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.
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.
the Creative Commons Attribution 3.0 License.
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
CNRM – UMR3589 (Météo-France, CNRS), Toulouse, 31057, France
Simon Munier
CNRM – UMR3589 (Météo-France, CNRS), Toulouse, 31057, France
Clément Albergel
CNRM – UMR3589 (Météo-France, CNRS), Toulouse, 31057, France
Carole Planque
CNRM – UMR3589 (Météo-France, CNRS), Toulouse, 31057, France
Nabil Laanaia
CNRM – UMR3589 (Météo-France, CNRS), Toulouse, 31057, France
Dominique Carrer
CNRM – UMR3589 (Météo-France, CNRS), Toulouse, 31057, France
Jean-Christophe Calvet
CORRESPONDING AUTHOR
CNRM – UMR3589 (Météo-France, CNRS), Toulouse, 31057, France
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Cited
13 citations as recorded by crossref.
- Remote Sensing Data for Digital Soil Mapping in French Research—A Review A. Richer-de-Forges et al. 10.3390/rs15123070
- SMAP soil moisture improves global evapotranspiration A. Purdy et al. 10.1016/j.rse.2018.09.023
- LDAS-Monde Sequential Assimilation of Satellite Derived Observations Applied to the Contiguous US: An ERA-5 Driven Reanalysis of the Land Surface Variables C. Albergel et al. 10.3390/rs10101627
- Why make inverse modeling and which methods to use in agriculture? A review Y. Zhang et al. 10.1016/j.compag.2024.108624
- Monitoring and Forecasting the Impact of the 2018 Summer Heatwave on Vegetation C. Albergel et al. 10.3390/rs11050520
- An ensemble square root filter for the joint assimilation of surface soil moisture and leaf area index within the Land Data Assimilation System LDAS-Monde: application over the Euro-Mediterranean region B. Bonan et al. 10.5194/hess-24-325-2020
- From Monitoring to Forecasting Land Surface Conditions Using a Land Data Assimilation System: Application over the Contiguous United States A. Mucia et al. 10.3390/rs12122020
- Available water capacity from a multidisciplinary and multiscale viewpoint. A review I. Cousin et al. 10.1007/s13593-022-00774-8
- Towards constraining soil and vegetation dynamics in land surface models: Modeling ASCAT backscatter incidence-angle dependence with a Deep Neural Network X. Shan et al. 10.1016/j.rse.2022.113116
- Hydrological assessment of atmospheric forcing uncertainty in the Euro-Mediterranean area using a land surface model E. Gelati et al. 10.5194/hess-22-2091-2018
- A multi-sourced assessment of the spatiotemporal dynamics of soil moisture in the MARINE flash flood model J. Eeckman et al. 10.5194/hess-25-1425-2021
- Sequential assimilation of satellite-derived vegetation and soil moisture products using SURFEX_v8.0: LDAS-Monde assessment over the Euro-Mediterranean area C. Albergel et al. 10.5194/gmd-10-3889-2017
- An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications H. Fang et al. 10.1029/2018RG000608
13 citations as recorded by crossref.
- Remote Sensing Data for Digital Soil Mapping in French Research—A Review A. Richer-de-Forges et al. 10.3390/rs15123070
- SMAP soil moisture improves global evapotranspiration A. Purdy et al. 10.1016/j.rse.2018.09.023
- LDAS-Monde Sequential Assimilation of Satellite Derived Observations Applied to the Contiguous US: An ERA-5 Driven Reanalysis of the Land Surface Variables C. Albergel et al. 10.3390/rs10101627
- Why make inverse modeling and which methods to use in agriculture? A review Y. Zhang et al. 10.1016/j.compag.2024.108624
- Monitoring and Forecasting the Impact of the 2018 Summer Heatwave on Vegetation C. Albergel et al. 10.3390/rs11050520
- An ensemble square root filter for the joint assimilation of surface soil moisture and leaf area index within the Land Data Assimilation System LDAS-Monde: application over the Euro-Mediterranean region B. Bonan et al. 10.5194/hess-24-325-2020
- From Monitoring to Forecasting Land Surface Conditions Using a Land Data Assimilation System: Application over the Contiguous United States A. Mucia et al. 10.3390/rs12122020
- Available water capacity from a multidisciplinary and multiscale viewpoint. A review I. Cousin et al. 10.1007/s13593-022-00774-8
- Towards constraining soil and vegetation dynamics in land surface models: Modeling ASCAT backscatter incidence-angle dependence with a Deep Neural Network X. Shan et al. 10.1016/j.rse.2022.113116
- Hydrological assessment of atmospheric forcing uncertainty in the Euro-Mediterranean area using a land surface model E. Gelati et al. 10.5194/hess-22-2091-2018
- A multi-sourced assessment of the spatiotemporal dynamics of soil moisture in the MARINE flash flood model J. Eeckman et al. 10.5194/hess-25-1425-2021
- Sequential assimilation of satellite-derived vegetation and soil moisture products using SURFEX_v8.0: LDAS-Monde assessment over the Euro-Mediterranean area C. Albergel et al. 10.5194/gmd-10-3889-2017
- An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications H. Fang et al. 10.1029/2018RG000608
Latest update: 14 Dec 2024
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.
Soil maximum available water content (MaxAWC) is a key parameter in land surface models. Being...
