wang [Wang, X., Yang, W., Melesse, A.M., 2008. Using hydrologic equivalent wetland concept within SWAT to estimate streamflow in watersheds with numerous wetlands. Trans. ASABE 51 (1), 55-72.], can be a best resort. However, there is a serious lack of information about simulated effects using this kind of integrated modeling approach. The objective of this study was to use the HEW concept in SWAT to assess effects of wetland restoration within the Broughton's Creek watershed located in southwestern Manitoba, and of wetland conservation within the upper portion of the Otter Tail River watershed located in northwestern Minnesota. The results indicated that the HEW concept allows the nonlinear functional relations between watershed processes and wetland characteristics (e.g., size and morphology) to be accurately represented in the models. The loss of the first 10-20% of the wetlands in the Minnesota study area would drastically increase the peak discharge and loadings of sediment, total phosphorus (TP), and total nitrogen (TN). On the other hand, the justifiable reductions of the peak discharge and loadings of sediment, TP, and TN in the Manitoba study area may require that 50-80% of the lost wetlands be restored. Further, the comparison between the predicted restoration and conservation effects revealed that wetland conservation seems to deserve a higher priority while both wetland conservation and restoration may be equally important.

Development of multi-model ensemble approach for enhanced assessment of impacts of climate change on climate extremes.

The severity and frequency of climate extremes will change in the future owing to global warming. This can severely impact the natural environment. Therefore, it is common practice to project climate extremes with a global climate model (GCM) in order to quantify and manage the associated risks. Several studies have demonstrated that a multi-model ensemble approach increases the reliability of predictions by exploiting the strengths and discounting the weaknesses of each climate simulator. However, the available multi-model averaging approaches exhibit significant drawbacks as they are not capable of extracting different climate extreme characteristics from the climate models. This study proposes a new approach that combines multiple models for projecting climate extremes by accounting for different extreme indices in the climate model performance weighting scheme. The capability of this method was evaluated with respect to reliability ensemble averaging (REA) and Taylor diagram-based GCM skill approaches for reproducing wet and dry precipitation events. The proposed multi-model averaging approach outperformed the available approaches in reducing the root mean square error (RMSE) by 5% and 54% in the wet and dry precipitation conditions, respectively. Therefore, it can be concluded that incorporating the different precipitation extremes in a multi-model combination approach could enhance the assessment of climate change impacts on the climate extremes. The climate change impacts on the extreme events, based on the proposed multi-model ensembles, is thus assessed using the standardized precipitation indexes of 3 month, 6 month, and 12 month durations. In general, the results exhibited that the frequency of wet events increases, whereas that of drought events decreases.

Evaporation estimation of rift valley lakes: comparison of models.

Evapotranspiration (ET) accounts for a substantial amount of the water flux in the arid and semi-arid regions of the World. Accurate estimation of ET has been a challenge for hydrologists, mainly because of the spatiotemporal variability of the environmental and physical parameters governing the latent heat flux. In addition, most available ET models depend on intensive meteorological information for ET estimation. Such data are not available at the desired spatial and temporal scales in less developed and remote parts of the world. This limitation has necessitated the development of simple models that are less data intensive and provide ET estimates with acceptable level of accuracy. Remote sensing approach can also be applied to large areas where meteorological data are not available and field scale data collection is costly, time consuming and difficult. In areas like the Rift Valley regions of Ethiopia, the applicability of the Simple Method (Abtew Method) of lake evaporation estimation and surface energy balance approach using remote sensing was studied. The Simple Method and a remote sensing-based lake evaporation estimates were compared to the Penman, Energy balance, Pan, Radiation and Complementary Relationship Lake Evaporation (CRLE) methods applied in the region. Results indicate a good correspondence of the models outputs to that of the above methods. Comparison of the 1986 and 2000 monthly lake ET from the Landsat images to the Simple and Penman Methods show that the remote sensing and surface energy balance approach is promising for large scale applications to understand the spatial variation of the latent heat flux.

Rainfall trend and variability in Southeast Florida: Implications for freshwater availability in the Everglades.

Freshwater demand in Southeast Florida is predicted to increase over the next few decades. However, shifting patterns in the intensity and frequency of drought create considerable pressure on local freshwater availability. Well-established water resources management requires evaluating and understanding long-term rainfall patterns, drought intensity and cycle, and related rainfall deficit. In this study, the presence of rainfall monotonic trends was analyzed using linear regression and Mann-Kendal trend tests. Pettit's single point detection test examined the presence of an abrupt change of rainfall. Drought in Southeast Florida is assessed using the Standardized Precipitation Index (SPI) in 3-, 6-, 12-, and 24-months scale; and the Fast Fourier Transform is applied to evaluate the frequency of each drought intensity. There was an increase of rainfall in most of the wet season months, the total wet season, and the annual total. The wet season duration showed a decrease driven by a decrease in October rainfall. Since 1990, wet season and total annual rainfall exhibited an abrupt increase. The SPI analysis has indicated that extended wetness characterizes the contemporary rainfall regime since 1995, except for the incidence of intermittent dry spells. Short-term droughts have 3-year to 5-year recurrence intervals, and sustained droughts have a 10-year and 20-year recurrence intervals. In Southeast Florida, prolonged drought limits freshwater availability by decreasing recharge, resulting in a longer hydro-period to maintain the health of the Everglades Ecosystem, and to control saltwater intrusion. The increasing dry season duration suggests the growing importance of promoting surface water storage and demand-side management practices.