LDAS-Monde Sequential Assimilation of Satellite-Derived Vegetation and Soil Moisture Products Impact over North America (Invited Presentation)

In this study LDAS-Monde, a land data assimilation system with global capacity, is applied over North America to increase monitoring accuracy for land surface moisture energy and water states and fluxes, including evapotranspiration and stream flow as well as vegetation growth. LDAS-Monde ingests information from satellite-derived Surface Soil Moisture (SSM) and Leaf Area Index (LAI) estimates 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. LDAS-Monde uses the CO₂-responsive version of ISBA which models leaf-scale physiological processes and plant growth, while transfer of water and heat through the soil rely on a multilayer diffusion scheme. Surface SSM and LAI estimates 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 (LAI and seven layers of soil: from 1 cm to 100 cm depth).

LDAS-Monde analysis impact over 2007-2016 is assessed over North America using satellite-driven model estimates of land evapotranspiration from the Global Land Evaporation Amsterdam Model (GLEAM) project and upscaled ground-based observations of gross primary productivity from the FLUXCOM project. Over the US, in-situ measurements of soil moisture from the USCRN network and of turbulent heat fluxes and GPP from FLUXNET-2015 are used in the evaluation, together river discharges from the USGS. Those data sets highlight the added value of LDAS-Monde compared to an open-loop simulation (i.e. no assimilation). Finally, it has been shown that LDAS-Monde has a strong ability to monitor agricultural drought, by providing improved initial conditions which persist through time, aiding agricultural drought prediction.