Optimizing soil and plant hydraulic parameters in eCLM using multi-variable data assimilation of soil and vegetation observations

Accurate representation of soil and plant hydraulic processes is crucial for predicting forest water and carbon fluxes under variable climate conditions. At the Forest Research sites across Germany, we applied an Ensemble Smoother with Multiple Data Assimilation (ESMDA) coupled with the Community Land Surface Model (eCLM; https://github.com/HPSCTerrSys/eCLM) to optimize soil and vegetation parameters. Site-specific observations included soil moisture, evapotranspiration (ET), and dynamic canopy conductance, with the latter derived from sap-flow measurements. Across all sites, ensemble simulations were performed for 2012 – 2024 using 120 ensemble members in which key parameters controlling soil hydraulics, photosynthesis, stomatal behavior, and plant hydraulics were perturbed.

We tested several data assimilation configurations. Assimilating soil moisture alone improved simulated soil water content, reducing RMSE by 5–50% across soil depths, but had limited impact on ET, gross primary production (GPP), or net ecosystem exchange (NEE). In contrast, assimilating both soil moisture and ET further constrained vegetation parameters, resulting in modest improvements in ET, GPP and NEE, while maintaining a large ensemble spread that captures a high percentage of observations. Optimized soil and plant hydraulic parameters also enhanced the representation of seasonal plant water stress, capturing summer stress dynamics more realistically in both wet and dry years, with stronger hydraulic limitation during dry years.

These results indicate that correcting soil water availability alone is insufficient to improve plant water use and photosynthesis. Including ET and canopy conductance observations provides additional constraints, strengthening the interactions between soil moisture, transpiration, and carbon uptake. This demonstrates that multi-variable data assimilation is needed to effectively reduce uncertainty in both soil and plant hydraulics, and that direct physiological measurements, such as sap-flow, can further enhance model predictions of both water and carbon fluxes.