Regionalization of reference streamflows for the Araguaia River basin in Brazil

Abstract

Hydraulic projects and water management require reliable hydrological data. The Araguaia-Tocantins River basin, in addition to agricultural use, has great potential for hydroelectric exploitation. However, the streamflow monitoring network in the Araguaia River basin is composed of only a few stations, resulting in a lack of hydrological data. The regionalization of the reference streamflows is a technique that can help circumvent this lack of data, enabling the estimation of streamflows from easily obtainable explanatory variables. In this context, the objective of this study was to develop regional functions for the maximum streamflow (Qmax) applicable to different Return Periods (RP), the long-term mean streamflow (Qmlt) and the 95% streamflow permanence (Q95) of the upper and middle Araguaia River sub-basins. The dimensionless streamflow methodology was adopted with the drainage area as an explanatory variable. The tested regressive models were the linear, potential and quotient models. Leave-one-out cross-validation was used to assess the quality of the regional models. Ten statistical distributions of 2 to 5 parameters were used. (i) Satisfactory results were obtained for all reference streamflows. (ii) The cross-validation technique proved to be essential for the selection of the most robust model. (iii) The quotient model was shown to be superior to the potential linear model in most cases.

Hydrological regionalization of streamflows for the Tocantins River Basin in Brazilian Cerrado biome

“…However, considering that the goal is to define a regional function that is capable of estimating the Qmax streamflow for different RPs, and that the GEV PDF was the only one that had a good fit for all Qmax series within the homogeneous regions 1 and 2, the GEV PDF was adopted here. Morais et al (2020), in a regionalization study for the Araguaia River Basin, Brazil, also identified the GEV PDF as the only one that had a good fit for all Qmax series. Kumar et al (2003), when evaluating 12 PDFs in a regionalization study of Qmax for Middle Ganga Plains Subzone 1 (f) of India, identified the GEV PDF as the most robust.…”

“…When regionalizing Qmean_max in a case study on the island of Sicily, Italy, Noto and La Loggia (2009) obtained a model with R 2 coefficient of 0.77, considering the drainage area as the only explanatory variable. When regionalizing Qmean_max for the Araguaia River Basin, Brazil, Morais et al (2020) obtained power mathematical models with R 2 coefficients ranging from 0.87 to 0.9 and confidence index values (c) > 0.85, considering the drainage area as the only explanatory variable. When regionalizing Qmean_max for the state of Rio Grande do Sul, Brazil, Cassalho et al (2018) obtained models with R 2 coefficients ranging from 0.57 to 0.96, also considering the drainage area as the only explanatory variable.…”

Hydrological Regionalization of the Annual Maximum Streamflows of the Upper and Middle Paraopeba River – Mg Using the Index-Flood Technique

“…GEV distribution application in the seven analyzed stations allowed obtaining the dimensionless quantiles for the respective return periods considered for different engineering applications. Morais et al (2020) analyzed the efficiency of 10 probabilistic models in a regionalization study in the Araguaia River basin and identified GEV as the only one that had a good fit for the maximum streamflow series. This robustness was also observed by Cassalho et al (2018) when evaluating six probability distributions in the Mirim-São Gonçalo basin in Rio Grande do Sul and Rodrigues et al (2021), who regionalized reference streamflows in the Cerrado biome in Tocantins.…”

Avaliação de metodologias de transferência de vazões de referência em bacias hidrográficas: sua aplicação na bacia do rio Japaratuba - Sergipe - Brasil

“…As vazões mínimas como Q7,10 e as vazões de permanência Q90% e Q95% são, geralmente, utilizadas como vazões de referência. O Brasil e seus estados federais definem seus próprios percentuais do fluxo de referência como o fluxo máximo para usos consultivos (Gomes e Fernandes, 2017;Morais et al, 2020). Por exemplo, no Estado de Sergipe utiliza-se uma vazão de referência de 90% (Q90) ao longo de um ano (SERGIPE, 2015).…”

“…) eLopes et al (2017) eMorais et al (2020), a área de drenagem e o comprimento do rio se caracterizaram como as variáveis mais expressivas para a representação nas funções regionalizadas. Para vazões máximas, os valores foram sempre superestimados, ou seja, as estimativas superaram os valores reais obtidos no posto Fazenda Cajueiro.…”