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<p>Improving water management in semi-arid regions is of great value to both the public and private sectors, as it could lead to increased investment in these areas. The states that encompass such regions must work together to enhance water management. Proper management of water resources relies on analyzing water data, even though this data is notalways available. Fortunately, several studies have been conducted to characterize regions with missing data by identifying local similarities (regionalization). This article aims to promote data regionalization in the Brazilian semi-arid region, i.e. to transpose information from similar regions with more data automatically using computational resources. The methods used included hydrological signatures from all fluviometric stations within the studied basin and upstream of the reservoir, combined with the k-means clustering algorithm to identify homogeneous regions. We selected hydrological signatures that capture the intermittent behavior of rivers in the semi-arid region. The results suggested a non-clear correlation among the variables, except for <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="40899_2025_1196_Article_IEq1.gif" Format="GIF" Height="17" Rendition="HTML" Resolution="72" Type="Linedraw" Width="28" /> </InlineMediaObject> <EquationSource Format="TEX">\(Q_{95}\)</EquationSource> </InlineEquation> and <i>lowfr</i>. The study catchment exhibited two regions (two clusters) with similar hydrological characteristics and a DB Score of 0.71.</p>

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Assessing hydrologic similarity in the semi-arid region: a case study in Piranhas-Açu basin

  • 期刊论文

  • 发表日期：

    2025年2月21日

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  • Pedro Lucas Bernardo Costa,
  • Yan Ranny Machado Gomes,
  • Christopher Freire Souza

摘要

Improving water management in semi-arid regions is of great value to both the public and private sectors, as it could lead to increased investment in these areas. The states that encompass such regions must work together to enhance water management. Proper management of water resources relies on analyzing water data, even though this data is notalways available. Fortunately, several studies have been conducted to characterize regions with missing data by identifying local similarities (regionalization). This article aims to promote data regionalization in the Brazilian semi-arid region, i.e. to transpose information from similar regions with more data automatically using computational resources. The methods used included hydrological signatures from all fluviometric stations within the studied basin and upstream of the reservoir, combined with the k-means clustering algorithm to identify homogeneous regions. We selected hydrological signatures that capture the intermittent behavior of rivers in the semi-arid region. The results suggested a non-clear correlation among the variables, except for \(Q_{95}\) and lowfr. The study catchment exhibited two regions (two clusters) with similar hydrological characteristics and a DB Score of 0.71.