They were organised in 4 work packages with the aim of improving the performance of the ExpeER infrastructures and related services in a sustainable and coordinated manner:
WP 7: Develop and test new methods to overcome current limitations in understanding ecosystem processes.
Participant involved: INRA - BGU - CNRS - FSU JENA - Jülich - SOTON - TUM - UFZ
A first main task of this work package was the advancement of techniques for creating future environmental conditions in terms of both realism and cost, with a focus on warming and CO2 enrichment. In designing realistic warming experiments, it was aimed to identify potential artifacts associated with currently used methods to increase temperatures such as greenhouses, infrared heaters and monolith translocation and propose solutions to minimise errors. This includeed unwanted secondary artifacts such as changes in (soil and air) moisture conditions. Computational Fluid Dynamics were mainly used to create spatial CO2 concentration gradients over large areas, but the technique could also be used for imposing temperature increases. A second task involves designing new approaches for experimental ecosystems, including model analogues of large-scale systems (e.g. oceans) and studying whether we can define a standardised ecosystem that includes the most appropriate biotic and abiotic components. A final task was to design new biodiversity and climate change experiments using knowledge gained from the other tasks and with the goal to design experimental approaches that are more realistic and more generalisable than past attempts.
Participant involved: INRA - CNR - CNRS - FSU JENA - Imperial - KIT - UA - UFZ - UP
A modelling toolkit including dynamic ecosystem models and plant community dynamics was developed and made available for the users of the infrastructure. The toolkit includes a parameter library, models of hydrological and biogeochemical dynamics, vegetation dynamics/species interactions as well as evaluations tool for uncertainty estimations. Models were chosen that are well documented and tested. A model parameter library was developed based on freeware global parameter databases and existing model applications and parameter sets tested and enhanced and parameter uncertainties assessed. A dynamic vegetation modeling component was developed with improved integration of dynamic vegetation processes into ecosystem hydrology and biogeochemistry models. A model toolbox/workspace was developed for the site owners to easily get access to these models, parameter settings and documentation. This workspace included enhanced ecosystem models representing hydrological, biogeochemical and dynamic vegetation components and evaluation tools to provide scientific testing of hypotheses and extrapolation of results from the experiments. A simple-to-use system of evaluating model performance was included with a menu-driven series of options to run the models with new or different parameter sets. The workspace included all relevant model documentation (design, links to data and parameter settings and model outputs), manuals and instructions (theoretical basis, user guide, variables, inputs and outputs and model connections) and the ecosystem specific parameter library. The use of the workspace provided through involvement of the site researchers and users through workshops and training courses.
Participant involved: INRA - DTU - KTH - NERC - ULUND
The modelling toolbox is now available here.
WP 10: Development of upscaling and data interpretations methods of biogeochemical and ecological processes.
In this workpackage “upscaling tools” were developed to assess the potential of the ExpeER infrastructure to upscale ecosystem responses to environmental changes at large scales. It extended the work in WP9 by providing complementary numerical tools based on model-data fusion approaches. The work that has been carried out can be divided into three categories :
- The development of an operational upscaling framework in space and time for biogeochemical fluxes including water, C and N. It is planned to use land surface models like ORCHIDEE and CLM together with measurement data (including remote sensing data) as input for data assimilation methods like 4DVAR and Ensemble Kalman Filter for the upscaling. The upscaling is based on the estimation of effective parameters with help of the mentioned methods.
- In addition, an upscaling tool will be provided for determining species density and functional diversity based on data-oriented statistical models, like for example wavelet-based methods.
- On the basis of the developed upscaling frameworks for biogeochemical fluxes and biodiversity, which will be tested jointly for at least one experimental site, we expect to come up with improved insights on monitoring strategies. It was expected to gain more insight in the relative value of certain data types for certain predictions and the required monitoring density. The optimisation of the monitoring strategy was focused on the more reliable prediction of the ecosystem response with respect to climate change.
Participant involved: CNRS - Jülich - UFZ - UHEL - UNIVLEEDS