Parameter optimisation for a better representation of drought by LSMs: inverse modelling vs. sequential data assimilation

Dewaele, Hélène; Munier, Simon; Albergel, Clément; Planque, Carole; Laanaia, Nabil; Carrer, Dominique; Calvet, Jean-Christophe

doi:https://doi.org/10.5194/hess-21-4861-2017

Articles | Volume 21, issue 9

https://doi.org/10.5194/hess-21-4861-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/hess-21-4861-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 21, issue 9

Research article

|

28 Sep 2017

Research article |

| 28 Sep 2017

Parameter optimisation for a better representation of drought by LSMs: inverse modelling vs. sequential data assimilation

Hélène Dewaele, Simon Munier, Clément Albergel, Carole Planque, Nabil Laanaia, Dominique Carrer, and Jean-Christophe Calvet

Supplement

https://doi.org/10.5194/hess-21-4861-2017-supplement

Data sets

LAI GEOV1 Copernicus http://land.copernicus.eu/global/

WFDEI atmospheric variables IIASA ftp://rfdata:forceDATA@ftp.iiasa.ac.at

annual statistical surveys over France Agreste http://agreste.agriculture.gouv.fr/

Short summary

Soil maximum available water content (MaxAWC) is a key parameter in land surface models. Being difficult to measure, this parameter is usually unavailable. A 15-year time series of satellite-derived observations of leaf area index (LAI) is used to retrieve MaxAWC for rainfed straw cereals over France. Disaggregated LAI is sequentially assimilated into the ISBA LSM. MaxAWC is estimated minimising LAI analyses increments. Annual maximum LAI observations correlate with the MaxAWC estimates.