Introduction Conversion Factors Model Input >Model Development Results File Naming Data Future Plans & Acknowledgements References Cited Figures and Tables PDF

The development of DEMs for use in EDEN applications, as well as other hydrologic and ecologic modeling and adaptive management, has been an iterative process. Prior to the EDEN project a DEM for an area beyond those of EDEN modeling (i.e., including coastal regions influenced by tides) was produced using the ESRI ARCGIS topogrid algorithm¹ (figure 5). This algorithm relies on spline interpolation that is modified to produce a "hydrologically correct" DEM. While visually pleasing and sufficient for regional scale analysis, this model is not suitable for the sub-regional and finer-scale quantitative analyses envisioned for EDEN outputs. For example while the spline approach honors the individual HAED values (i.e., spline surface elevations are exactly those of the input points at the input point locations), topogrid can generate false peaks and pits along regions where drastic changes in elevations occur and channels are not supported by actual ground measurements. Figure 6 depicts a small area in which water depth estimates have been created using the DEM produced by topogrid. While the dendritic drainage pattern depicted may seem plausible, it is not supported by field measurements and suggests resolution in the data that do not exist. Also when applied to the entire HAED dataset at once, the spline process fails to adequately represent topographic breaks that occur along the boundaries of Water Conservation Areas where levees, canals, and service roads interrupt the natural gradients presumably present prior to development of the water control infrastructure.

To create a more realistic region-wide elevation model for EDEN purposes, the elevation data were segregated by Water Conservation Areas and National Park boundaries so that local trends could be isolated, sub-region specific interpolation models could be developed, and realistic breaks in elevation along sub-region boundaries could be imbedded in a final, region-wide DEM. For each EDEN sub-area (figure 7), several surfacing algorithms that are more conservative than topogrid (when interpolating between known elevations) were evaluated. Outputs from these different methods were evaluated through three approaches. First, for each sub-region 15 percent of the points were withheld from the model development for their respective area before numerous interpolation methods and parameters within interpolation methods were specified using the remaining 85 percent of HAED points. The withheld points were then used as a "check" of simulated elevation values by comparing generated surfaces against their values. Next all HAED were included in sub-area model development and cross-validation was applied. In this process the software iteratively compares modeled surfaces to those of the input points used to create the surface². Water depths are created by subtracting the generated DEM from water surfaces that were interpolated from EDEN gage data; these depths are compared against estimated depths gathered by various principal investigators (i.e., "PI data") during field campaigns. Based on these evaluations and consideration of the utility of other diagnostic surfaces that are created as by-products of various interpolation processes, a "best" model is selected for each sub-area. Finally selected sub-area models are combined to create an EDEN regional DEM. The steps used in surface modeling can be summarized as follows:

1) Subset the HAED by EDEN sub-areas.

2) Randomly extract 15 percent of the HAED points from the set of observations associated with each EDEN sub-area.

3) Create numerous models for each sub-area using different surface interpolation methods.

4) Compare elevations interpolated using each method against those of the data points withheld during model development.

5) Based on error analysis select the "best" method for each EDEN sub-area.

6) Given the best method selected use ALL available HAED points to generate numerous elevation models for each sub-area by varying within-method modeling parameters.

7) Create depth layers for specific wet and dry days by subtracting modeled ground elevation surfaces from water height surfaces interpolated from recorded EDEN gage data.

8) Compare modeled water depths against field-estimated water depths.

9) Select the best performing elevation model for each sub-area.

10) Combine the chosen sub-area models to create one single EDEN elevation model.

11) As new HAED and field measurements of water depth become available, return to Step 1.

Figure 5. A regional DEM created using the TOPOGRID algorithm. While visually appealing and useful for region-wide analysis, this DEM is not suitable for higher-resolution EDEN applications.

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Figure 6. Modeled water depth for a small area of Water Conservation Area 3 (indicated with a star symbol on figure 7) produced by subtracting the DEM produced using the TOPOGRID algorithm from the water surface elevation model created from EDEN water level data for May 27, 2004. This DEM suggests a channel network that cannot be validated with available data. More conservative results from other approaches are depicted in the figures 8 and 9. [larger image]

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Figure 7. A location map depicting the Greater Everglades region, EDEN DEM processing sub-regions and the location of method comparison referenced in figures 6, 8, and 9 (shown by star). [larger image]

¹ Use of product and trade names is for illustrative and informational purposes only and does not represent an endorsement by the U.S. Government.

² Cross-validation only applies to techniques that do not honor the actual elevation value at the observed point in creating the estimated surface.