Estimating groundwater flows in karst-dominated landscapes is difficulted by the lack of observed groundwater flows and the uncertainty in hydrogeological parameters (hydraulic conductivity, specific storage and yield, porosity, anisotropy, etc.). The State of Florida (USA) landscape is dominated by the largest geographic area of geologically young carbonate and rocks with well-developed karst features in North America. In this research, a MODFLOW groundwater model for central Biscayne Bay is presented. The Bay is surrounded by karst terrain. The groundwater model is loosely linked to a hydrodynamic model to estimate salinity concentrations at the Biscayne Bay’s central coast. The loose-link model framework is shown to improve salinity estimations in 50% from estimations that do not include groundwater contributions to the Bay. A statistical comparison of simulated concentrations versus observed data demonstrates that the loose-link model simulates salinity concentration values and trends within acceptable margin of errors (0.72 < R < 0.77; 0.52 < R2 < 0.59; 0.62 < K-G < 0.70; d > 0.76; 4.1% < PBIAS < 13.5%). These results have the potential to be applied to other coastal locations in Florida and the world where karst terrain is predominant. 

Estimation of Groundwater and Salinity for the Central Biscayne Bay Coast, Florida, USA

Prediction of salinity intrusion in the east Upputeru estuary of India using hybrid metaheuristic algorithms

Estimating salinity concentrations in coastal waters allows characterization of the spatial and temporal dynamics of the freshwater/saltwater interface. In Southeast Florida (USA) the saltwater interface is monitored and evaluated for potential impacts to public supply wellfields and biological communities. In this research, a closed-loop autoregressive neural network with exogenous inputs was developed to estimate salinity concentrations at a coastal water quality station (BISCC4) in Biscayne Bay, Florida. The neural network (ANN) is shown to successfully simulate hourly salinity concentrations for years 2015 through 2019. A statistical comparison of simulated concentrations versus observed data demonstrates that the ANN simulates salinity concentration values and trends within acceptable margin of errors (R2 = 0.59, K-G = 0.64, NSE = 0.33, d = 0.86, PBIAS = 1.5%, RSR = 0.82). In its current form, the ANN model performs better in simulating salinity concentrations and trends, than an existing hydrodynamic model. These results have the potential to be applied to other coastal locations in Biscayne Bay where freshwater inputs from inland streams and canals are affecting salinity concentrations.

Paul F. Mickle

Papers

Estimation of Hourly Salinity Concentrations Using an Artificial Neural Network