Climate and catchment influences on streamflows in Brazilian watersheds

South America holds vast freshwater reserves, contributing to its global prominence across various sectors. Understanding streamflows at different levels—minimum flows for ecosystem maintenance, mean flows for hydropower and navigation, and high flows associated with floods—is critical for ensuring societal and ecological resilience. These streamflows are influenced by changes in catchment characteristics and climate change, yet the relationship between climate and catchment drivers with streamflows, particularly in tropical regions, remains poorly understood. Recent advances in explainable artificial intelligence (XAI) offer promising avenues for addressing these gaps by linking observational data to potential causal inference. Here, we investigated the climatic and catchment drivers influencing five streamflow types (Q₁, Q₅, Q_mean, Q₉₅ and Q₉₉) across 735 Brazilian watersheds using XAI approaches. Random Forest models were trained with 16 most important attributes for each streamflow type. SHapley Additive exPlanations were applied to explain the directionality and magnitude of each driver's impact, while inflection points were delineated to capture critical thresholds for streamflow changes. Results showed the aridity index (potential evapotranspiration/precipitation) as the most impactful predictor globally, likely due to its role in long-term water balance. However, for Q₉₉, soil sand content emerges as the dominant factor, showing that catchment characteristics rival climatic factors in importance for rare streamflow events. The analysis highlighted critical thresholds, such as reductions in streamflow when the aridity index exceeds 1.30 and potential declines in streamflow for soil carbon content below 30%, likely due to reduced water infiltration and storage capacity. Similarly, forest cover below 40% potentially increases streamflows, possibly due to reduced evapotranspiration and water retention in soils. Regional differences were also observed: in central Brazil, land cover and land use, and topography potential response for decreased the low streamflows, while in the south and northeast, climatic factors such as aridity and precipitation seasonality control the potential decreases. Rare high events (Q₉₉) in the south this watershed scale attributes height above the nearest, permeability and porosity potential increases the magnitude of events. These findings highlight that, while climatic attributes dominate streamflow relationships at a national scale, regional variations underscore the importance of catchment characteristics. This study demonstrates how data-driven models have the potential to capture the complex interplay between climatic and catchment attributes, linking these factors to streamflow dynamics.