A biophysical soil–site model for estimating potential productivity of forested landscapes

Abstract

A biophysical soil–site model is presented for predicting potential forest productivity, defined as cubic metres per hectare per year at culmination of mean annual increment (CMAI), for use in forest taxation by the state of Montana. The model combines soil, climate, and topographic data layers within a geographic information system; a climate model (MT-CLIM); and an ecosystem carbon–water balance model (Forest-BGC) to generate estimates of potential forest productivity for all forested sites in the state. Weather station data were used to define base climate regions within the state, and to build regional precipitation models. Hydrologic equilibrium theory was used to estimate maximum leaf area from climate and soil water availability. Forest-BGC was initialized with estimates of the stem, leaf, and root carbon pools (kg C/ha) at CMAI and the 1-year prediction of stem carbon increment (kg C•ha−1•year−1) was taken as the initial estimate of potential productivity. The gross stem carbon increment was then converted to wood increment, reduced for branch wood growth, and finally adjusted for mortality rates found in regional yield tables. Other model adjustments included stockability factors for low-elevation forest–grassland sites, and modification of a photosynthesis rate parameter for north aspects. Response surfaces show potential productivity to vary as expected with precipitation, soil water, and temperature. Despite rather gross resolution of model inputs, regressions of site index on model predictions showed R2 values and standard errors comparable with those found when fitting empirical soil–site equations in the region.