Predicting leaf area index from scaling principles: Corroboration and consequences

Leaf area index (LAI) is a key biophysical variable in most process-based forest-ecosystem models. However, most such models require LAI as an input, typically obtained from empirical observations. We tested whether scaling principles based on trade-offs between single leaf and canopy properties could be effectively used to model LAI, thereby obviating the need for empirical observations. To do so, we used the process-oriented model, PnET, configured to estimate LAI from these same scaling principles. We derived biologically based LAI predictions (LAI_PnET) for the Harvard Forest (Massachusetts, USA) eddy covariance tower site, a predominately mixed deciduous hardwood forest, using PnET, and compared these with a locally observed phenology record and with LAI estimates from both local (ground-based) photosynthetically active radiation transmittance (LAI_TRANS) and normalized difference vegetation index satellite data (LAI_NDVI). We generated the LAI _PnET trajectory by running the PnET model with meteorological observations from the flux tower as model drivers. We derived LAI _TRANS from measurements of above- and below-canopy photosynthetically active radiation at the flux tower, and LAI_NDVI from observations from the Advanced Very High Resolution Radiometer (AVHRR) satellite-borne sensor of surface greenness for the 1 km₂ cell containing the flux tower. Over a 5-year period, LAI_PnET and LAI _TRANS values were comparable intra- and interannually, with maximum values differing by less than 0.1 to 0.2 LAI units (m₂ m ^-2). Values of LAI_NDVI were similar to LAI_PnET and LAI_TRANS in midsummer, but higher LAI values were predicted in the early and late portions of the growing season. In addition, we used the three alternative LAI trajectories in a modified version of the PnET model and compared the resulting outputs of gross primary production (GPP) with GPP estimates from the flux tower for 5 continuous years. The LAI_PnET and LAI_TRANS inputs resulted in a difference of less than 3% in mean annual GPP from 1995 to 1999, and these were within 7 and 9%, respectively, of the annual eddy flux-based estimates over the same time period. The results indicate that biologically based LAI scaling approaches can closely track temporal changes in a deciduous forest and have potential for spatial and temporal scaling of LAI.