Evaluation and Calibration of ATLSS SESI Models
Louis Gross, Jane Comiskey, Mark Palmer
University of Tennessee, Knoxville, TN
Donald DeAngelis
USGS, Center for Water and Restoration Studies, University of Miami, Coral
Gables, FL
A primary product of the USGS's Across Trophic Level System Simulation
(ATLSS) Program is the set of Spatially Explicit Species Index (SESI) models
developed for the Greater Everglades area. These models (eight as of 2002)
produce values for habitat suitability ranging from 0.0 to 1.0 for all 111,000 cells
of the 500 x 500 m array. These habitat suitability values are typically calculated
for every individual year in a 31-year sequence, simulated using inputs from the
South Florida Water Management Model (SFWMM) as processed by the ATLSS
High Resolution Hydrology Model. Averages can also be computed over any set
of years (e.g., wet years, dry years, all 31 years), and over a variety of sub-regions
within the total region included. The SESI models are used for relative
comparisons between alternative future scenarios, not for producing absolute
evaluations of habitat quality.
The current versions of ATLSS SESI models were evaluated and calibrated with
historical demographic observations to the maximum extent possible, given data
availability and time constraints. Abundance data for many Everglades species
are scarce/sporadic, and methods of collection and reporting are often inconsistent
over time and space. The degree to which SESI model evaluation was possible
has also been limited by delays in availability of calibration water data for the
model for recent years, when more complete and consistent species abundance
data have been collected. The lack of high-spatial-resolution water data continues
to limit evaluation efforts.
As an example of using the best available data in SESI calibration, spatial
abundance data were extracted for each group of wading birds from Systematic
Reconnaissance Flight (SRF) records as they became available. The 3-level
estimates of water depth recorded along with SRF observations (dry, transitional,
wet) were used to approximate historical water depths. Abundance counts were
summed and demographic trends were graphed over each sub-region. These trend
graphs were compared to SESI output, graphed over model sub-regions, for yearto-year trends, and trends in wet, average, and dry periods. These evaluations
were all made based upon the SFWMM Calibration/Verification data available
through 1995.
There was no opportunity during the rapid cycle of model development to
formalize and document these efforts to compare SESI model output to empirical
observations and adjust model parameters to reflect historical biological responses
to hydrologic parameters such as hydroperiod and water depth. This process is
being formalized and improved as additional monitoring data become available
and as restoration modifications proceed. The SESI model code is in an objectoriented structure that readily allows for model modification as data collection
proceeds.
As with the SESI models, availability of new data has required testing and
revisions of the SFWMD Hydrology Model which produces hydrology data sets
used to drive ATLSS scenario evaluations. An expanded set of hydrologic
calibration data, providing daily water depths over the model area during the
period 1979 to 2000, will become available from the SFWMD. Generation of this
data set has required hydrology model modifications and revisions that will result
in output that differs from the original calibration hydrology data. Together with
recent data documenting species abundances over the model area, this extended
and modified set of hydrologic inputs will enable further testing, evaluation, and
revision of SESI models. This activity is essential in order to increase the
reliability of the relative predictions made by SESI models.
Some approaches to be used in current and further SESI model development,
evaluation, and refinement include the following activities.
(1) Extensive model runs have already been performed to evaluate the sensitivity
of the models to wet, dry and typical hydrological patterns as represented in both
the F2050 base and the AltD13R scenario. These model runs were accomplished
by creating new water data sets by extracting wet, dry and typical years of water
data from the existing water files and recombining them to create multiple water
files representing scenarios which were wetter, drier and more average for each of
the F2050 and the AltD13R scenarios than the original F2050 and AltD13R
scenarios. The model output for these resultant water files was then summarized
for both the entire model region and selected sub-regions and compared to the
summarized output for the original water file. Analysis of these results has
indicated that the models do indicate the appropriate response to water level
differences (index values increase on average across the region for the American
Alligator under wetter conditions). These results also indicate a fairly consistent
relative pattern of differences for the models (e.g., if the average index value from
a model for AltD13R is higher than its value for F2050, this ranking usually holds
true for scenarios comprised of wet, dry, and typical water years). An expansion
of this methodology is planned to evaluate the sensitivity of scenario rankings to
variation of model parameters.
(2) A web-based interface to the SESI models as an extension of the ATLSS Data
Viewer will be made available that allows authorized users to make modifications
to selected model parameters and execute models on an ATLSS computational
server. The resultant output file(s) could then be downloaded to the user's
computer and the ATLSS Data Viewer used to compare different model runs and
compare abundance data. The ATLSS Data Viewer tool would be invaluable in
comparing model output with empirical data, as this tool incorporates many of the
spatial summary routines required for such analysis.
The original restoration science concept for South Florida included continuing
feedback between modeling, monitoring, and management programs. This must
incorporate open lines of communication among modelers, biologists, and
managers. Long-range stable funding must be secured for model evaluation,
updating, and analysis as new monitoring and calibration data become available.
Planned, periodic version updates of SESI models should incorporate information
from trends reflected in current monitoring data.
In order for modeling to play an effective role in the restoration process, the
following tasks necessary for model development, updating, and effective use
over the period of adaptive restoration management in South Florida should be
explicitly planned and funded:
(1) Accumulation and timely distribution of monitoring data.
(2) Maintenance of links and dialogues with experts, ensuring that models
continue to reflect what is known about modeled species as the knowledge
base grows.
(3) Liaison with users of model output, explaining caveats and restrictions, what
output represents and how it should be interpreted.
Louis J. Gross, University of Tennessee, 569 Dabney Hall, Knoxville, TN, 379961610
Phone: 865-974-4295, Fax: 865-974-3067, gross@tiem.utk.edu