Vorobevskii et al. (2026) The role of multivariate time-series consistency in micrometeorological forcing data for climate impact modelling

Identification

• Journal: Agricultural and Forest Meteorology
• Year: 2026
• Date: 2026-08-14
• Authors: Ivan Vorobevskii, Rico Kronenberg, Thomas Grünwald, Matthias R. Mauder
• DOI: 10.1016/j.agrformet.2026.111416

Research Groups

• Faculty of Environmental Sciences, Chair of Meteorology, TUD Dresden University of Technology, Tharandt, 01737, Germany
• ICOS (Integrated Carbon Observation System)
• FLUXNET

Short Summary

This study assesses the impact of dataset versioning and gap-filled micrometeorological forcing data on process-based climate impact models. The analysis focuses on the subdaily version of the physically based one-dimensional soil-plant-atmosphere model BROOK90, applied to the ICOS ecosystem site DE-Tha in Tharandt, Germany.

Objective

• Investigate how dataset version updates and gap-filled periods affect simulated water and energy fluxes.
• Examine the sensitivity of process-based climate impact models to micrometeorological forcing data uncertainties.

Study Configuration

• Spatial Scale: Local scale (ICOS ecosystem site DE-Tha)
• Temporal Scale: Subdaily resolution

Methodology and Data

• Models used: BROOK90, a physically based one-dimensional soil-plant-atmosphere model.
• Data sources: ICOS ecosystem products, including gap-filled micrometeorological variables.

Main Results

• Dataset version updates led to substantial differences in historical time series of air temperature, solar radiation, precipitation, wind speed, and vapor pressure deficit.
• Gap-filled periods introduced forcing-data sensitivities that affected model performance and numerical stability.
• Multivariate forcing substitution amplified the effects of gap-filling on simulated energy fluxes.

Contributions

• This study highlights the importance of explicit dataset version tracking and diagnostic checks of gap-filled forcing periods in process-based climate impact modeling.
• The findings underscore the need for continued development of user guidance for reproducible and physically interpretable impact modeling.

Funding

• Not specified

Citation

@article{Vorobevskii2026role,
  author = {Vorobevskii, Ivan and Kronenberg, Rico and Grünwald, Thomas and Mauder, Matthias R.},
  title = {The role of multivariate time-series consistency in micrometeorological forcing data for climate impact modelling},
  journal = {Agricultural and Forest Meteorology},
  year = {2026},
  doi = {10.1016/j.agrformet.2026.111416},
  url = {https://doi.org/10.1016/j.agrformet.2026.111416}
}