Abstract ID: 099
Forecasting hydrometeorological drivers of forest damage over Europe
Lead Author: Pauline Rivoire
Institute of Earth Surface Dynamics, University of Lausanne, Switzerland
Keywords: extreme, hydro-meteorological hazards, vegetation, NDVI, drought
Abstract: 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 in 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. On the subseasonal-to-seasonal (S2S) forecasting timescale, skillful forecasts of hydro-meteorological hazards can be crucial to prevent large-scale vegetation damage. The utility of S2S forecasts for vegetation is very broad (agriculture and food security, biodiversity and flora protection, wildfire risk management, forest management, etc.).
We here focus on forest damage over Europe, defined as negative anomalies of the normalized difference vegetation index (NDVI). 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, among all the hydrometeorological variables available as output from the S2S forecasts in the ECMWF model, the most relevant ones to predict forest damage. For this purpose, we establish an automated procedure to identify the compound hydro-meteorological conditions leading to low NDVI events at the S2S timescale. 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 and soil temperature. 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.
To bridge the research gap between the S2S forecasts of hydrometeorological variables and vegetation damage, we assess the forecast skill of variables from the S2S hindcast database of ECMWF identified as responsible for low NDVI events. The idea is to determine to what extent S2S models can predict conditions triggering forest damage, by identifying the sources of predictability or potential need for improvement.
Co-authors:
Domeisen Daniela (Institute of Earth Surface Dynamics, University of Lausanne, Switzerland)
Guisan Antoine (Institute of Earth Surface Dynamics, University of Lausanne, Switzerland)
Vittoz Pascal (Institute of Earth Surface Dynamics, University of Lausanne, Switzerland)
