Estimating carbon dynamics using H2CM: a hybrid global carbon-water cycle model

Process-based models (PBMs) and machine learning (ML) offer complementary strengths for representing the coupled carbon-water cycle. PBMs enforce physical principles and provide interpretable diagnostics but rely on incomplete process knowledge, many priors, and very limited use of expanding Earth observations, leading to substantial inter-model spread. ML leverages observations to uncover complex patterns and reduce reliance on assumptions, but can violate physical constraints and extrapolate poorly. Hybrid modeling combines both, uniting ML’s flexibility with PBMs’ interpretability and process consistency.

We present H2CM, a hybrid carbon-water cycle model that merges process‑informed deep learning with direct learning from observations (Baghirov et al., 2025; https://doi.org/10.5194/egusphere-2025-3123). H2CM simulates carbon fluxes—gross primary productivity (GPP), autotrophic respiration, and heterotrophic respiration—and water storages (soil moisture, groundwater, snow) and fluxes (evapotranspiration, runoff). The model is informed by carbon observations—GPP, net ecosystem exchange (NEE) from satellite- and in situ–based inversions, and fAPAR—and by water-cycle observations—evapotranspiration, runoff, terrestrial water storage, and snow. H2CM runs daily at 1° spatial resolution.

H2CM outperforms both purely data-driven approaches and state-of-the-art PBMs in reproducing seasonal NEE, particularly in wet and dry tropics, and it captures the rain‑pulse respiration response in drylands that many models miss. Its estimates of global NEE interannual variability align more closely with satellite- and in situ–based inversion products than do PBM estimates. Finally, we disentangle photosynthetic versus respiratory controls and quantify how different regions (e.g., wet vs. dry tropics) contribute to global variability in land–atmosphere carbon exchange.