Review of the paper by Bonan et al. entitled "An Ensemble Square Root Filter for the joint assimilation of surface soil moiture and leaf area index within LDAS-Monde: application over the Euro-Mediterranean region"

Abstract. This paper introduces an Ensemble Square Root Filter (EnSRF), a deterministic Ensemble Kalman Filter, to the context of assimilating jointly observations of surface soil moisture (SSM) and leaf area index (LAI) in the Land Data Assimilation System LDAS-Monde. By ingesting those satellite-derived products, LDAS-Monde constrains the Interaction 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 (CTRIP), to improve the reanalysis of land surface variables (LSVs). To evaluate its ability to produce improved LSVs reanalyses, the EnSRF is compared with the Simplified Extended Kalman, which has been routinely operated in LDAS-Monde, in a real case over the well-studied Euro-Mediterranean region at a 0.25° spatial resolution between 2008 and 2017. Both data assimilation approaches provide a positive impact on SSM and LAI estimates with respect to the model alone, putting them closer to assimilated observations. SEKF and EnSRF have a similar behaviour for LAI showing performances that are influenced by the vegetation type. For SSM, EnSRF estimates tend to be closer to observations than SEKF. The impact of assimilating SSM and LAI is also assessed on unobserved soil moisture in the other layers of soil. Unobserved control variables are updated in the EnSRF through covariances and correlations sampled from the ensemble linking them to observed control variables. In our context, a strong correlation between SSM and soil moisture in deeper soil layers is exhibited, as expected, showing seasonal patterns that vary geographically. Moderate correlation and anti-correlations are also noticed between LAI and soil moisture in spring, summer and autumn, their absolute value tending to be larger for soil moisture in root-zone areas, showing that assimilating LAI can have an influence on soil moisture. Finally an independent evaluation of both assimilation approaches is conducted using satellite estimates of evapotranspiration and gross primary production (GPP) as well as measures of river discharges from gauging stations. The EnSRF shows a systematic albeit moderate improvement for evapotranspiration and GPP and a highly positive impact on river discharges, while the SEKF exhibits a more contrasting performance.

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Integrating ASCAT surface soil moisture and GEOV1 leaf area index into the SURFEX modelling platform: a land data assimilation application over France

Hydrology and Earth System Sciences ◽

10.5194/hess-18-173-2014 ◽

2014 ◽

Vol 18 (1) ◽

pp. 173-192 ◽

Cited By ~ 50

Author(s):

A. L. Barbu ◽

J.-C. Calvet ◽

J.-F. Mahfouf ◽

S. Lafont

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Leaf Area Index ◽

Leaf Area ◽

Surface Soil ◽

Surface Model ◽

Surface Soil Moisture ◽

Area Index ◽

Land Data Assimilation ◽

The Impact

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.

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Integrating ASCAT surface soil moisture and GEOV1 leaf area index into the SURFEX modelling platform: a land data assimilation application over France

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-10-9057-2013 ◽

2013 ◽

Vol 10 (7) ◽

pp. 9057-9103 ◽

Cited By ~ 2

Author(s):

A. L. Barbu ◽

J.-C. Calvet ◽

J.-F. Mahfouf ◽

S. Lafont

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Leaf Area Index ◽

Leaf Area ◽

Surface Soil ◽

Cumulative Distribution ◽

Surface Soil Moisture ◽

Area Index ◽

Land Data Assimilation ◽

The Impact

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 ASCAT backscatter data and the GEOV1 satellite-based LAI into the ISBA-A-gs land surface model within the SURFEX modelling platform of Meteo-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 France domain at a spatial resolution of 8 km. Each model grid box is divided in 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 to distribute the root-zone soil moisture and LAI increments among 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 showing the potential of the assimilation to improve drought monitoring. A comparison between simulated and in situ soil moisture gathered at the twelve SMOSMANIA stations shows improved anomaly correlations for eight stations.

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Assessment of Remotely Sensed Near-Surface Soil Moisture for Distributed Eco-Hydrological Model Implementation

Water ◽

10.3390/w11122613 ◽

2019 ◽

Vol 11 (12) ◽

pp. 2613 ◽

Cited By ~ 2

Author(s):

Carlos Echeverría ◽

Guiomar Ruiz-Pérez ◽

Cristina Puertes ◽

Luis Samaniego ◽

Brian Barrett ◽

...

Keyword(s):

Soil Moisture ◽

Leaf Area Index ◽

Leaf Area ◽

Surface Soil ◽

Remotely Sensed ◽

Validation Period ◽

Surface Soil Moisture ◽

Area Index ◽

Spatio Temporal ◽

Calibration Approach

The aim of this study was to implement an eco-hydrological distributed model using only remotely sensed information (soil moisture and leaf area index) during the calibration phase. Four soil moisture-based metrics were assessed, and the best alternative was chosen, which was a metric based on the similarity between the principal components that explained at least 95% of the soil moisture variation and the Nash-Sutcliffe Efficiency (NSE) index between simulated and observed surface soil moisture. The selected alternative was compared with a streamflow-based calibration approach. The results showed that the streamflow-based calibration approach, even presenting satisfactory results in the calibration period (NSE = 0.91), performed poorly in the validation period (NSE = 0.47) and Leaf Area Index (LAI) and soil moisture were neither sensitive to the spatio-temporal pattern nor to the spatial correlation in both calibration and validation periods. Hence, the selected soil moisture-based approach showed an acceptable performance in terms of discharges, presenting a negligible decrease in the validation period (ΔNSE = 0.1) and greater sensitivity to the spatio-temporal variables’ spatial representation.

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