Classifying flow regimes of the Amazon basin

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.

Amazonian run‐of‐river dam reservoir impacts underestimated: Evidence from a before–after control–impact study of freshwater turtle nesting areas

“…Distance to the hydropower dam was calculated along the river network using the riverdist package (Tyers, 2020) in R (R Core Team, 2020). Rainfall is an important determinant of annual and seasonal variation in Amazonian river flow rates (Melack & Coe, 2021; Siddiqui et al, 2021), as such elevated precipitation during the months preceding nesting could result in nesting areas remaining submerged (Eisemberg et al, 2016). The total and standard deviation (SD) of precipitation during each of the 3 months (July, August and September) before the peak of the nesting season in the six monitored years was calculated (Supporting Information S1).…”

“…The ecological structure and complexity of Amazonian freshwater ecosystems is a result of myriad factors operating across different temporal and spatial scales, including flood pulse (Junk & Wantzen, 2006; Melack & Coe, 2021) and the spatial–temporal heterogeneity of seasonal rainfall (Reis et al, 2019; Melack & Coe, 2021; Siddiqui et al, 2021). The structure, composition, functions and services of globally important Amazonian ecosystems are threatened owing to the expansion of hydropower developments.…”

Amazonian run-of-river dam reservoir impacts underestimated: Evidence from a Before-After Control-Impact study of freshwater turtle nesting areas

“…Distance to the hydropower dam was calculated along the river network using the riverdist package (Tyers, 2020). Rainfall is an important determinant of annual and seasonal variation in Amazonian river flow rates (Melack & Coe, 2021;Siddiqui et al, 2021) and it could be expected that elevated precipitation during the months preceding nesting could result in nesting-areas remaining submerged. The total and standard deviation of precipitation (PPT) during each of the three months (July, August and September) prior to the peak of the nesting season in the six monitored years was calculated (Supporting Information S1).…”

“…The ecological structure and complexity of Amazonian freshwater ecosystems is a result of myriad factors operating across different temporal and spatial scales, including flood pulse (Junk & Wantzen, 2006;Melack & Coe, 2021) and the spatial-temporal heterogeneity of seasonal rainfall (Melack & Coe, 2021;Reis et al, 2019;Siddiqui et al, 2021). But, diverse and globally important Amazonian ecosystem functions and services are threatened due to expansion of hydropower developments.…”

Hydrologic flow regimes in humid tropics river basin

“…The bacteriological contamination in the drinking water source and increased industrial and agricultural water use are causing stress in the quality of water available. The long-term changes in rainfall, overfishing, exotic species booming lotic ecosystems are hard to reverse (Sauque et al 2021;Siddiqui et al 2021) desalination isn't always feasible to obtain drinking water().…”