## 5 Discussion
Moreover, the employed dataset and forecasting performance metrics were selected according to the previous work of [PERSON] et al. [19], which focused on streamflow forecasting with ML models in the same Amazon sub-region analyzed in the present study. Furthermore, the same forecasting metrics were even leveraged by [PERSON] et al. [20], which even focused on the Amazon basin. In particular, the first group of AutoML models showed, for each employed time-lag, median forecasting performances better of an order of magnitude with respect to the ones reported in [PERSON] et al. [19], in terms of MAE and RMSE. Comparable performances were reached in terms of BIAS, NSEI, and KGEI. It must be noted that even regarding the second group of AutoML models, TPOT and MLBox reached superior median forecasting performances regarding MAE and RMSE with respect to [PERSON] et al. [19] for each employed time-lag. Superior median test performances were even obtained with respect to [PERSON] et al. [30], which focused on the same sub-region of the present study in terms of the computed NSEI and Bias metrics. Comparable forecasting performances were obtained with respect to [PERSON] et al. [20], which computed predictions within another sub-region of the Amazon basin relying on the PDIR-Now product and ANNs. However, in the latter case, it must be noted that in contrast with [PERSON] et al. [19] and [PERSON] et al. [30], the employed dataset was based on an Amazon sub-region different with respect to the one analyzed by the present study, since the authors focused on data related to the Branco River basin. As a result, it is difficult to compare the ML model performances reported by [PERSON] et al. [20] with the ones of the present research. Last, but not least, it must be noted that the authors developed an ANN-based forecasting model with a forecast horizon only limited to a 1 day time-lag. According to the results shown in Figure 7, the Kruskal-Wallis test performed in Section 4 indicated no statistically significant differences in the AutoML model's median performances with respect to all the employed features. In particular, the performance related to the remotely sensed estimated products (PERSIANN, PERSIAN-CSS, PERSIANN-CDR, and PDIR-Now) showed considerable promising results as input factors for predicting the streamflow with AutoML models. Indeed, such features based on remote sensing can augment the data used for training and testing ML models, particularly in wide areas like the Amazon basin, therefore enhancing the reliability of streamflow forecasts for the management of HPPs. The latter features could serve as a viable alternative to mitigate the uncertainties arising from the sparse distribution of rainfall gauge stations, thus leading to limited data available. Moreover, it must be noted that remotely sensed estimated products could have a superior representation of average rainfall in the considered basin. Indeed, it must be noted that the rainfall in the Amazon basin is primarily convective, characterized by high intensity, brief duration, and limited spatial distribution [70]. As a result, the average rainfall recorded by surface pluviometric stations could be skewed, especially in the considered area where hydrological data-monitoring networks are sparse. It must be finally noted that new fluviometric and pluviometric stations were installed in the upper Teles Pires River basin, but they registered only a few years of data for training and testing of ML models. Hence, we decided not to employ the available data related to the latter stations, as done by the previous recent study of [PERSON] et al. [19]. Satellite rainfall products have been successfully employed in the past literature as input data for hydrological models to forecast the streamflow [19, 20, 66, 71]. In addition to the products employed in the present study, other products were leveraged in the past literature, such as the Tropical Rainfall Measuring Mission (TRMM) products [72].