Hydro-Meteorological Drivers of Forest Damage over Europe

Extreme meteorological events, such as heat and drought, can induce significant damage to vegetation and ecosystems. The frequency and intensity of extreme events are subject to change due to anthropogenic global warming. It is therefore crucial to quantify the impact of such events for better preparedness.

Here, we focus on forest damage in Europe, defined as negative anomalies of the normalized difference vegetation index (NDVI, a measure of vegetation greenness). Compound drought and heat wave events are known to trigger low NDVI events in summer. A dry summer combined with moist conditions during the previous autumn can also have a negative impact. Hence, 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 NDVI events. We train the model using remote sensing observations of 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.

We provide an automated procedure with strong predictive performance for identifying low-greenness events during summer based on prior hydro-meteorological conditions. The most essential preceding periods and variables are location and forest-type dependent. 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.