Estimating physical soil parameters for sample plots of large-scale forestinventories

The use of data from large-scale forest inventories for the development and application of explanatory ecosystem models is often hampered by a lack of quantitative site data. To circumvent such limitations, we describe a meth od to estimate soil water-holding capacity (SWC), a key parameter in many e cosystem simulation models, for the sample plots of the Austrian Forest Inv entory (AFI). In accordance with the limited soil data which are customaril y recorded by forest inventories, a functional model for the calculation of SWC from soil depth, coarse fractions in two soil layers, water-holding ca pacity of the fine fraction and the organic carbon content of the mineral s oil is developed. Bayesian probability theory is employed to establish rela tionships between measured site attributes of the AFI and the soil paramete rs required for calculating SWC. More detailed site and soil data of 514 sa mple plots of the Austrian Forest Soil Survey (AFSS) which can be considere d a subsample of the AFI are used for model calibration. As explanatory var iables orientation, slope, elevation, topography, soil-type and humus-type are included in the predictive models for the estimation of the required so il parameters. Model performance in reproducing the calibration dataset was evaluated by means of intraclass correlation coefficients. A predictabilit y index from the field of information theory is used to evaluate the contri bution of the independent variables in explaining the variability (entropy) of the dependent variables. A preliminary model validation with a subset o f AFSS data indicated good agreement of observed and predicted values for t he individual soil parameters as well as for the composite parameter SWC. ( C) 1998 Elsevier Science B.V.