Estimativa do escoamento superficial em diferentes níveis de dossel vegetativo e cobertura do solo

• Ano de defesa: 2014
• Autor(a) principal: Knies, Alberto Eduardo
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Santa Maria; BR; Engenharia Agrícola; UFSM; Programa de Pós-Graduação em Engenharia Agrícola
• Palavras-chave em Português: Palha; Declividade do terreno; Chuva simulada; Chuva efetiva; Método curva número; Equações multivariadas; Modelo SIMDualKc; Straw; Land slope; Simulated rainfall; Effective rain; Curve number method; Multiple linear regression; SIMDualKc Model; CNPQ::CIENCIAS AGRARIAS::ENGENHARIA AGRICOLA
• Link de acesso: http://repositorio.ufsm.br/handle/1/3612

The soil tillage systems modify its water balance and for the correct irrigation management is fundamental to determining the runoff and effective rainfall, which helps to maximize the use of rainwater and minimizes the use of supplemental irrigation. The objective of this study was to determine, model and estimate the runoff and the effective rainfall during the development cycle of the common black bean and maize in soil with and without straw on the surface, in different land slope and rainfall intensities simulated, using the field experiments, multivariate equations, the Curve Number Method (CN) and the SIMDualKc Model. Two experiments were conducted in the field with crops of black beans and maize, where different intensities of simulated rainfall (35, 70 and 105 mm h-1) were applied at different times of the crop cycle (soil cover of 0, 28, 63 and 100% by the canopy beans; 0, 30, 72 and 100% by canopy of maize) and distinct land slope (1, 5 and 10%) in soil without and with (5 Mg ha-1) of oat straw on the surface. The runoff values observed were compared with those estimated by the CN method, suggesting new values of CN to improve the estimate. From the set of data collected from the field analysis of multiple linear regression to estimate runoff and simulations with SIMDualKc model to estimate runoff and effective rainfall were performed. The start time of the runoff, constant runoff rate, total runoff and the percentage of runoff in relation to the volume of rain were little influenced by the crops of beans and maize. Reductions in runoff were provided by the straw on the soil surface within 45 and 48% for the crops beans and maize, respectively. The CN method for the bean crop underestimated runoff by up to 10% for the soil without straw on the surface, and overestimated by up to 17% for the soil with straw. For maize, the method overestimated the runoff by up 32.4% in soil with straw and 12% in soil without straw. To improve estimation the CN, new values are proposed for CN, considering the crop, the presence or absence of straw on soil surface and intensity rain. The use of multiple linear regression analyzes indicated that the volume of precipitation (R2=0.52) and soil cover by straw (R2=0.18) are the variables with the greatest influence on runoff. Four multiple equations were generated, and the equation 2, whose input parameters are the volume of rain and amount of litter on the soil surface, was presented the best estimate of the runoff of a data set than the one that gave its origin. The SIMDualKc Model requires adjustments to estimate runoff and effective rainfall during the crop cycle of beans and maize, so consider the benefits of straw on the soil surface in reducing runoff. Thus, the suggested value of CN (CN=75) was changed to 71 and 87 to the black bean crop, and 56 and 79 for the maize crop for the soil with and without straw on the surface, respectively.