Abstract
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.
Highlights
Extreme weather conditions markedly affect agricultural production
In a first step before integrating the disaggregated leaf area index (LAI) observations into the ISBA model, we checked the consistency of the interannual variability of LAIomax (Sect. 2.6) with that of the observed grain yield (GY) from Agreste
The 45 grid cells of 35 km × 35 km are further used to calculate average 10-day LAI observations to be integrated in the ISBA model through either inverse modelling or land data assimilation system (LDAS) tuning
Summary
Extreme weather conditions markedly affect agricultural production. The interannual variability of rainfed crop yields is driven to a large extent by the climate variability. Comparing agricultural statistics to climate data shows the impact of atmospheric conditions on vegetation production. Capa-Morocho et al (2014) showed the influence of air temperature on crop yields. Li et al (2010) showed that air temperature tends to influence mean crop yields at small scales (400 to 600 km), whereas rainfall drives crop yields at larger scales (50 to 300 km). They established a link between temperature anomalies related to the El Niño phenomenon and potential crop yield anomalies, obtained from reanalysis data and crop model, respectively
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