Classifying flow regimes of the Amazon basin. Issue 5 (14th April 2021)

• Record Type: Journal Article
• Authors: Siddiqui, Sharmin F.; Zapata‐Rios, Xavier; Torres‐Paguay, Sandra; Encalada, Andrea C.; Anderson, Elizabeth P.; Allaire, Mark; da Costa Doria, Carolina Rodrigues; Kaplan, David A.
• Other Names: Castello Leandro guestEditor.
• Is Part Of: Aquatic conservation. Volume 31:Issue 5(2021)
• Page Start: 1005
• Page End: 1028
• Publication Date: 2021-04-14
• Subjects: catchment management -- classification -- ecohydrology -- flow metrics -- hydrographs -- river -- streamflow -- tropical -- watershed; Aquatic ecology -- Periodicals; Conservation of natural resources -- Periodicals; Aquatic resources -- Periodicals; 333.95216
• DOI: 10.1002/aqc.3582
• Languages: English
• ISSNs: 1052-7613

Abstract

Abstract: 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. 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. 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. 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. 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. 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.