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Zhongwei Liu, Ph.D. 1* Aaron Higer 1 Frank Mazzotti, Ph.D. 1 Leonard Pearlstine, Ph.D. 2

Validation of Spatially Continuous EDEN Water-Surface Model for the Everglades, Florida with Ecosystem Applications. Zhongwei Liu, Ph.D. 1* Aaron Higer 1 Frank Mazzotti, Ph.D. 1 Leonard Pearlstine, Ph.D. 2 1. Ft. Lauderdale Research & Education Center, University of Florida

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Zhongwei Liu, Ph.D. 1* Aaron Higer 1 Frank Mazzotti, Ph.D. 1 Leonard Pearlstine, Ph.D. 2

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  1. Validation of Spatially Continuous EDEN Water-Surface Model for the Everglades, Florida withEcosystem Applications Zhongwei Liu, Ph.D.1* Aaron Higer1 Frank Mazzotti, Ph.D.1 Leonard Pearlstine, Ph.D.2 1. Ft. Lauderdale Research & Education Center, University of Florida 2. Everglades and Dry Tortugas National Parks * Liuz@ufl.edu AAG annual conference Boston, MA, 4/2008

  2. Outline • Introduction • EDEN network • EDEN water-surface model • Methodology • Study area • Data collection • Analysis methods • Results • Ecosystem Applications • Conclusions and Discussions

  3. tree islands wet prairie sawgrass marsh wet prairie slough alligator holes I. Introduction • Efforts to measure and link surface-water depths to biotic communities in the Everglades(Loveless, 1959; Craighead, 1971; McPherson, 1973; Cohen, 1984; Newman et al., 1996; Busch et al., 1998; Ross et al., 2000; Hendrix and Loftus, 2000; Gawlik, 2002; Chick et al., 2004; Palmer and Mazzotti, 2004; Trexler et al., 2005). • Hydrologic models could provide spatially continuous hydrologic information. • Two research objectives: model validation and applications alligator holes Source: www.broward.edu.

  4. Everglades Depth Estimation Network (EDEN) • Integrated network of real-time water level monitoring, ground elevation modeling, and water-surface modeling • Daily water level/stage data (2000 - present) from 253 gage stations • Two types of gage stations: marsh stations and canal stations a) satellite gage A marsh gage station

  5. EDEN Water-Surface Model • Developed by Pearlstine et al. (2007) • Spatial interpolation of 240 gage stations in ArcGIS: radial basis function (RBF) • Model outputs • Water surface of 400m x 400 m grid spacing, to match the EDEN ground digital elevation model (DEM) • Water depth = water surface - DEM

  6. II. Methodology • Study area • WCAs 3A South and 3B • Florida Department of Environmental Protection (FDEP) benchmark network

  7. Data Collection • Field water-level data collected by Florida Atlantic University (FAU) • At 24 benchmarks • Apr. - Sept., 2007 • Via airboat and helicopter

  8. Analysis Methods • GIS • Spatial analysis • Error statistics • MAE (Mean Absolute Error)= • MBE (Mean Biased Error)= • RMSE (Root Mean Squared Error)= • Nonparametric statistical methods • Spearman’s rank correlation • Wilcoxon signed rank test • Kruskal-Wallis nonparametric analysis of variance (ANOVA)

  9. III. Results • DIFF_STAGE = predicted water stage – observed water stage. • Underestimates and overestimates are represented by negative and positive values, respectively.

  10. Results (cont.) • Major statistics of interpolation errors for water stage

  11. Spearman’s rank correlation analysis • Spearman’s rank correlation analysis for temporally detrended water stage data (Rangel et al., 2006)

  12. Wilcoxon’s signed rank tests • Kruskal-Wallis nonparametric ANOVA

  13. IV. EDEN Water-Surface Model Applications • Estimation of Ground Elevation: Ground elevation = predicted water stage - observed water depth.

  14. Estimation of Water-Depth Hydrographs: Water depth = predicted water stage - derived ground elevation.

  15. V. Conclusions and Discussions • We found there are no statistically significant differences between model-predicted and field-observed water-stage data (p-value = 0.5129). Overall, the model is reliable by a RMSE of 3.3 cm. By region, the RMSE is 2.48 cm and 7.76 cm in WCAs 3A and 3B, respectively. • Two applications of the validated EDEN water-surface model to investigate the relationship between water depth and fresh water marsh habitats.

  16. Discussions • Boundary problems • When a benchmark reports high interpolation errors, it is likely to be near boundaries. • Data collection • Missing gage data • Localized impacts on the water surface • The method of estimating water depth • More accurate • More cost-effective

  17. Further Studies • More field observations of dry and wet seasons, and in other areas • Examine WCA 3B to improve the model • Rainfall data • A better regional ground DEM • Other interpolation techniques

  18. Acknowledgements • Dr. John Volin from University of Connecticut (formerly FAU) and Dr. Dianne Owen and Jenny Allen from FAU • Pamela A. Telis (USGS/USACE Liaison), Dr. John Jones, Paul Conrads, Heather Henkel, and Michael Holmes from USGS, and Roy Sonenshein from ENP • Elmar Kurzbach, U.S. Army Corps of Engineers (USACE), and Dr. Ronnie Best, USGS Priority Ecosystem Science • Dr. Laura Brandt, Kevin Chartier, Adam Daugherty, and Wingrove Duverney, Joint Ecosystems Modeling (JEM) • Others…

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