THE USE OF MODELS TO INTEGRATE INFORMATION AND UNDERSTANDING OF SOIL-C AT THE REGIONAL-SCALE

Regional analysis of ecosystem properties, including soil C, is a rapi dly developing area of research. Regional analyses are being used to q uantify existing soil C stocks, predict changes in soil C as a functio n of changing landuse patterns, and assess possible responses to clima te change. The tools necessary for such analyses are simulation models coupled with spatially-explicit databases of vegetation, soils, topog raphy, landuse and climate. A general framework for regional analyses which integrates models with site-specific and spatially-resolved data is described. Two classes of models are currently being used for anal yses at regional scales, ecosystem-level models, which were originally designed for local scale studies, and more aggregated 'macro-scale' m odels developed for continental and global scale applications. A consi deration in applying both classes of models is the need to minimize er rors associated with aggregating information to apply to coarser spati al and temporal scales. For model input data, aggregation bias is most severe for variables which enter into non-linear model functions, suc h as soil textural effects on organic matter decomposition and water b alance or the temperature response of decomposer organisms. Aggregatio n of model structure also needs to be considered, particularly for mac ro-scale models. For example, representations of litter and soil organ ic matter by only one or two pools may be suitable for representing eq uilibrium conditions but rates of change will tend to be overestimated for transient-state conditions using highly aggregated models. Geogra phic soils data, derived from field surveys, are a key component for r egional analyses. Issues of data quality and interpretation of soil su rvey data are discussed in the context of regional analyses of soil C. Areas for further development of data and modeling capabilities, incl uding refining soil C maps, developing spatial databases on landuse an d management practices, using remotely sensed data in regional model a pplications, and linking terrestrial ecosystem models with global clim ate models, are discussed. (C) 1997 Published by Elsevier Science B.V.