USING WEATHER GENERATORS IN CROP MODELLING

A stochastic weather generator is a numerical model that produces synthetic daily time series of climate variables statistically similar to the observed. Weather generators are used in combination with crop simulation models for various agricultural applications and the paper describes some examples. Weather generators are capable to produce long weather time series allowing an assessment of agricultural risk because the lengths of observed series are often insufficient for estimates of the probability of extreme events. Agricultural impact assessments, based on crop models, are now often made on high-resolution grids or at multiple sites across a region where observed weather records are not available; weather generators provide the means of extending the simulation of weather time-series to unobserved locations. Recently, weather generators have been used to assess the effects of climate change on crop production. Predicted changes in climatic variables obtained from Global Climate Models (GCMs) were applied to the weather generator parameters to generate high resolution climate change scenarios suitable for agricultural applications. Weather generators can be used in real-time simulations to predict crop yields within a season. Comparison of two weather generators, WGEN and LARS-WG, their limitations and possible implications for crop modelling are discussed.