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<p>Estimating low flows is essential for water-resources management, especially in semi-arid regions with sparse hydrometric monitoring and high drought vulnerability. This study advances low-flow regionalization in a Brazilian semi-arid basin by integrating spatial climate data, precipitation and actual evapotranspiration from TerraClimate, into regional regression models. Of fourteen compiled gauges, ten met data-quality criteria and were used for calibration and leave-one-station-out cross-validation (LOSO-CV). The streamflow-equivalent water balance computed from TerraClimate fields (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\:{\text{WBeq}}_{\text{TC\_TC}}\)</EquationSource> </InlineEquation>) showed the best univariate predictive performance, outperforming models based on drainage area and on precipitation alone. Spatialization applied a WBeq &gt; 0 domain-of-applicability mask and an empirical envelope check based on the observed station range. Uncertainty was quantified by residual bootstrap in log space, and scenario perturbations (<i>P</i> - 10%, AET + 10%) indicated strong sensitivity of low-flow estimates in the driest reaches. Overall, the results show that freely available gridded climate datasets can improve low-flow regionalization in data-scarce basins and support water management in semi-arid environments.</p>

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Low-flow regionalization in a data-scarce Brazilian semi-arid basin using gridded climate data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月25日

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  • Arthur Kolling Neto,
  • Rayssa Balieiro Ribeiro

摘要

Estimating low flows is essential for water-resources management, especially in semi-arid regions with sparse hydrometric monitoring and high drought vulnerability. This study advances low-flow regionalization in a Brazilian semi-arid basin by integrating spatial climate data, precipitation and actual evapotranspiration from TerraClimate, into regional regression models. Of fourteen compiled gauges, ten met data-quality criteria and were used for calibration and leave-one-station-out cross-validation (LOSO-CV). The streamflow-equivalent water balance computed from TerraClimate fields ( \(\:{\text{WBeq}}_{\text{TC\_TC}}\) ) showed the best univariate predictive performance, outperforming models based on drainage area and on precipitation alone. Spatialization applied a WBeq > 0 domain-of-applicability mask and an empirical envelope check based on the observed station range. Uncertainty was quantified by residual bootstrap in log space, and scenario perturbations (P - 10%, AET + 10%) indicated strong sensitivity of low-flow estimates in the driest reaches. Overall, the results show that freely available gridded climate datasets can improve low-flow regionalization in data-scarce basins and support water management in semi-arid environments.