Abstract
Abstract. The land monitoring service of the European Copernicus programme has developed a set of satellite-based biogeophysical products, including surface soil moisture (SSM) and leaf area index (LAI). This study investigates the impact of joint assimilation of remotely sensed SSM derived from Advanced Scatterometer (ASCAT) backscatter data and the Copernicus Global Land GEOV1 satellite-based LAI product into the the vegetation growth version of the Interactions between Soil Biosphere Atmosphere (ISBA-A-gs) land surface model within the the externalised surface model (SURFEX) modelling platform of Météo-France. The ASCAT data were bias corrected with respect to the model climatology by using a seasonal-based CDF (Cumulative Distribution Function) matching technique. A multivariate multi-scale land data assimilation system (LDAS) based on the extended Kalman Filter (EKF) is used for monitoring the soil moisture, terrestrial vegetation, surface carbon and energy fluxes across the domain of France at a spatial resolution of 8 km. Each model grid box is divided into a number of land covers, each having its own set of prognostic variables. The filter algorithm is designed to provide a distinct analysis for each land cover while using one observation per grid box. The updated values are aggregated by computing a weighted average. In this study, it is demonstrated that the assimilation scheme works effectively within the ISBA-A-gs model over a four-year period (2008–2011). The EKF is able to extract useful information from the data signal at the grid scale and distribute the root-zone soil moisture and LAI increments throughout the mosaic structure of the model. The impact of the assimilation on the vegetation phenology and on the water and carbon fluxes varies from one season to another. The spring drought of 2011 is an interesting case study of the potential of the assimilation to improve drought monitoring. A comparison between simulated and in situ soil moisture gathered at the twelve SMOSMANIA (Soil Moisture Observing System–Meteorological Automatic Network Integrated Application) stations shows improved anomaly correlations for eight stations.
Highlights
Monitoring the seasonal and interannual variability of the water and carbon cycles over land is needed for many applications, including hydrological and climate studies
The dimensionless values of NWG2 are larger than 1 when the soil moisture content exceeds the volumetric field capacity and negative when the soil moisture content is below the wilting point, meaning that the root water uptake has stopped
The France domain encompasses a wide variety of soil and vegetation ecosystems
Summary
Monitoring the seasonal and interannual variability of the water and carbon cycles over land is needed for many applications, including hydrological and climate studies. For simulated root-zone soil moisture, a mean volumetric standard deviation (std) error of 0.02 m3 m−3 was chosen, as suggested by several authors (Mahfouf et al, 2009; Draper et al, 2011; Barbu et al, 2011). In this experiment, the observational error is set to 0.05 m3 m−3 according to the median value of SSMsat ASCAT data error estimates. Concerning LAI, Barbu et al (2011) estimated in situ LAI observation errors for grassland in a previous study Their results are difficult to extend to satellite-based LAI uncertainties at the scale of France. The std errors of LAIsat is increased up to 0.3 in order to account for the additional uncertainties related to the aggregation procedure from 1 to 8 km resolution
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