## 1 Introduction
[18], it must be noted that within the field of hydrology, the latter models are particularly relevant when the focus is put on the model's reliability and/or simplicity rather than the ability to effectively represent the underlying hydrological, physical processes. For instance, high reliability in predictions is crucial for operational uses both in reservoirs and flood alert systems [1, 3]. Indeed, several recent research works, including the ones of [PERSON] et al. [19] and [PERSON] et al. [20], focused on the applicability of ML models to daily streamflow forecasting in the Amazon biomes and such authors underscored the remarkable performances of several traditional ML models in the context of Amazon basin, where limited hydrometeorological data were available. Incidentally, similar findings resulting in the successful application of ML-based forecasting models were obtained in other regions of Brazil with comparable hydrometeorological characteristics, e.g., in the Brazilian state of Pernambuco by [PERSON] et al. [21]. Furthermore, it was noted that the effectiveness of ML models in streamflow forecasting is not only due to their resilience in handling the scarcity of available hydrometeorological information but also in dealing with the frequent non-stationary nature of the considered data [19, 22]. Finally, [PERSON] et al. [19] showed that historical rainfall and streamflow data alone are mostly sufficient to exploit ML for streamflow forecasting. Even if the traditional ML models demonstrated their effectiveness in the context of streamflow forecasting, they often present several drawbacks to their effective applicability. In particular, traditional ML models typically require a deep understanding of each of the employed underlying ML algorithms and their adjustable parameters and hyperparameters for effective model selection and tuning [23, 24, 25]. Furthermore, the process of manually training, selecting the proper model, and tuning the respective hyperparameters is often time-consuming, in particular when dealing with ML algorithms allowing the possibility of adjusting a wide set of parameters and hyperparameters, each with multiple or even continuous degrees of freedom [24, 25, 26]. Last but not least, every step of the traditional ML pipeline required to obtain a fully working forecasting ML model, i.e., data preprocessing, feature extraction, repeated model training and testing, hyperparameter tuning, and final model selection must be done manually [23, 24, 25]. Automated Machine-Learning (AutoML) models were introduced to mitigate the above-mentioned issues, and they offer several advantages over the traditional ML pipeline [23, 24, 25]. First, AutoML models reduce the need for deep expertise in each of the employed ML algorithms, as they completely automate data preprocessing, feature extraction, repeated model training and testing, hyperparameter tuning, and final model selection by significantly simplifying the entire ML design process for researchers [23, 24, 25]. Furthermore, the provided automation does not only reduce design complexity but also drastically cuts down the time required for developing reliable ML models, as it can efficiently test a vast space of candidate optimal models and respective hyperparameters, something that would often be impractical to do manually [23, 24, 25, 26]. Finally, AutoML models streamline the entire ML pipeline--from data preprocessing to final model selection--ensuring that each step is optimally performed without or with minimal manual intervention [23, 24, 25]. As a final result, the design of ML forecasting models becomes a completely end-to-end design process, which, in the context of hydrology, could accelerate the development of reliable ML-based models for streamflow prediction, even in environments with limited hydrometeorological data. Indeed, recently, AutoML was applied in a few related studies that focused on streamflow forecasting with remarkable forecasting performance [27, 28, 29].