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

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<p><strong>Abstract.</strong> 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 CO<sub>2</sub>-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 (<i>B</i><sub>ag</sub>) 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 (<i>p</i> value<span class="thinspace"></span> < <span class="thinspace"></span>0.01) between <i>B</i><sub>ag</sub> and GY are found for up to 36 and 53<span class="thinspace"></span>% 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 <i>B</i><sub>ag</sub> than the inverse modelling experiment. Using undisaggregated LAI observations leads to an underestimation of MaxAWC and maximum <i>B</i><sub>ag</sub> in both experiments. Median annual maximum values of disaggregated LAI observations are found to correlate very well with MaxAWC.</p>
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