Abstract
Abstract. In this study, a global land data assimilation system (LDAS-Monde) is applied 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 (SSM) and leaf area index (LAI) observations to constrain the interactions between soil–biosphere–atmosphere (ISBA, Interactions between Soil, Biosphere and Atmosphere) 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. SSM 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 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 SSM have an impact on the different control variables. From the assimilation of SSM, the LDAS is more effective in modifying soil moisture (SM) from the top layers of soil, as model sensitivity to SSM 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 to 60 cm). The assimilation is more efficient in summer and autumn than in winter and spring. Results shows that the LDAS works well constraining the model to the observations and that stronger corrections are applied to LAI than to SM. A comprehensive evaluation of the assimilation impact is conducted 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 observation-based estimates of upscaled gross primary production and evapotranspiration from the FLUXNET network. Comparisons with those four datasets highlight neutral to highly positive improvement.
Highlights
Land surface models (LSMs) forced by gridded atmospheric variables and their coupling with river routing models are important for understanding the terrestrial water and vegetation cycles (Dirmeyer et al, 2006)
This study provides an assessment of the land data assimilation system (LDAS)-Monde implementation to increase monitoring accuracy for land surface variables over the Europe–Mediterranean area
Satellitederived surface soil moisture and leaf area index are assimilated over 2000–2012 in the CO2-responsive and multilayer diffusion scheme version of the ISBA land surface model coupled with the CTRIP hydrological system
Summary
Land surface models (LSMs) forced by gridded atmospheric variables and their coupling with river routing models are important for understanding the terrestrial water and vegetation cycles (Dirmeyer et al, 2006). These LSMs need to simulate biogeophysical variables such as surface, root zone soil. The seasonal dynamics of vegetation properties, such as LAI, are connected to soil moisture dynamics (Kochendorfer and Ramirez, 2010) Both the simulation of hydrological processes and the exchange of water vapour and CO2 between the vegetation canopy and atmosphere interface are strongly influenced by LAI (Jarlan et al, 2008; Szczypta et al, 2014)
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