Compounding hydro-meteorological drivers of forest damage over Europe

Extreme meteorological events such as frost, heat, and drought can induce significant damage to vegetation and ecosystems. In particular, heat and drought events are projected to become more frequent under a changing climate. It is therefore crucial to predict the frequency (on climate timescales) and the occurrence (on timescales of weeks to months) of such extremes.

The subseasonal-to-seasonal (S2S) forecasting timescale refers to forecasting timescales from two weeks to a season. Skillful S2S forecasts of hydro-meteorological hazards can be of crucial importance to prevent large-scale vegetation damage. The utility of S2S forecasts for vegetation is very broad (agriculture, biodiversity and flora protection, wildfire risk management, forest management, etc.).

We focus here on forest damage, defined as negative anomalies of the normalized difference vegetation index (NDVI). We use the AVHRR dataset, providing NDVI data over Europe. Compound droughts and heat waves 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. The idea is to find, among all the hydrometeorological variables available as S2S forecast in the ECMWF model, the most relevant ones to predict forest damage. For that, we establish an automated procedure to identify the compound hydro-meteorological conditions leading to low NDVI events, up to several seasons before the impact. We train a model using ERA5 and ERA5-Land reanalysis datasets for the explicative variables. These variables include temperature, precipitation, dew point temperature, surface latent heat flux, soil moisture, snow water equivalent, soil temperature, etc. Several space and time aggregations are considered in order to find the optimal scales and most relevant combinations of variables to predict low NDVI events. The overall goal of this research project is to bridge the research gap between the S2S forecast of hydrometeorological variables and vegetation damage in general. For that, we assess the forecast skill of variables identified as responsible for compound low NDVI events and vegetation biodiversity loss.