Rivoire et al. (2026) Identification of hydro-meteorological drivers for forest low greenness events in Europe

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: ETH Zürich Research Collection
• Year: 2026
• Date: 2026-03-09
• Authors: Pauline Rivoire, Sonia Dupuis, Antoine Guisan, Pascal Vittoz, Daniela Domeisen
• DOI: 10.3929/ethz-c-000797306

Research Groups

Not explicitly available in the provided text.

Short Summary

This study presents a novel, large-scale, spatially explicit analysis of forest browning drivers across Europe using a random forest modeling framework. It identifies key hydro-meteorological predictors, including warm and dry spring/early summer conditions and multi-year soil moisture and temperature anomalies, as crucial for explaining low Normalized Difference Vegetation Index (NDVI) events.

Objective

• To identify the most relevant hydro-meteorological predictors of low Normalized Difference Vegetation Index (NDVI) events at monthly to annual timescales across Europe, capturing the diversity of forest responses.

Study Configuration

• Spatial Scale: Europe, analyzed at 0.5 degrees grid points.
• Temporal Scale: Monthly to annual timescales, considering hydro-meteorological conditions up to 18 months prior to observed browning, and multi-year influences.

Methodology and Data

• Models used: Homogeneous and automated random forest modeling framework, with independent models run at each 0.5 degrees grid point.
• Data sources:
  • Normalized Difference Vegetation Index (NDVI) data from Advanced Very High Resolution Radiometers (AVHRR).
  • Climate variables (maximum temperature at 2 m, precipitation, surface latent heat flux, soil moisture) from ERA5 and ERA5-Land reanalyses.

Main Results

• The random forest model demonstrated high prediction skill over most European grid points, achieving a critical success index greater than 0.75 for 65 % of the grid points.
• Key hydro-meteorological predictors of forest browning include maximum temperature at 2 m, precipitation, surface latent heat flux, and soil moisture.
• Warm and dry conditions during spring and early summer were identified as essential predictors for browning events.
• Multi-year influences were significant, with soil moisture and temperature anomalies from the preceding year playing a substantial role, particularly in Scandinavia and for coniferous forests.
• The random forest approach revealed non-linear relationships, such as both positive and negative precipitation anomalies at different lags contributing to browning risk.

Contributions

• Provides a novel, large-scale, and spatially explicit analysis of forest browning drivers across Europe using a homogeneous and automated random forest modeling framework.
• Enables a region-specific comparison of hydro-meteorological drivers by running independent models at each 0.5 degrees grid point, thus capturing the diverse forest responses across the continent.
• Identifies critical multi-year influences and non-linear relationships between hydro-meteorological variables and forest browning, advancing the understanding of complex ecosystem responses.

Funding

Not explicitly available in the provided text.

Citation

@article{Rivoire2026Identification,
  author = {Rivoire, Pauline and Dupuis, Sonia and Guisan, Antoine and Vittoz, Pascal and Domeisen, Daniela},
  title = {Identification of hydro-meteorological drivers for forest low greenness events in Europe},
  journal = {ETH Zürich Research Collection},
  year = {2026},
  doi = {10.3929/ethz-c-000797306},
  url = {https://doi.org/10.3929/ethz-c-000797306}
}