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Diversion hydroelectric facilities are proliferating worldwide, yet how they alter water quality has seldom been assessed. This study evaluates effects of a nearly 'run-of-river' diversion hydropower dam (210 MW capacity) on water quality in the Correntes River, a tributary to the Pantanal floodplain of Brazil. Water quality was analyzed from eight locations before and after reservoir construction. Relative to an upstream reference site, the reservoir significantly decreased turbidity (mean, 38%) and concentrations of total solids (23%), total phosphorus (28%), and nitrate (14%). Most changes occurred in the upstream segment of the reservoir (except for turbidity). Only turbidity and nitrate were affected by reservoir operator control of the hydraulic retention time, which ranged from 7.3 to 21.2 days. Overall, the most consequential changes in downstream water quality may be sediment and associated phosphorus retention, which over the long term could affect channel geomorphology and reduce floodplain biological productivity. [ABSTRACT FROM AUTHOR]

Abstract: Since 1999, the Manso hydropower plant has been regulating the Cuiabá River, which is one of the two main affluents of the northern Pantanal of Mato Grosso, responsible for the flooding of at least 20,000km2 of the world''s greatest floodplain. Since the initiation of regular reservoir operation in 2002, discharge at the beginning of the wet season (November–December) has been reduced by about 20%. Current power plant operation increases dry-season discharges from about 100m3/s to 150m3/s, resulting in water levels approximately 1m above those recorded before regulation. Rainy season runoff higher than 400m3/s is retained in about 6 weeks and the period of drying up is anticipated. Hydrograph analysis in conjunction with a planialtimetric field survey determined that discharge of 240m3/s is the minimum required to initiate flooding in the Pantanal in the northern Cuiabá River floodplain, while runoff of 355m3/s is necessary to guarantee consistent flooding of the same. Constrained by positive linear trends in precipitation and discharge, which were detected through a time series analysis of a 68-year period, two reoperation alternatives are proposed, which would not reduce mass balance of power generation, but can guarantee river overflow during the months November through May, minimizing the impact on ecological functions of the floodplain. [Copyright &y& Elsevier]

Summary: The relations between hydrology, geomorphology and vegetation provide the basis for understanding the ecological processes in the floodplains of rivers. In this paper, long-term (1969–2009) local flood characteristics (magnitude, duration, frequency, return period) are quantified for the floodplain of the Cuiabá River (Pantanal wetland in Brazil), using a predictive model based on artificial neural networks (ANNs) with input given by the historic river-level record. Morphological features were then described and their relation to vegetation analyzed and used to reconstruct floods. In addition to the river-level data used to train and validate a three-layer ANN, data were also used from 11 floodplain gauges across a 12km transect lateral to the river, which were monitored during the annual floods of 2004–2007. The resulting neural network gave satisfactory estimates of water depth over the floodplain, with an absolute error of 0.09m not exceeded in 95% of occurrences. The ANN showed that in almost all years, the level reached by the river was high enough to flood the profile completely: the exceptions were 1971 and 2001 when only 50% and 58% was flooded, to mean depths of 0.34 and 0.48m, respectively. The highest flood was that of 1995 when the floodplain was flooded to a mean depth of 2.56m. The median flood event (return period 2years) produced a mean flood depth 1.80m and lasted 119days. Among the most important morphological features is the presence of palaeo-channels which provide hydrological connectivity between the river and the floodplain. The distribution of phytophysiognomic units is significantly influenced by local geomorphology, which determines spatial variation in the magnitude, duration, and frequency of flooding. [ABSTRACT FROM AUTHOR]