Despite 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.

中文翻译：

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巴西水文行为的驱动因素：流域分类的见解


尽管巴西拥有约 16% 的全球淡水和近 50% 的水资源，但巴西流域规模的驱动力与径流之间的关系仍然知之甚少。在这里，我们使用巴西流域属性数据集中 735 个流域的水流特征和属性来调查流域的主要水文过程。我们还评估流域如何根据水文行为相似性进行分组，并分析哪些气候/景观属性控制水流变化。为了对流域进行分类和分组，我们使用了通过 Elbow 方法优化的k均值方法以及主成分分析。通过k倍交叉验证检查流域分组的不确定性。然后，我们使用随机森林技术进行递归特征消除来评估对水文特征最有影响力的流域属性。我们的结果显示了六个相似组，它们主要遵循从最潮湿到最干燥的干旱梯度，但也有季节性。气候是水资源有限群体水文行为的主要驱动因素，凸显了巴西几个流域大气需求的影响和重要性。由于地下通量的贡献，与高降水相关的能源有限流域的高土壤存储容量导致全年高流量。我们的研究结果可能有助于通过进一步了解流域景观特征造成的水文相似性及其特征来提高水流可预测性和水文行为识别。 此外，通过采用易于重复的方法和明确的指标来权衡不确定性，我们的研究为建立流域规模的通用分类系统迈出了重要一步。