Abstract
Both water balance (WB) and rating curve (RC) are methods for estimating streamflow. The first is mostly used to estimate reservoir outflows, while the second is usually adopted in hydrometeorological network streamflow gauges. While WB uses hourly collected data, the RC estimates streamflow using current water level and extrapolation techniques. The objective of this study was to analyze variations in the reservoir’s hourly outflow at Queimado Hydroelectric Power Plant (HPP Queimado) and to propose a method to evaluate whether the estimate of the daily outflows, obtained by the WB method, is similar to the flow values obtained at a conventional station. The logistic regression (LR) model was used because it is a method that adopts binary, categorically dependent variables to identify the event of interest. The results showed that the values of streamflow, obtained from an average of two daily readings, were a good representation of the flows in the region. The LR was able to identify atypical data, especially in the rainy season. This means that data consistency analysis can be faster and safer, when adequately employed and considering the proposed conditions, contributing to both management policies and the management of water resources.
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Agência Nacional de Águas (ANA).
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The authors would like to thank the Department of Agricultural Engineering (DEA) and as the Universidade Federal de Viçosa, for supporting this research.
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The authors thank the Coordination of Higher Education Personnel Improvement (CAPES) – Finance Code: 001 and the National Council for Scientific and Technological Development (CNPq) for the scholarship grants.
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Tarcila Neves Generoso, Demetrius David da Silva, Ricardo Santos Silva Amorim, Lineu Neiva Rodrigues, Erli Pinto dos Santos participated in the creation, design, material preparation, data collection and analysis.
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Generoso, T.N., da Silva, D.D., Amorim, R.S.S. et al. Methodology for Estimating Streamflow by Water Balance and Rating Curve Methods Based on Logistic Regression. Water Resour Manage 36, 4389–4402 (2022). https://doi.org/10.1007/s11269-022-03259-1
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DOI: https://doi.org/10.1007/s11269-022-03259-1