Sequential assimilation of satellite-derived vegetation and soil moisture products using SURFEX_v8.0: LDAS-Monde assessment over the Euro-Mediterranean area

Abstract

In this study, a global Land Data Assimilation system (LDAS-Monde) is tested over Europe and the Mediterranean basin to increase monitoring accuracy for land surface variables. LDAS-Monde is able to ingest information from satellite-derived surface Soil Moisture (SM) and Leaf Area Index (LAI) observations to constrain the Interactions between Soil, Biosphere, and Atmosphere (ISBA) land surface model (LSM) coupled with the CNRM (Centre National de Recherches Météorologiques) version of the Total Runoff Integrating Pathways (ISBA-CTRIP) continental hydrological system. It makes use of the CO₂-responsive version of ISBA which models leaf-scale physiological processes and plant growth. Transfer of water and heat in the soil rely on a multilayer diffusion scheme. Surface SM and LAI observations are assimilated using a simplified extended Kalman filter (SEKF), which uses finite differences from perturbed simulations to generate flow-dependence between the observations and the model control variables. The latter include LAI and seven layers of soil (from 1 cm to 100 cm depth). A sensitivity test of the Jacobians over 2000–2012 exhibits effects related to both depth and season. It also suggests that observations of both LAI and surface SM have an impact on the different control variables. From the assimilation of surface SM, the LDAS is more effective in modifying soil-moisture from the top layers of soil as model sensitivity to surface SM decreases with depth and has almost no impact from 60 cm downwards. From the assimilation of LAI, a strong impact on LAI itself is found. The LAI assimilation impact is more pronounced in SM layers that contain the highest fraction of roots (from 10 cm to 60 cm). The assimilation is more efficient in summer and autumn than in winter and spring. Assimilation impact shows that the LDAS works well constraining the model to the observations and that stronger corrections are applied to LAI than to SM. The assimilation impact's evaluation is successfully carried out using (i) agricultural statistics over France, (ii) river discharge observations, (iii) satellite-derived estimates of land evapotranspiration from the Global Land Evaporation Amsterdam Model (GLEAM) project and (iv) spatially gridded observations based estimates of up-scaled gross primary production and evapotranspiration from the FLUXNET network. Comparisons with those four datasets highlight neutral to highly positive improvement.