|A comparison of simulated and field-derived Leaf Area Index (LAI) and canopy height values from four forest complexes in the southeastern USA
|Iiames, J.S., E. Cooter, D. Schwede and J. Williams
|URL (non-DOI journals):
|ALMANAC, EPIC & SWAT
|Broad Application Category:
|crop/plant/tree growth or production
|Primary Application Category:
|crop, forest and/or vegetation growth/yield and/or parameters
|Secondary Application Category:
|calibration, sensitivity, and/or uncertainty analysis
|Four sites in North Carolina and Virginia in the southeast U.S.
|This study was performed with the EPIC model. However, the authors state in Section 2.4 that the simulations performed in EPIC were heavily influenced by previous ALMANAC and ALMANACBF studies, and that an overall goal of the research was to help improve representation of agro-forests in SWAT.
|Vegetative leaf area is a critical input to models that simulate human and ecosystem
exposure to atmospheric pollutants. Leaf area index (LAI) can be measured in the field or numerically
simulated, but all contain some inherent uncertainty that is passed to the exposure assessments that
use them. LAI estimates for minimally managed or natural forest stands can be particularly difficult
to develop as a result of interspecies competition, age and spatial distribution. Satellite-based LAI
estimates hold promise for retrospective analyses, but we must continue to rely on numerical models
for alternative management analysis. Our objective for this study is to calculate and validate LAI
estimates generated from the USDA Environmental Policy Impact Climate (EPIC) model (a widely
used, field-scale, biogeochemical model) on four forest complexes spanning three physiographic
provinces in Virginia and North Carolina. Measurements of forest composition (species and number),
LAI, tree diameter, basal area, and canopy height were recorded at each site during the 2002
field season. Calibrated EPIC results show stand-level temporally resolved LAI estimates with
R2 values ranging from 0.69 to 0.96, and stand maximum height estimates within 20% of observation.
This relatively high level of performance is attributable to EPIC’s approach to the characterization
of forest stand biogeochemical budgets, stand history, interspecies competition and species-specific
response to local weather conditions. We close by illustrating the extension of this site-level approach
to scales that could support regional air quality model simulations.
|LAI; leaf area index; EPIC; simulation; satellite; MODIS; biomass; evaluation;
southern U.S. forests