Biogeochemical models have been used to evaluate ecosystem responses to global change. Recently, data assimilation has been applied to improve these models for ecological forecasting. It is not clear what the relative information contributions of NEE (net ecosystem exchange) vs. Biometric data are to constraints of forecasting. In this study, we assimilated three sets of five-year data (net ecosystem exchange, aboveground biomass, litter carbon) collected from Harvard Forest into a Terrestrial ECOsystem model (TECO). The relative information contribution was measured by Shannon information index calculated from probability density functions (PDF) 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 PDF of modeled carbon pools minus that in the uniform PDF while the relative data contribution was the information content in PDF of modeled carbon pools after data was assimilated minus that before data assimilation.

Results/Conclusions

Our results showed that the information contribution of NEE to constrain carbon dynamics is more than that of Biometric data in short term forecast. With the increase of forecast time range, information contribution of both NEE and biometric data decrease and the decrease rate of Biometric data is slower than that of NEE data. The knowledge on relative information contributions of NEE vs. Biometric data is useful for model development, uncertainty analysis, future data collection, and evaluation of ecological forecasting.