Relative information contributions of model vs. data to short- and long-term forecasts of forest carbon dynamics

Biogeochemical models have been used to evaluate long-term ecosystem responses to global change on decadal and century time scales. Recently, data assimilation has been applied to improve these models for ecological forecasting. It is not clear what the relative information contributions of model (structure and parameters) vs. data are to constraints of short- and long-term forecasting. In this study, we assimilated eight sets of 10-year data (foliage, woody, and fine root biomass, litter fall, forest floor carbon [C], microbial C, soil C, and soil respiration) collected from Duke Forest into a Terrestrial Ecosystem model (TECO). The relative information contribution was measured by Shannon information index calculated from probability density functions (PDFs) of carbon pool sizes. The null knowledge without a model or data was defined by the uniform PDF within a prior range. The relative model contribution was information content in the PDF of modeled carbon pools minus that in the uniform PDF, while the relative data contribution was the information content in the PDF of modeled carbon pools after data was assimilated minus that before data assimilation. Our results showed that the information contribution of the model to constrain carbon dynamics increased with time whereas the data contribution declined. The eight data sets contributed more than the model to constrain C dynamics in foliage and fine root pools over the 100-year forecasts. The model, however, contributed more than the data sets to constrain the litter, fast soil organic matter (SOM), and passive SOM pools. For the two major C pools, woody biomass and slow SOM, the model contributed less information in the first few decades and then more in the following decades than the data. Knowledge of relative information contributions of model vs. data is useful for model development, uncertainty analysis, future data collection, and evaluation of ecological forecasting.