A Bayesian modelling approach to forecasting short-term reference crop evapotranspiration from GCM outputs

Abstract

Evapotranspiration is one of the most important climate variables for agricultural and environmental management. We investigate the characteristics of daily crop evapotranspiration (ETo) forecasts up to 16 days ahead derived from GCM outputs using the Penman-Monteith Equation. Furthermore, we propose a post-processing method to calibrate the raw ETo forecasts to observations and improve overall reliability and skill. The post-processing method comprises: (1) the Yeo-Johnson transformation to handle the non-normal distribution of daily ETo, (2) the bi-variate normal distribution to formulate the relationship between transformed raw forecasts and observations, and (3) the Schaake shuffle to establish realistic temporal behaviour in each ensemble member. For three distinct Australian case-study locations, ETo forecasts derived from raw ACCESS-S1 outputs are positively correlated with observations up to 16 days ahead. However, raw forecasts verify more poorly than naïve climatology forecasts because of significant bias and poor ensemble spreads. Post-processing the raw ACCESS-S1 ETo improves skill and reliability markedly. Skilful daily ETo forecasts are achieved in the first week, and the skill of cumulative ETo forecasts extends beyond two weeks into the future. The proposed ETo post-processing method could be applied to support operational, multi-week forecasting of short-term ETo in support of the day-to-day decisions of irrigators in Australia and elsewhere.