## 1 Introduction  
Reliable streamflow forecasting is crucial for several applications, such as reservoir management [1], predicting energy generation in hydroelectric installations [2], flood mitigation [3], designing hydraulic schemes [4], assessing the impact of human-induced changes on water accessibility [5], and estimating the occurrence of extreme climate phenomena [6]. The wide spectrum of the mentioned applications highlights the essential need for accurate streamflow predictions [7]. Consequently, there is an increasing demand for improved short-term and long-term streamflow predictions to enhance water-resource management practices [7, 8]. However, reliability in streamflow forecasts remains a significant challenge within the field of water-resource management. Indeed, the difficulty in accurate hydrological forecasting is primarily due to the dynamic relations and considerable spatial and temporal variability of the elements that regulate the hydrological cycle that convert rainfall into river flows [9]. Moreover, the transformation of rainfall into runoff is generally nonlinear [10], thus leading to additional complexities in its proper modeling. Precipitation is the primary source of water input into a river basin, directly influencing river flow through several hydrological processes [7, 9, 11]: when precipitation occurs, it can either infiltrate the soil, contributing to groundwater recharge or become surface runoff, which flows over the land and into rivers and streams. The amount and intensity of precipitation, along with the characteristics of the watershed, such as soil type, vegetation cover, and topography, determine the proportion of water that becomes runoff versus infiltration [7, 8]. During periods of heavy rainfall, the soil's infiltration capacity can be exceeded, leading to increased surface runoff and higher river flows. Conversely, during dry periods, reduced precipitation results in lower river flow as groundwater contributions diminish [5, 6]. Additionally, the temporal distribution of precipitation events, such as the occurrence of prolonged rainfall versus short, intense storms, plays a crucial role in shaping the river flow patterns [9]. Understanding the reported dynamics is essential for accurate streamflow forecasting and effective water-resource management, particularly in regions with limited hydrological data like the Amazon basin [7, 8, 9]. The transformation of rainfall into runoff has been modeled in multiple ways through physical [12], conceptual [13], and empirical approaches [14]. Physical and conceptual models provide a comprehensive mathematical formulation of the hydrological elements that govern the transformation of rainfall into streamflow [13, 12]. However, the application of the latter models requires comprehensive data about the considered basin, including climatic conditions, soil features, and characteristics of drainage channels. As a consequence, the need for such extensive hydrometeorological data can render physical and conceptual models impractical or result in subpar performance in predicting rainfall-runoff in watersheds with limited available data [7]. On the other hand, empirical models, often based on Machine Learning (ML), can identify correlations between input and output data of hydrological systems without taking into account the involved physical processes [15, 16, 17]. Indeed, ML models can forecast the hydrological components of interest by employing mathematical equations that only fit the available training data. Traditional ML-based hydrological models, for instance, based on \(k\)-Nearest Neighbors (\(k\)-NN), Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Networks (ANNs), and Deep Learning (DL) have proven to be effective in predicting the streamflow in basins with limited hydrometeorological data [16, 17]. Regarding ML models, according to [PERSON] et al.