MODELING FOREST PRODUCTIVITY ACROSS LARGE AREAS AND LONG PERIODS

. There are three classes of forest model used to simulate forest productivity across large areas and over long periods: growth and yield models, based on statistical relationships derived from measurements on trees; the so‐called gap models, concerned with species succession and dynamics, and carbon balance or biomass models. The characteristics of each type are discussed and illustrated by reference to some of the more important of the models in current use. The emphasis in this paper is on the carbon balance models, particularly on a new model (3‐PG), developed in a deliberate attempt to bridge the gap between growth and yield and carbon balance models, and the companion model (3‐PGS) derived from 3‐PG to utilize satellite data as inputs to constrain the simulation calculations and improve estimates of growth over time. 3‐PG/3‐PGS run on monthly time steps, driven by weather data, and avoid the problems of over‐parameterization and the requirements for a great deal of input data that limit the practical value of most carbon balance models. We present test results from 3‐PG against experimental data, and against forest plot (mensuration) data from large areas; also test results from 3‐PGS against estimates of average forest growth over large areas, and in plantations with different planting times, using AVHRR and Landsat MSS data to constrain the model outputs. The paper discusses the problems of the variability of natural forests and the difficulties this causes in validating models intended for use over large areas. The value of remote sensing as means of overcoming this problem is considered.