Resumen
Machine learning has been used in hydrological applications for decades, and recently, it was proven to be more efficient than sophisticated physically based modelling techniques. In addition, it has been used in hybrid frameworks that combine hydrological and machine learning models. The concept behind the latter is the use of machine learning as a filter that advances the performance of the hydrological model. In this study, we employed such a hybrid approach but with a different perspective and objective. Machine learning was used as a tool for analyzing the error of hydrological models in an effort to understand the source and the attributes of systematic modelling errors. Three hydrological models were applied to three different case studies. The results of these models were analyzed with a recurrent neural network and with the k-nearest neighbours algorithm. Most of the systematic errors were detected, but certain types of errors, including conditional systematic errors, passed unnoticed, leading to an overestimation of the confidence of some erroneously simulated values. This is an issue that needs to be considered when using machine learning as a filter in hybrid networks. The effect of conditional systematic errors can be reduced by naively combining the simulations (mean values) of two or more hydrological models. This simple technique reduces the magnitude of conditional systematic errors and makes them more discoverable to machine learning models.