Monitoring of vegetation and drought in France using the ISBA land surface model and satellite data

The 2022 drought in Western Europe exposed major shortcomings in Europe's strategies for managing drought risk. In France, the drought was characterised by an unprecedented number of vegetation fires, as well as damage to buildings caused by clay shrink-swell. This has led to a growing awareness of the risks associated with drought and highlighted the need to improve climate risk management capabilities. While land surface models (LSMs) offer continuous temporal and spatial coverage, they may struggle to accurately represent certain processes due to their complexity. As LSMs cannot represent all relevant processes, data assimilation (DA) can be used to update them with observational data. This improves LSMs' capacity to monitor soil and vegetation variables driven by climatic and anthropogenic factors. In this study, we use the interactions between soil, biosphere and atmosphere (ISBA) land surface model within the SURFEX modelling platform, which was developed by Météo-France, to monitor land surface variables and characterise droughts. We analyse leaf area index (LAI) and root-zone soil moisture (RZSM) using sequential data assimilation (DA) and machine learning (ML) techniques in near-real time at a high spatial resolution, combining AROME weather forecast model forecasts with Copernicus Land Monitoring Service (CLMS) LAI observations derived from Sentinel-3. We use a satellite data assimilation system (LDAS, or Land Data Assimilation System) to correct ISBA model simulations by integrating LAI observations. We will present examples of applications related to monitoring natural hazards (e.g. clay shrinkage, wildfires and flash floods) in the context of the Integrated Research Insight into Climate Risks (IRICLIM) project (https://www.pepr-risques.fr/fr/projets-cibles/iriclim-recherche-integree-sur-risques-lies-au-climat).