Frontiers of Ecosystem Modeling and Large-Scale Experiments

This chapter reviews the need, history, and current status of data-model integration to improve model simulations. It presents some projects, Spruce and Peatland Responses Under Climatic and Environmental Change Experiment and Extreme Drought in Grasslands Experiment, model integration approaches are designed to achieve different research goals. Tremendous efforts have been made to improve the predictability of ecosystem states and processes using three independent approaches: carefully designed long-term observational studies, ecological experimentation, and process-based modeling. Empirical data from observational and experimental studies are useful in exploring general patterns of ecosystem phenomena and studying underlying mechanisms. Ecological models, particularly process-based models, can supplement limitations inherent in observations and experiments. Process-based models have advanced quickly in recent years, which can partly be attributed to dramatic increase of computational capability. Empirical data can help model development and improvement through a few pathways: providing mechanistic understanding behind ecosystem processes, generalizing patterns, identifying ranges and patterns of key model parameters, and constraining models.