Ancillary data supply strategies for improvement of temperature-based ETo ANN models

The development of new and more precise models for reference evapotranspiration (ET_o) estimation from minimum climatic data is mandatory, since the application of existing methods that provide acceptable results is limited to those places where large amounts of reliable climatic data are available. The performance quality of empirical equations and their local calibrations is to be questioned in a large variety of climatic contexts. As an alternative to traditional techniques, artificial neural networks (ANNs) are highly appropriate for the modelling of non-linear processes, which is the case of evapotranspiration. Nevertheless, temperature-based ANN models do not always provide accurate enough ET_o estimations and their performance depends highly on the specific relationships temperature-ET_o of the studied continental context. This paper describes the performance improvement of temperature-based ANN models through the consideration of exogenous ET_o records as ancillary inputs in different continental contexts of the autonomous Valencia region, on the Spanish Mediterranean coast. The influence on the model performance of the number of considered ancillary stations and the corresponding number of training patterns is also analysed. Finally, this performance is compared with existing empirical and ANN temperature-based models. The proposed models can be used with high accuracy not only for infilling purposes, but also for estimating ET_o outside the training station. Concerning models which demand scant climatic inputs, the proposed model provides performances with lower associated errors than the currently existing temperature-based models, which only consider local data. The local performance of the model presents 0.084 of average absolute relative error (AARE). The external performance of the model presents 0.1072 of AARE.