Dynamic factor modeling of ground and surface water levels in an agricultural area adjacent to Everglades National Park

The extensive eastern boundary of Everglades National Park (ENP) in south Florida (USA) is subject to one the most expensive and ambitious environmental restoration projects in history. Understanding and predicting the interaction between the shallow aquifer and surface water is a key component for fine-tuning the process. The Frog Pond is an intensively instrumented agricultural 2023 ha area adjacent to ENP. The interactions among 2 1 multivariate daily time series (ground and surface water elevations, rainfall and evapotranspiration) available from this area were studied by means of dynamic factor analysis, a novel technique in the field of hydrology. This method is designed to determine latent or background effects governing variability or fluctuations in non-stationary time series. Water levels in 16 wells and two drainage ditch locations inside the area were selected as response variables, and canal levels and net recharge as explanatory variables. Elevations in the two canals delimiting the Frog Pond area were found to be the main factors explaining the response variables. This influence of canal elevations on water levels inside the area was complementary and inversely related to the distance between the observation point and each canal. Rainfall events do not affect daily water levels significantly but are responsible for instantaneous or localized groundwater responses that in some cases can be directly associated with the risk of flooding. This close coupling between surface and groundwater levels, that corroborates that found by other authors using different methods. could hinder on-going environmental restoration efforts in the area by bypassing the function of wetlands and other surface features. An empirical model with a reduced set of parameters was successfully developed and validated in the area by interpolating the results from the dynamic factor analysis across the spatial domain (coefficient of efficiency across the domain: 0.66-0.99). Although specific to the area, the resulting model is deemed useful for water management within the wide range of conditions similar to those present during the experimental period. (c) 2005 Elsevier B.V. All rights reserved.