Modelling soil moisture and latent heat fluxes at grassland sites in Central Europe

Agrometeorological modelling provides a powerful tool to gain insights into the dynamics of agricultural ecosystems, especially where measurements are unavailable. For practical applications like forecasting, both temporal patterns and absolute levels of target variables have to be confidently predicted by the model. In our EU-funded project, we are working on the Europe-wide application of the model AMBAV. This physics-based model, which computes coherent water and energy balances in agricultural soil-vegetation-atmosphere systems, is developed at Germany's national meteorological service DWD (Deutscher Wetterdienst), and is in operational use for the area of Germany. The aim of this study is to evaluate the performance of AMBAV at sites in neighbouring countries using local measurements as reference.

We used soil descriptions and meteorological time series from selected DWD and ICOS ecosystem stations to run AMBAV simulations for grassland sites, resulting in multi-year time series at hourly resolution. To assess the model performance, the model predictions of soil moisture in various soil depths and latent heat flux densities were compared to locally measured time series. A set of statistical metrics including Pearson's correlation, the mean error and the ratio of standard deviations as well as the Kling-Gupta-efficiency was used to report on model performance.

The results show that the AMBAV model can be used to reliably predict soil moisture and latent heat flux densities at Central-European grassland sites. For soil moisture, correlations above 0.75 and mean errors within ± 0.08 m³ m^-3 in soil depths down to 100 cm are achieved. Similarly high correlations are found for latent heat flux densities, while the magnitude of the mean error strongly depends on the corrections for energy balance closure typically applied to the measurement data. We also address seasonal variations of model performance in our evaluation. This work highlights strengths and weaknesses of the model AMBAV as well as the value of high-quality input and reference data. Our results encourage further investigation on a broad Europe-wide application of the AMBAV model.