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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 21, issue 9
Hydrol. Earth Syst. Sci., 21, 4861–4878, 2017
https://doi.org/10.5194/hess-21-4861-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 21, 4861–4878, 2017
https://doi.org/10.5194/hess-21-4861-2017
© Author(s) 2017. This work is distributed under
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, Simon Munier, Clément Albergel, Carole Planque, Nabil Laanaia, Dominique Carrer, and Jean-Christophe Calvet Hélène Dewaele et al.
  • CNRM – UMR3589 (Météo-France, CNRS), Toulouse, 31057, France

Abstract. Soil maximum available water content (MaxAWC) is a key parameter in land surface models (LSMs). However, being difficult to measure, this parameter is usually uncertain. This study assesses the feasibility of using a 15-year (1999–2013) time series of satellite-derived low-resolution observations of leaf area index (LAI) to estimate MaxAWC for rainfed croplands over France. LAI interannual variability is simulated using the CO2-responsive version of the Interactions between Soil, Biosphere and Atmosphere (ISBA) LSM for various values of MaxAWC. Optimal value is then selected by using (1) a simple inverse modelling technique, comparing simulated and observed LAI and (2) a more complex method consisting in integrating observed LAI in ISBA through a land data assimilation system (LDAS) and minimising LAI analysis increments. The evaluation of the MaxAWC estimates from both methods is done using simulated annual maximum above-ground biomass (Bag) and straw cereal grain yield (GY) values from the Agreste French agricultural statistics portal, for 45 administrative units presenting a high proportion of straw cereals. Significant correlations (p value  <  0.01) between Bag and GY are found for up to 36 and 53 % of the administrative units for the inverse modelling and LDAS tuning methods, respectively. It is found that the LDAS tuning experiment gives more realistic values of MaxAWC and maximum Bag than the inverse modelling experiment. Using undisaggregated LAI observations leads to an underestimation of MaxAWC and maximum Bag in both experiments. Median annual maximum values of disaggregated LAI observations are found to correlate very well with MaxAWC.

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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...
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