1. The Amazon River basin contains a vast diversity of lotic habitats and accompanying hydrological regimes. Further understanding the spatial distribution of flow regimes across the Amazon can be useful for recognizing riverine ecohydrological processes and informing river management and conservation, especially in areas with limited or inconsistent streamflow monitoring.
2. This study compares four inductive approaches for classifying streamflow regimes across the Amazon using an unprecedented compilation of streamflow records from Bolivia, Brazil, Colombia, Ecuador, and Peru.
3. Inductive classification schemes use attributes of streamflow data to categorize river reaches into similar classes, which then may be generalized to understand streamflow behaviour at the basin scale. In this study, classification was accomplished through hierarchical clustering of 67 flow metrics calculated using indicators of hydrologic alteration (IHA) and daily streamflow data from median annual hydrographs (MAHs) for 404 stations (representing >7,000 station‐years) across five Amazonian countries.
4. Classification was performed using both flow magnitude‐inclusive and flow magnitude‐independent datasets. For flow magnitude‐independent methods, optimal solutions included six or seven primary hydrological classes for IHA and MAH datasets; for approaches that retained magnitude, variance was sufficiently large to prevent convergence to a specific number of classes.
5. Across methods, class membership was strongly associated with the timing, frequency, and rate of change of flow, and spatially coherent clusters were associated with seasonal, elevational, and stream‐order gradients. These results highlight the diversity of flow regimes across the Amazon and provide a framework for studying relationships between hydrological regimes and ecological responses in the context of changing climate, land use, and human‐induced hydrological alteration.
6. The methodology applied provides a data‐driven approach for classifying flow regimes based on observed data. When coupled with ecological knowledge and expertise, these classifications can be used to develop ecohydrologically informed and management‐relevant conservation practices.

中文翻译：

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分类亚马逊盆地的流态

1. 亚马逊河流域包含大量多样的沼泽生境和伴随的水文状况。进一步了解整个亚马逊河水流态势的空间分布，对于识别河流生态水文过程和告知河流管理和养护，特别是在流量监测有限或不一致的地区，可能很有用。
2. 这项研究使用前所未有的来自玻利维亚，巴西，哥伦比亚，厄瓜多尔和秘鲁的水流记录汇编，比较了四种归纳方法对整个亚马逊河水流状况进行分类。
3. 归纳分类方案使用流量数据的属性将河段分类为相似的类别，然后可以将其推广以了解流域尺度上的流量行为。在这项研究中，通过对67个流量指标进行分层聚类来完成分类，这些指标使用水文变化指标（IHA）和来自五个亚马逊国家404个站点的中位年水位图（MAHs）的每日流量数据（代表> 7,000个站点年）计算得出。
4. 使用包含流量幅度和独立于流量幅度的数据集进行分类。对于与流量无关的方法，最佳解决方案包括针对IHA和MAH数据集的六个或七个主要水文分类。对于保留幅度的方法，方差足够大以防止收敛到特定数量的类别。
5. 在所有方法中，班级成员资格与流动的时间，频率和变化率密切相关，而空间上连贯的类与季节，海拔和河流次序梯度相关。这些结果凸显了整个亚马逊河的水流状况的多样性，并为研究气候变化，土地利用和人为引起的水文变化的背景下水文状况与生态响应之间的关系提供了框架。
6. 所采用的方法论提供了一种基于数据的方法，可以根据观察到的数据对流态进行分类。结合生态知识和专业知识，这些分类可用于制定生态水文知识和与管理相关的保护措施。