In my thesis I investigate how we can best improve our understanding of ecosystem physiology by integrating multiple data-streams into a diagnostic model. Our understanding of ecosystem processes and their interactions is strongly limited. The ongoing progress of new land- and spaceborne measurement and remote-sensing techniques, by contrast, offers a multitude of new observations with the potential of both reducing parameter uncertainties and excluding inconsistent model formulations. Global observations are however still not available for many processes and variables. We hope to bridge this gap by using a diagnostic ecosystem model that meets minimum physical requirements such as mass and energy balances. By limiting the overall level of model-complexity we can constrain the model-parameters and avoid the problem of equifinality. The goal is a data-informed framework that enables the testing of submodels and the diagnostic analysis of global carbon–water covariations.
Boese, S., Lange, H. (2012). Recurrence analysis of global photosynthentic activity. Data analysis and modeling in the earth science (DAMES), Potsdam, 8–10 October 2012.
Boese, S., Jung, M., Carvalhais, N., Reichstein, M. (2015). Understanding the Spatiotemporal Variability of Inherent Water Use Efficiency. European Geosciences Union General Assembly 2015, Vienna, Austria, 12–17 April 2015.
Lange, H., Boese, S. (2014). Recurrence Quantification and Recurrence Network Analysis of Global Photosynthetic Activity. In C.L. Webber & N. Marwan (Eds.), Recurrence Quantification Analysis – Theory and Best Practices (p. 349–374). Springer.