Reference evapotranspiration estimation without local climatic data

The Penman–Monteith equation for reference evapotranspiration (ETo) estimation cannot be applied in many situations, because climatic records are totally or partially not available or reliable. In these cases, empirical equations that rely on few climatic variables are necessary. Nevertheless, the uncertainty associated with empirical model estimations is often high. Thus, the improvement of methods relying on few climatic inputs as well as the development of emergency estimation tools that demand no local climatic records turns into a task of great relevance. The present study describes different approaches based on multiple linear regression, simple regression and artificial neural networks (ANNs) to deal with ETo estimation exclusively from exogenous records from secondary stations. This cross-station approach is based on a continental characterization of the study region, which enables the selection and hierarchization of the most suitable ancillary data supplier stations. This procedure is compared with different traditional and cross-station approaches, including methodologies that also consider local temperature inputs. The proposed methods are also evaluated as gap infilling procedures and compared with a simple methodology, the window averaging. The artificial neural network and the multiple linear regression approaches present very similar performance accuracies, considerably higher than simple regression and traditional temperature-based approaches. The proposed input combinations allow similar performance accuracies as ANN models relying on exogenous ETo records and local temperature measurements. The cross-station multiple linear regression procedure is recommended due to its higher simplicity.