Paustian, K., E. Levine, W. M. Post, and I. M. Ryzhova. 1997. The use of models to integrate information and understanding of soil C at the regional scale. Geoderma 79:227-260.
Regional analysis of ecosystem properties, including soil C, is a rapidly developing area of research. Regional analyses are being used to quantify existing soil C stocks, predict changes in soil C as a function of changing landuse patterns, and assess possible responses to climate change. The tools necessary for such analyses are simulation models coupled with spatially-explicit databases of vegetation, soils, topography, 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 analyses at regional scales, ecosystem-level models, which were originally designed for local scale studies, and more aggregated ‘macro-scale’ models developed for continental and global scale applications. A consideration in applying both classes of models is the need to minimize errors associated with aggregating information to apply to coarser spatial and temporal scales. For model input data, aggregation bias is most severe for variables which enter into non-linear model functions, such as soil textural effects on organic matter decomposition and water balance or the temperature response of decomposer organisms. Aggregation of model structure also needs to be considered, particularly for macro-scale models. For example, representations of litter and soil organic matter by only one or two pools may be suitable for representing equilibrium conditions but rates of change will tend to be overestimated for transient-state conditions using highly aggregated models. Geographic soils data, derived from field surveys, are a key component for regional analyses. Issues of data quality and interpretation of soil survey data are discussed in the context of regional analyses of soil C. Areas for further development of data and modeling capabilities, including refining soil C maps, developing spatial databases on landuse and management practices, using remotely sensed data in regional model applications, and linking terrestrial ecosystem models with global climate models, are discussed.
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