## Materials and Methods
### AutoML Models Description and Experimental Settings
The spatial resolution, selected data period, update frequency, and number of computed samples for each considered product are reported. employed ML models (optimized for model performance), and the second one includes just the best-performing ML model for each algorithm class (optimized for production use). The output of H2O AutoML is finally an object containing a \"leaderboard\" that ranks all the trained ML models based on a custom user-defined performance metric, making it straightforward to identify the optimal performing ML models. AutoKeras is an AutoML model specifically designed for DL architectures, built over the Keras Python library [57]. AutoKeras relies on the principle of network morphism, which allows the functionality of a DL network to be preserved while its underlying architecture is altered. In particular, AutoKeras leverages Bayesian optimization to steer network morphism in the search for the optimal DL architecture for the given problem and dataset. To effectively surf the wide resulting search space, the developers of AutoKeras devised a specific neural network kernel and a tree-structured optimization algorithm. MLBox is an AutoML Python library that provides a suite of features aimed at streamlining the ML pipeline [58]. The latter ones include fast data reading and distributed data preprocessing, cleaning, and formatting which simplifies the often complex and time-consuming task of preparing the data to be provided as input to ML models. Furthermore, MLBox is also capable of highly robust feature selection, with the aim of ensuring data quality and reliability of training data. Another key feature is the MLBox capability of performing accurate hyperparameter optimization processes in high-dimensional spaces, thus allowing the fine-tuning of ML models to achieve optimal predicting performance. Furthermore, MLBox includes the latest state-of-the-art predictive ML models for both classification and regression tasks, such as RF, Bagging, and Light GBM. Finally, MLBox provides predictions even endowed with ML model interpretations, thus aiding both in the understanding and transparency of the trained ML models. The five AutoML models employed in this study exhibit distinct structural characteristics and operational methodologies. Indeed, auto-sklearn leverages Bayesian optimization to surf a vast hypothesis space, integrating multiple preprocessing and feature selection techniques. TPOT utilizes genetic programming to evolve machine-learning pipelines, optimizing them through evolutionary algorithms. H2O AutoML combines various machine-learning algorithms, including Gradient Boosting Machines, Deep Neural Networks, and Generalized Linear Models, and employs ensemble learning to enhance predictive performance. AutoKeras focuses on deep learning, utilizing network morphism and Bayesian optimization to identify optimal neural network architectures. MLBox emphasizes ease of use with robust feature selection and hyperparameter optimization, primarily leveraging tree-based models like LightGBM and Random Forest. Such structural differences influence the adaptability, computational efficiency, and suitability of the selected AutoML models for different types of datasets and prediction tasks. Table 3 reports the features employed to train the selected AutoML models and the resulting predicted outputs. Identical datasets were set across all the AutoML models, and the unique induced variation was in the time-lag of the predicted streamflow, extending it up to 3 days, to assess the forecasting performance of the models. For each feature set, the AutoML models were trained, validated, and tested using daily surface rainfall and streamflow data collected from the time periods specified in Table 3. In particular, average streamflow and rainfall data were used jointly as input features where the data were available for both the employed features. The available data for each feature was split to include the last 30% of the data in the final test set, ensuring a final assessment of AutoML models on unseen data.