Streamflow regionalization of Q95: comparison of methods for the Taquari-Antas River Basin

  • Kássia Regina Bazzo Universidade Federal de Pelotas (UFPel), Pelotas, RS, Brasil Centro de Engenharias (CENG)
  • Hugo Alexandre Soares Guedes Universidade Federal de Pelotas (UFPel), Pelotas, RS, Brasil Centro de Engenharias (CENG)
  • Andréa Souza Castro Universidade Federal de Pelotas (UFPel), Pelotas, RS, Brasil Centro de Engenharias (CENG)
  • Tirzah Moreira Siqueira Universidade Federal de Pelotas (UFPel), Pelotas, RS, Brasil Centro de Engenharias (CENG)
  • Claudia Fernanda Almeida Teixeira-Gandra Universidade Federal de Pelotas (UFPel), Pelotas, RS, Brasil Centro de Engenharias (CENG)
Keywords: regression, siscorv, water resources

Abstract

The low density of the national hydrometeorological network has greatly limited the representation of the behavior of water resources. Thus, stream regionalization is an alternative to better exploit existing data, allowing the transfer of information from one location to another with similar hydrological behavior. The aim of this study was therefore to assess the estimates of the minimum Q95 flowrate obtained through the Traditional and Mass Conservation methods in the basins of the Taquari-Antas River, in state of Rio Grande do Sul, with the aim of assisting water-resource management and facilitating decision-making in relation to the process of granting water-use permits. As an independent variable for the mathematical regression, we used the drainage area, extracted from a MDEHC. The regionalization equations of the reference flow Q95 and the respective hydrologically homogeneous regions were obtained for the two methods tested, being the magnitude of the relative errors inherent in the methodologies employed. Considering the peculiarities of this work, both assessed methods have shown satisfactory results in obtaining models to estimate the Q95 flowrate; however, it should be noted that the applied techniques do not replace hydrological information obtained with a sufficiently dense hydrometric network. 


Published
23/08/2017
Section
Papers