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
CNRM – UMR3589 (Météo-France, CNRS), Toulouse, 31057, France
Received: 02 Mar 2017 – Accepted for review: 16 Mar 2017 – Discussion started: 17 Mar 2017
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 unavailable. This study assesses the feasibility of using a fifteen-year (1999–2013) time-series of satellite-derived low resolution observations of Leaf Area Index (LAI) to retrieve MaxAWC for rainfed croplands over France. LAI inter-annual 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, (2) a more complex method consisting in integrating observed LAI in ISBA through a Land Data Assimilation System (LDAS) and minimizing LAI analysis increments. The evaluation of the MaxAWC retrievals 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 low resolution 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.
Dewaele, H., Munier, S., Albergel, C., Planque, C., Laanaia, N., Carrer, D., and Calvet, J.-C.: Parameter optimisation for a better representation of drought by LSMs: inverse modelling vs. sequential data assimilation, Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2017-120, in review, 2017.