Revista Brasileira de Ciência do Solo (Oct 2021)

Assessing sediment yield and streamflow with SWAT model in a small sub-basin of the Cantareira System

  • Lucas Machado Pontes,
  • Pedro Velloso Gomes Batista,
  • Bárbara Pereira Christofaro Silva,
  • Marcelo Ribeiro Viola,
  • Humberto Ribeiro da Rocha,
  • Marx Leandro Naves Silva

DOI
https://doi.org/10.36783/18069657rbcs20200140
Journal volume & issue
Vol. 45

Abstract

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ABSTRACT Hydro-sedimentological models might be useful tools for investigating the effectiveness of soil and water conservation practices. However, evaluating the usefulness of such models requires that predictions are tested against observational data and that uncertainty from model parameterization is addressed. Here we aimed to evaluate the capacity of the SWAT model to simulate monthly streamflow and sediment load in the Posses creek catchment (12 km2), Southeast Brazil. The SUFI-2 algorithm from SWAT-CUP was applied for calibration, testing, uncertainty, and sensitivity analysis. The model was calibrated and initially tested using discharge and sediment load data, which were measured at the catchment outlet. Additionally, we used soil loss measurements from erosion plots within the catchment as independent data for model evaluation. Average monthly streamflow simulations obtained satisfactory results, with Nash-Sutcliffe coefficient (NSE) values of 0.75 and 0.51 for the calibration and testing periods, respectively. Sediment load simulations also displayed satisfactory results for calibration (NSE = 0.65) and testing (NSE = 0.52). However, the comparison with independent plot data revealed that SWAT severely overestimated hillslope erosion rates and compensated it with high sediment channel deposition. Moreover, the model was not sensitive to the parameters used for calculating hillslope sediment yields. Therefore, it should be used with caution for evaluating the interactions between land use, soil erosion, and sediment delivery. We found that the commonly used outlet-based approach for model calibration and testing can lead to internal misrepresentations, and models can reproduce the right answer for the wrong reasons.

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