5 Poster Session

Tuesday, 8 June 2010
8:00 AM - 8:00 PM
Private Dining Room / Atrium

REVISION AND ASSESSMENT OF WATER-SURFACE MODELING OF THE EVERGLADES DEPTH ESTIMATION NETWORK (EDEN)

Paper number: 5-26
Program: General Sessions

Authors

Abstract

Hydrologic regime is a critical limiting factor in the delicate ecosystem in the greater Everglades freshwater wetland in southeastern Florida, and “getting the water right” is regarded as critical to the successful restoration of this unique ecosystem. The products of the Everglades Depth Estimation Network (EDEN) quantify spatial-temporal hydrologic patterns at the landscape scale, such as water depth, hydroperiods, and days since dry events. One critical EDEN component is the daily water-surface model, interpolated from water-level data collected at a network of real-time water-level gaging stations maintained by multiple agencies. The hourly water-level readings are assembled, quality assured, and missing data are filled in with estimates. The daily medians are computed and input into the water-surface model to create daily water surfaces on a grid comprised of 400 meter (m) by 400m grid cells. The model utilizes a radial basis function (RBF) algorithm and a combination of real and pseudo canal gages to model the discontinuities of water level across sub-region boundaries of the Everglades.

The water-surface model, as well as other EDEN products, have been well received by scientists and resource mangers involved in Everglades restoration. Improvements of the model have been ongoing to accommodate the needs raised by users and to incorporate new data and improved understanding of the hydrologic dynamics in the Everglades. This presentation documents the new model developments, including the addition of new gages, datum correction for existing gages, canal water-level revisions, RBF reparameterization, model adjustment for dry conditions, and model assessment using independent water-level data collected at benchmarks of known elevation.

Acronyms

EDEN: Everglades Depth Estimation Network

RBF: Radial Basis Function