Deep-learning and process-based models for simulating carbon and water fluxes across scales (C04)

• Subject Area: Ecology and Biodiversity of Plants and Ecosystems ; Soil Sciences ; Data Management, Data-Intensive Systems, Computer Science Methods in Business Informatics ; Forestry ; Plant Physiology
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 459819582

C4.1 Process-based model simulationsWe will develop process-based model simulations and deep learning tools for data analysis to interact with and optimize the sensor-based monitoring, as well as to deepen our understanding of impacts of spatio-temporal heterogeneity and dynamics for total ecosystem water and carbon exchange. An existing 2D process-based model will be extended, calibrated and run in a now- and forecasting 3D mode, covering spatio-temporal heterogeneity of small-scale processes and integrating new scaling laws for non-linear interactions.C4.2 Deep learningUsing deep-learning algorithms, the plethora of data will be efficiently evaluated to distinguish between important and redundant data. The aim is to provide sufficient spatio-temporal resolution and save sensor node energy and reduce redundant data accumulation. Thereby, deep learning and process simulations interact with the sensor network through i) data assimilation from the sensor network and ii) adjustment and optimization of the measuring design based on simulated outputs and predictions

• DFG Programme: Collaborative Research Centres
• Subproject of: SFB 1537: ECOSENSE – Multiscale quantification of spatio-temporal dynamics of ecosys-tem processes by smart autonomous sensor networks
• Applicant Institution: Albert-Ludwigs-Universität Freiburg
• Project Heads: Professor Dr. Carsten Dormann; Dr. Rüdiger Grote; Privatdozent Dr. Ralf Kiese