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Different Infiltration Methods for Swat Model Seasonal Calibration of Flow and Sediment Production

Author

Listed:
  • Priscila Pacheco Mariani

    (Federal University of Rio Grande do Sul)

  • Nilza Maria Reis Castro

    (Federal University of Rio Grande do Sul)

  • Vanessa Sari

    (Federal University of Santa Maria)

  • Taís Carine Schmitt

    (Federal University of Santa Maria)

  • Olavo Correa Pedrollo

    (Federal University of Rio Grande do Sul)

Abstract

Hydrosedimentological models make it possible to better understand the dynamics of water and sediment production in watersheds when properly calibrated. The objective of this study was to analyze the effects of the curve number (CN) and Green and Ampt (GA) methods and of seasonal calibration of the Soil and Water Assessment Tool (SWAT) model for estimating flow and sediment production in an agricultural basin. In this research, we presented an original application with the hourly suspended sediment concentration (SSC) generated by artificial neural networks (ANNs) for use in SWAT model calibration. This method was applied in the Taboão basin (77.5 km2), with data from 2008 to 2018. The best Nash–Sutcliffe (NS) coefficient values were obtained using the combination of wet years for calibration and the GA method for both daily flow (NScalibration: 0.74; and NSvalidation: 0.68) and daily sediment production (NScalibration: 0.83; and NSvalidation: 0.77). The CN method did not result in satisfactory values during daily flow calibration (NScalibration 0.39). The results showed that it is possible to employ the SWAT model for hydrosedimentological prediction in the Taboão basin, with a favorable efficiency, using the GA method and calibration with data for wet periods.

Suggested Citation

  • Priscila Pacheco Mariani & Nilza Maria Reis Castro & Vanessa Sari & Taís Carine Schmitt & Olavo Correa Pedrollo, 2024. "Different Infiltration Methods for Swat Model Seasonal Calibration of Flow and Sediment Production," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(1), pages 303-322, January.
    • Handle: RePEc:spr:waterr:v:38:y:2024:i:1:d:10.1007_s11269-023-03671-1
    DOI: 10.1007/s11269-023-03671-1
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    References listed on IDEAS

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    1. Laís Coelho Teixeira & Priscila Pacheco Mariani & Olavo Correa Pedrollo & Nilza Maria Castro & Vanessa Sari, 2020. "Artificial Neural Network and Fuzzy Inference System Models for Forecasting Suspended Sediment and Turbidity in Basins at Different Scales," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(11), pages 3709-3723, September.
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