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<p>This work investigates the rainfall-runoff processes in the Muriaé River basin, located in the Southern Region of Brazil, using the TopModel hydrological model implemented in R. The model was calibrated over a 21-year period, utilizing historical data from the Cardoso Moreira fluviometric station and incorporating monthly averages of precipitation and potential evapotranspiration. The sensitivity analysis revealed that the parameter representing maximum water storage capacity in the soil’s surface layer (Srmax) was critical in controlling runoff dynamics, particularly during periods of high precipitation. The model successfully captured the flow behavior within defined uncertainty bounds, with most observed values falling within the 50% to 90% quantile range. The calibration yielded a Nash–Sutcliffe efficiency (NSE) of 0.82, demonstrating a strong agreement between simulated and observed flows. During the subsequent verification phase, the model achieved NSE of 0.79. Probabilistic validation via ROC curves confirmed exceptional flood-detection skill for the 90% quantile (AUC = 0.87). Despite potential uncertainties related to data sources, interpolation techniques, and the inherent variability of local measurements, the findings demonstrate TopModel’s operational readiness for early-warning systems in data-sparse tropical basins. This research contributes to the physical knowledge about the basin and the understanding of rainfall-runoff processes on a monthly scale, potentially supporting more effective water resource management strategies in the region.</p>

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Hydrometeorological analysis of extreme events in tropical climate basins: application of topmodel in the Muriaé River basin

  • 期刊论文

  • 发表日期：

    2025年9月8日

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  • Raphaella Barros Pereira da Silva,
  • Hugo Abi Karam

摘要

This work investigates the rainfall-runoff processes in the Muriaé River basin, located in the Southern Region of Brazil, using the TopModel hydrological model implemented in R. The model was calibrated over a 21-year period, utilizing historical data from the Cardoso Moreira fluviometric station and incorporating monthly averages of precipitation and potential evapotranspiration. The sensitivity analysis revealed that the parameter representing maximum water storage capacity in the soil’s surface layer (Srmax) was critical in controlling runoff dynamics, particularly during periods of high precipitation. The model successfully captured the flow behavior within defined uncertainty bounds, with most observed values falling within the 50% to 90% quantile range. The calibration yielded a Nash–Sutcliffe efficiency (NSE) of 0.82, demonstrating a strong agreement between simulated and observed flows. During the subsequent verification phase, the model achieved NSE of 0.79. Probabilistic validation via ROC curves confirmed exceptional flood-detection skill for the 90% quantile (AUC = 0.87). Despite potential uncertainties related to data sources, interpolation techniques, and the inherent variability of local measurements, the findings demonstrate TopModel’s operational readiness for early-warning systems in data-sparse tropical basins. This research contributes to the physical knowledge about the basin and the understanding of rainfall-runoff processes on a monthly scale, potentially supporting more effective water resource management strategies in the region.