Linear Regression Salinity Models for Everglades National Park

Empirical Tools for Simulating Salinity for the Estuaries in Everglades National Park, Florida

F. E. Marshall, D. T. Smith, D. M. Nickerson

Estuarine, Coastal and Shelf Science doi: 10.1016/j.ecss.2011.10.001

 · 37 peer-reviewed, daily resolution time-series salinity models for Everglades National Park estuaries

· Domain: Florida Bay, southwest Gulf coast (mangrove rivers), Barnes Sound

· Independent variables (forcing factors): upstream water level, wind, tide

· Models explain 65-80% of daily variability with an accuracy of 2-4 salinity units

· Used by RECOVER Southern Coastal Systems Sub-team for CERP restoration targets


Salinity in a shallow estuary is affected by upland freshwater inputs (surface runoff, stream/canal flows, groundwater), atmospheric processes (precipitation, evaporation), marine connectivity, and wind patterns.  In Everglades National Park (ENP) in South Florida, the unique Everglades ecosystem exists as an interconnected system of fresh, brackish, and salt water marshes, mangroves, and open water.  For this effort a coastal aquifer conceptual model of the Everglades hydrologic system was used with traditional correlation and regression hydrologic techniques to create a series of multiple linear regression (MLR) salinity models from observed hydrologic, marine, and weather data.  The 37 ENP MLR salinity models cover all of the estuarine areas of ENP and produce daily salinity simulations that are capable of estimating 65-80% of the daily variability in salinity depending upon the model.  The Root Mean Squared Error is typically about 2-4 salinity units, and there is little bias in the predictions.  However, the absolute error of a model prediction in the nearshore embayments and the mangrove zone of Florida Bay may be relatively large for a particular daily simulation during the seasonal transitions.  Comparisons show that the models group regionally by similar independent variables and salinity regimes. The MLR salinity models have approximately the same expected range of simulation accuracy and error as higher spatial resolution salinity models.