## 5 Discussion
However, within the present research, we opted not to employ the TRMM product since it has been no longer in operation since 2015. Furthermore, it must be noted that sometimes, the process of updating remotely sensed databases is time-consuming, and it is carried out with several days of delay, rendering it impractical for short-term streamflow predictions. Indeed, the products selected for the present study (refer to Table 2) are mostly promptly updated, facilitating reliable predictions for HPPs, except for the PERSIANN-CDR, which was last updated in September 2020. Thus, the latter product may represent a secondary option when deciding to employ one of the AutoML models and feature sets developed in the present study. To facilitate the selection of which AutoML models and input feature setsresearchers could employ, it must be noted that recent studies compared the hydrological application and reliability of the employed satellite rainfall products: [PERSON] et al. [73] reported that PERSIANN-CDR and PDIR-Now tend to show better estimates if compared to other products, while the PERSIANN product tends to be less reliable in daily precipitation and runoff modeling. Similarly, [PERSON] et al. [74] found that PERSIANN-CDR and PDIR-Now tend to provide better rainfall estimates with respect to other products, particularly in terms of capturing extreme rainfall events and the spatial distribution of rainfall. Hence, even though the results found by the present study indicated no statistically significant differences in the AutoML models median performance with respect to all the employed input features, we suggest relying on AutoML models where input features are represented by the PDIR-Now product. In particular, not only the last product is currently updated constantly every 15-60 min (refer to Table 2), but with respect to the PERSIANN-CDR product, it is still currently updated and maintained. Moreover, according to the AutoML\({}_{\text{score}}^{\text{T}}\) performance evaluation reported in Table 5, both the two highest ranked combinations of employed AutoML models and features leveraged PDIR-Now as input feature, respectively, with the auto-sklearn and AutoKeras AutoML models. It must finally mentioned that the current study on daily streamflow forecasting using AutoML presents a few limitations: (1) the study relies heavily on the availability and accuracy of remote-sensing data, which, even if data are usually validated, can be subject to errors and inconsistencies, particularly in regions with dense forest cover like the Amazon. The sparse distribution of ground-based hydrometeorological stations in the Amazon basin further exacerbates this issue, as it limits the ability to validate and calibrate remote-sensing data effectively. Additionally, the study's focus on a specific sub-region of the Amazon basin means that the findings may not be generalizable to other areas with different hydrological and climatic conditions. The use of historical data for model training also poses a limitation, as it may not fully capture the impacts of recent land use changes and climate variability on streamflow patterns. Moreover, while the AutoML models employed in the study automate many aspects of the machine-learning pipeline, they still require significant computational resources and a certain, even if limited, level of expertise to set up and interpret the results. The present study also acknowledges that the transformation of rainfall into runoff is a complex, nonlinear process influenced by numerous factors, which may not be fully captured by the models used. Finally, the reliance on daily average rainfall and streamflow data may overlook important sub-daily variations and extreme events that could significantly impact streamflow forecasting accuracy. The reported limitations highlight the need for ongoing improvements in data collection, model development, and computational techniques to enhance the reliability and applicability of streamflow forecasts in the Amazon basin and similar regions.