Lazaro Alonso
A scientist at the Max Planck Institute for Biogeochemistry, advancing earth system models through hybrid modeling, integrating process-based models with machine learning. Through open, reproducible research and compelling visualizations, I bridge the gap between cutting-edge research and societal impact.
Sessions
The SINDBAD framework, with Sindbad.jl, SindbadTEM, OmniTools.jl, TimeSamplers.jl, and ErrorMetrics.jl packages offers a user‑friendly, Julia‑based system for terrestrial model–data integration. It enables scalable, differentiable experiments across spatial and temporal scales, supporting next‑generation understanding of vegetation–water–carbon interactions.
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.