Hydrodynamic Parameter Estimation for Simulating Soil-Vegetation-Atmosphere Hydrology Across Forest Stands in the Strengbach Catchment

Modeling the water cycle in the critical zone requires understanding interactions between the soil–vegetation–atmosphere compartments. Mechanistic modeling of soil water flow relies on the accurate determination of hydrodynamic parameters that control hydraulic conductivity and water retention curves. These parameters can be derived either using pedotransfer functions (PTFs), using soil properties obtained from field samples, or through inverse modeling, which allows the parameters to be adjusted to minimize differences between simulations and observations. While PTFs are widely used due to their simplicity, inverse modeling requires specific instrumentation and advanced numerical tools. This study, conducted at the Hydro-Geochemical Environmental Observatory (Strengbach forested catchment) in France, aims to determine the optimal hydrodynamic parameters for two contrasting forest plots, one dominated by spruce and the other by beech. The methodology integrates granulometric data across multiple soil layers to estimate soil parameters using PTFs (Rosetta). Water content and conductivity data were then corrected to account for soil stoniness, improving the KGE and NSE metrics. Finally, inverse parameter estimation based on water content measurements allowed for refinement of the evaluation of α, Ks, and n. This framework to estimate soil parameter was applied on different time periods to investigate the influence of the calibration chronicles on the estimated parameters. Results indicate that our methodology is efficient and that the optimal calibration period does not correspond to one with the most severe drought conditions; instead, a balanced time series including both wet and dry phases is preferable. Our findings also emphasize that KGE and NSE must be interpreted with caution, and that long simulation periods are essential for evaluating parameter robustness.