# Monitoring and Forecasting the Impact of the 2018 Summer Heatwave on Vegetation
## 1 Introduction  
Land surface conditions are critical in the global weather and climate system. Accurate characterization and simulation of hydrological and biophysical variables at the land surface represent a significant challenge given large spatial heterogeneity and human modifications of the land surface. In particular, observing and simulating the response and feedback of land surface conditions to extreme events is crucial in our ability to manage adaptation to climate change impacts. Land Surface Model's (LSM's) role has evolved over the years, from the primary goal of providing boundary conditions to atmospheric models to being used as monitoring and forecasting tools for estimating land surface conditions [1; 2; 3; 4]. Modeling of terrestrial variables can be improved through the dynamical integration of observations [5; 6; 7] and there is a growing emphasis on constraining LSM estimates with observational inputs as well as coupling them with other models of the Earth system [1; 8; 9; 10]. Enhanced estimates of land surface conditions are also recognized to improve forecasts of weather patterns, sub-seasonal temperatures and precipitations, agricultural productivity, seasonal streamflow, floods and droughts, as well as the carbon cycle [11; 12; 13; 14; 15; 16]. Remote sensing observations are particularly useful in this context as they are now unrestrictedly available at a global scale with high spatial resolution and with long-term records. Many satellite-derived products relevant to the hydrological (e.g., soil moisture, snow depth/cover, terrestrial water storage), vegetation (e.g., leaf area index, biomass), and energy (e.g., land surface temperature, albedo) cycles are readily available [17]. Data assimilation techniques allow to spatially and temporally integrate the observed information into LSMs in a consistent way [5; 18]. We refer to Land Data Assimilation Systems (LDASs) as the framework where LSMs are driven by and/or ingest such observations generating enhanced estimates of the land surface variables (LSVs) [10]. Several LDASs now exist from point to regional scale, amongst them are the Global Land Data Assimilation System (GLDAS, [19]), the Carbon Cycle Data Assimilation System (CCDAS, [20]), the Coupled Land Vegetation LDAS (CLVLDAS, [21; 22]) and more recently the U.S. National Climate Assessment LDAS (NCA-LDAS, [10]) as well as LDAS-Monde [7; 18] to name a few. These LDASs either optimize process parameters (e.g., CCDAS), state variables (e.g., GLDAS, NCA-LDAS, LDAS-Monde) or both (e.g., CLVLDAS). Assimilated Earth Observations (EOs) generally include satellite retrieval of surface soil moisture [5; 8; 23; 24; 25], snow depth [26; 27; 28; 29] and snow cover [9; 27; 30; 31], vegetation [7; 18; 32; 33; 34; 35], as well as terrestrial water storage [36; 37; 38]. Few studies have included multiple remote sensing measurements. For instance, [PERSON] et al. [10] assimilates various remote sensing measurements of the terrestrial water cycle within the NCA-LDAS over the USA while LDAS-Monde [7; 18] considers the joint assimilation of vegetation (Leaf Area Index, LAI) and surface soil moisture (SSM) measurements. LDAS-Monde is a sequential land data assimilation system with global capacity. It has been evaluated over various domains at various spatial resolutions including France at 8 km scale [33; 39] forced by the SAFRAN reanalysis of Meteo-France (Systeme d'Analyse Fournissant des Renseignements Atmospheriques a la Neige [40; 41], Europe at 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\)[18; 35] forced by ERA-Interim atmospheric reanalysis from the European Centre For Medium Range Weather Forecast (ECMWF) [42]: North America [7] and Burkina-Faso in western Africa at 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\)[43] forced by ERA5 atmospheric reanalysis [44].