Spatio-temporal variability of dry and wet spells and their influence on crop yields

Abstract

Spatial modelling of extreme dry (wet) spells is relevant to design agricultural insurances and establish risk management strategies. We characterised dry (wet) spells during the period 1971–2010 in 133 rainfall stations and investigated their impact on crop yields across Belgium. A day was considered as a dry (wet) day when the total amount of rainfall is below (above) 0.2 mm. A two-way ANOVA demonstrated significant differences between low and high yields across 12 agricultural regions in the number of consecutive dry (wet) days, rainfall amount during the wet days and their total during the crop growing season. Generalised Extreme Value distributions were fitted to the maxima series using maximum likelihood to estimate the distribution parameters. The number of consecutive dry (wet) days and rainfall amount was considered during the cropping season between March and October. We modelled maxima observed at rainfall stations by means of max-stable processes, an extension of multivariate extreme value theory. Spatial extreme modelling was performed through pairwise likelihood inference by a madogram and enabled 20-year return levels to be simulated across regions based on geographic coordinates and altitude as covariates. Spatial return levels confirmed the exceptionality of 2016 and 2018 as extreme wet and dry years with high return periods. The 2018 drought resulted in yield reductions of 74% for maize and 42% for potato, while the 2016 rainfall induced yield losses of up to 39% for winter cereals, 45% for maize and 53% for potato. The max-stable models provided a good fit to the joint behaviour of extremes in the number of consecutive dry (wet) days during the growing season as an indication for crop stress. The method allows for spatio-temporal characterisation of generalised extreme value distributions across regions, and provides a tool for weather based insurances and risk management.