Identification of hydro-meteorological drivers for low greenness events in Europe

Abstract. Extreme hydro-meteorological events can have a substantial impact on vegetation and ecosystems. In particular, with heatwaves and droughts projected to become more frequent due to climate change, understanding their effects on forests is crucial. The goal of our study is to find the most relevant predictors for forest damage in Europe at monthly to annual timescales. Using a Random Forest approach, we pinpoint hydro-meteorological conditions associated with low normalized difference vegetation index (NDVI) events. We train the model using the NDVI from the Advanced Very High Resolution Radiometers (AVHRR) as the predictand, and a range of variables from the ERA5 and ERA5-Land reanalysis as hydro-meteorological predictors. These predictors include maximum temperature at 2 meters and dewpoint temperature, precipitation, surface latent heat flux, and soil moisture up to 18 months before the observed damage. The random forest model exhibits a high prediction skill over most gridpoints in Europe, with a critical success index greater than 0.75 for 67 % of gridpoints. Notably, warm and dry conditions in spring and early summer emerge as essential predictors. Additionally, we emphasize a longer-term relationship between hydro-meteorological conditions and forest damage. For instance, low dewpoint temperatures one year before the studied summer impact broad-leaved forests, while soil moisture during the preceding autumn influences low greenness events in coniferous forests, albeit with location-specific variations.