Almagro, AndréMeira Neto, Antônio AlvesVergopolan, NoemiRoy, TirthankarTroch, Peter A.Oliveira, Paulo Tarso S.2024-11-042024-11-042024Almagro, A., Meira Neto, A. A., Vergopolan, N., Roy, T., Troch, P. A., & Oliveira, P. T. S. (2024). The Drivers of Hydrologic Behavior in Brazil: Insights From a Catchment Classification. Water Resources Research, 60(8), e2024WR037212. https://doi.org/10.1029/2024WR037212https://hdl.handle.net/1911/117984Despite hosting ∼16% of the global freshwater and almost 50% of water resources in South America, Brazilian catchment-scale relationships between drivers and streamflow are still poorly understood. Here, we used streamflow signatures and attributes of 735 catchments from the Catchment Attributes for Brazil data set to investigate the dominant hydrological processes for the catchments. We also assess how catchments group based on hydrologic behavior similarities and analyze which climatic/landscape attributes control the streamflow variability. To classify and group the catchments, we used the k-means method optimized by the Elbow approach, along with a Principal Component Analysis. Uncertainty on catchment grouping was checked by k-fold cross-validation. Then, we used a recursive feature elimination using the random forest technique to assess the most influential catchment attributes to the hydrological signatures. Our results revealed six similarity groups, which followed mainly an aridity gradient ranging from the wettest to the driest, but also seasonality. The climate is the primary driver of hydrological behavior for the water-limited groups, highlighting the influence and importance of the atmospheric demand in several Brazilian catchments. High soil storage capacity in energy-limited catchments associated with high precipitation led to high discharge all year due to the subsurface fluxes' contribution. Our findings may be useful to improve streamflow predictability and hydrological behavior identification by further understanding hydrological similarities and their signatures due to catchment landscape characteristics. Further, by employing an easily reproducible methodology and clear metrics to weigh uncertainty, our study provides a significant step toward establishing a catchment-scale common classification system.engExcept where otherwise noted, this work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives (CC BY-NC-ND) license. Permission to reuse, publish, or reproduce the work beyond the terms of the license or beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.Journal articleTheDriversHydrologicBehaviorhttps://doi.org/10.1029/2024WR037212