Adequacy of methodologies for determining SCS / CN in a watershed with characteristics of the Pampa biome

  • Zandra Almeida da Cunha Centro de Desenvolvimento Tecnológico. Universidade Federal de Pelotas (UFPel), Rua Gomes Carneiro, n° 1, CEP: 96010-610, Pelotas, RS, Brazil.
  • Samuel Beskow Centro de Desenvolvimento Tecnológico. Universidade Federal de Pelotas (UFPel), Rua Gomes Carneiro, n° 1, CEP: 96010-610, Pelotas, RS, Brazil.
  • Maíra Martim de Moura Centro de Desenvolvimento Tecnológico. Universidade Federal de Pelotas (UFPel), Rua Gomes Carneiro, n° 1, CEP: 96010-610, Pelotas, RS, Brazil.
  • Tamara Leitzke Caldeira Beskow Centro de Desenvolvimento Tecnológico. Universidade Federal de Pelotas (UFPel), Rua Gomes Carneiro, n° 1, CEP: 96010-610, Pelotas, RS, Brazil.
  • Carlos Rogério de Mello Departamento de Recursos Hídricos. Universidade Federal de Lavras (UFLA), Aquenta Sol, s/n, CEP: 37200-900, Lavras, MG, Brazil.
Keywords: effective rainfall, extreme events, hydrological modeling.

Abstract

The Soil Conservation Service Curve Number Model is a conceptual model intended for estimating effective rainfall (ER). This model is grounded in a parameter – referred to as Curve Number (CN), which is determined from information on the characteristics of the watershed. The Standard Method (M1) for determining the CN is based on soil and land-use tables; however, some authors have proposed alternative methodologies for defining the CN value from monitored rainfall-runoff events, such as those described by Hawkins (1993) (M2), Soulis and Valiantzas (2012) (M3), and Soulis and Valiantzas (2013) (M4). The objective of this study was to evaluate the impact of using these methods for determination of the CN parameter on the estimation of ER, taking as reference forty rainfall-runoff events monitored between 2015 and 2018 in the Cadeia River Watershed, which has characteristics of the Pampa biome. The different methods assessed for definition of the CN parameter resulted in contrasting performances with respect to the estimation of ER for CRW, as the following findings: i) M1 gave ER values with little reliability, mainly due to the classification of antecedent moisture content classes; ii) M3 provided the best results in determining ER, followed by M2; and iii) the ER values estimated according to M4 differed from those observed, mainly for events with lower rainfall depths.


Published
02/07/2021
Section
Papers