Bernhard Ahrens
Bernhard Ahrens leads the “Modeling Interactions in Soil Systems” group at the Max Planck Institute for Biogeochemistry in Jena and works in the Biogeochemical Integration Department under Prof. Dr. Markus Reichstein. He is a geoecologist and global change ecologist working at the interface of process-based soil organic matter modeling, data integration, and machine learning. Methodologically, he codevelops the software package EasyHybrid.jl to embed neural networks into process-based models. He (co-)developed the soil organic matter turnover models COMISSION v1.0 and v2.0 as well as the Jena Soil Model. He supervises several PhD and postdoctoral projects, including within the AI4SoilHealth and WETSCAPES2.0 consortia.
Session
Scientific modeling often forces a choice between flexible but opaque neural networks and interpretable process-based models that can be too rigid for real-world data. Hybrid modeling bridges this gap by combining mechanistic structure with machine-learning flexibility. In this talk we introduce EasyHybrid.jl, a user-friendly Julia package that makes hybrid modeling accessible across disciplines. We demonstrate the approach on a concrete problem: partitioning eddy-covariance net carbon fluxes into photosynthesis (a CO₂ sink) and ecosystem respiration (a CO₂ source), while estimating how strongly respiration responds to temperature. Temperature sensitivity is summarized by Q10, the factor by which respiration changes for a 10 K warming (e.g., Q10 = 2 means doubles per 10 K). We present cross-site results across hundreds of FLUXNET eddy covariance towers and show that, even when inferred jointly with hybrid flux partitioning, the learned temperature sensitivity exhibits a relatively narrow convergence across ecosystems.