Progress and Future Direction in Topographic Modeling for ATLSS Models.
Scott M. Duke-Sylvester
Louis J. Gross
University of Tennessee, Knoxville, TN
Topographic variation in South Florida, though generally small, is very important
for the flora and fauna of the region. Recently the US Geological Survey (USGS)
started the High Accuracy Elevation Data (HAED) collection project with the
goal of obtaining an estimate of topography in South Florida at a high spatial
resolution (nominally 400x400 meters) with high vertical accuracy (vertical
estimates are accurate to within about 3 cm). The project has now collected,
processed and made available topographic data for a large portion of the natural
areas in South Florida, including most of the Everglades National Park (ENP),
portions of Big Cypress National Preserve (BCNP) and Water Conservation Area
3.
Prior to the existence of the HAED project there were no topographic maps of
South Florida with both the spatial resolution and spatial extent needed to model
the ecology of fauna and flora in South Florida. To fill this void, the Across
Trophic Level System Simulation (ATLSS) project developed the High
Resolution Topography (HRT) model. This model estimates elevation for 30x30
meter grid cells across the natural regions of South Florida. The estimation is
based on the types of vegetation and the patterns of hydrology at each location.
The founding assumption of the HRT model is that plants tend to be in places
where local topography and hydrology combine to create a suitable habitat.
Knowledge of the vegetation and hydrology at a location can be used to estimate
elevation by choosing an elevation that results in hydrologic conditions suitable
for local vegetation.
Three data sets form the basic inputs into the HRT model: a map of the
distribution of vegetation in South Florida, a map of the history of hydroperiod
distributions, and a table of hydroperiod preferences for each of the vegetation
types in the vegetation map. The hydroperiod preferences are derived from a
review of available literature. The vegetation distribution map used is the Florida
GAP (FGAP) map (version 2.1) created by Leonard Pearlstine at the University of
Florida. The FGAP map assigns one of 43 vegetation types to each 30x30 meter
cell over most of South Florida.
The hydroperiod data for the HRT model is created from the
Calibration/Verification run of the South Florida Water Management Model
(SFWMM). The SFWMM is managed by the South Florida Water Management
District (SFWMD) and is the standard hydrologic model for South Florida. The
Calibration/Verification run of this model is considered to be the one that most
accurately reflects the historical pattern of hydrology in South Florida from 1979
to 1995. The SFWMM partitions South Florida into a grid, where each grid cell is
2x2 miles. The model estimates water depth in each 2x2 mile cell on a daily time
step from January 1, 1979 to December 31, 1995.
Over the past two years there have been a number of advances in the ATLSS
High Resolution Topography (HRT) modeling project. An extensive literature
review, Plant Community Parameter Estimates and Documentation for the Across
Trophic Level System Simulation (ATLSS) by Paul Wetzel has been completed
and peer-reviewed. This document describes the hydroperiod preferences of the
natural vegetation types used in the 6.1 version of the Florida GAP (FGAP) map.
The second major advance is the development of a new HRT map. This new
version is based on FGAP 6.1, hydrology data from the South Florida Water
Management Model (SFWMM) version 3.7 and the hydroperiod estimates that
appear in Wetzel’s report. We are currently waiting for a more current version of
the SFWMM Calibration/Verification output before completing a final version of
the new HRT map.
Finally, we have begun an analysis that looks at the relationship between HAED
elevations, distribution of vegetation as provided in the FGAP map and
hydrologic parameters as predicted by the SFWMM. This analysis uses a multiple
regression model with vegetation type and a number of yearly average hydrologic
variables as predictors and elevation estimates from the HAED collection project
as the response variable. There are two major purposes in performing this
analysis. The first is to test an assumption made by the HRT model that there is a
relationship between the elevation of a location and the vegetation associated with
that location by FGAP. The second is to determine if a multiple regression model
can be used to predict HAED elevation based on vegetation and hydrology. Both
of these results provide a basis for evaluating the output of the HRT model.
This analysis has been completed for the Big Boy Lake HAED sampling unit. The
regression coefficients for several of the vegetation types in this region are
significantly non-zero. This indicates that vegetation provides some information
about variation in topography. However, the r2 and PRESS values for the
regression model are not sensitive to the presence of vegetation as a descriptor
variable. This indicates that in the Big Boy Lake region the overall contribution of
vegetation to explaining variation in elevation is small. The conclusion we draw
from this result is that for the Big Boy Lake HAED region we do not expect the
HRT to perform well when compared to the HAED data since the inputs to the
HRT model have very little relationship to the HAED elevation data. The limited
scope of this analysis leaves a number of unanswered questions. The relationship
between vegetation and HAED elevation in other regions and at other spatial
scales has not yet been explored. While working with the HAED data we have
noticed that elevations were not sampled in some tree islands. According to Greg
Desmond, HAED project leader at USGS, the methods used to collect elevation
data for HAED result in the systematic omission of tree islands. The effect of the
sampling bias that arises from this omission is to compress the variance in
measured elevations relative to the actual variance.
The HAED data will become the primary basis for topography used by ATLSS
models. However, the availability of the HAED topography does not completely
eliminate the need for the HRT model. In the short term there is still a need to use
HRT estimates of topography for regions not yet covered by the HAED project. In
the long term, the methodologies used in the HRT model may be needed to
augment the topography generated by the HAED. We propose using the HRT
map and the HRT methodologies to augment the HAED topography in areas
where HAED has omitted vegetation structures, or has compressed variation.
Work has begun on identifying regions where the HRT model can be useful for
augmenting the HAED topography and the methods needed to incorporate HRT
output into a HAED-based topography.
Duke-Sylvester, Scott M., University of Tennessee, 569 Dabney Hall, Knoxville,
TN, 37996-1610
Phone: 865-974-0223, Fax: 865-974-3067, sylv@tiem.utk.edu