Tesis Doctoral/PHD

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Browsing Tesis Doctoral/PHD by Subject "Carga de sedimento"

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    Sediment transport in steep Andean rivers with coarse bed material
    (Universidad de Cuenca, 2022-05-05) Carrillo Serrano, Verónica Margarita; Timbe Castro, Luis Manuel; Cisneros Espinoza, Felipe Eduardo
    To improve the understanding of bedload sediment transport with the aim of proposing estimations of its transport rates with greater accuracy, a comprehensive analysis was developed. To investigate the former process, three rivers typical of the southern Ecuadorian mountainous region with longitudinal slopes ranging from 0.8% to 10% were characterized based on the hydraulic geometry (HG) theory. Dimensional and dimensionless downstream HG relations were obtained for top width, average flow depth, average flow velocity, and channel bed slope. The correlation coefficients (𝑅 2 ) of these HG relations indicated that the dimensionless equations adequately represented the observed data of each parameter with a single relation for all three rivers except for slope. Slope relation behavior indicates that slope is not dependent on discharge. Therefore, the non-dimensional HG relations were reformulated using dimensionless discharge and bed slope as independent variables. These new relations showed improved performance (high 𝑅 2 ) and demonstrated the role of slope in determining channel hydraulic and geometric variables. This might also indicate that slope would play an important role in the determination of bedload transport rates. A physical model of a characteristic high-gradient river was implemented to study bedload sediment transport. Several scenarios of channel slope, discharge, and bed material configuration were tested. The most relevant variables from a set of independent variables considered were selected based on a stepwise regression. Additionally, sediment characteristic diameter was also considered as an independent variable replicating the analysis for four different characteristic diameters (𝑑16, 𝑑50, 𝑑84, and 𝑑90). Linear and potential models were obtained with each characteristic diameter. Potential models showed more consistent behavior throughout the entire range of the data considered. Linear models performed better for medium to high transport rates. However, higher dispersion in the estimations was obtained for the lower transport rates. Better performance was observed for the models corresponding to the 𝑑50 and 𝑑84 characteristic diameters. For a validation analysis, higher prediction capacity was obtained for potential models (for 𝑑50 and 𝑑84) with 96% of the data falling within ½ order of the magnitude bands. Based on 𝑅 2 , the 𝑑84 potential model can be selected as the one with the better overall behavior for the laboratory-based models. Using field and laboratory data and considering additional independent variables that could describe better bedload sediment transport, a stepwise procedure was applied to determine the more relevant variables to estimate bedload transport rates. Laboratory data was used to validate field data behavior. Through the use of a State Dependent Parameter (SDP) technique, the nonlinear relations between each of the relevant variables identified and the bedload transport rate were established. From these relations, nonlinear models were built to estimate bedload transport rates. Based on the relevant variables, potential (linear through logarithmic transformation) models were also obtained for comparison. Similar performances were reported for potential and nonlinear models for the data used to calibrate the models. For validation of the models with an independent set of data, the nonlinear 𝑑50 showed a higher level of accuracy. Considering the high level of uncertainty in the estimations of bedload transport rates the 𝑑84 nonlinear model can be also considered as acceptable. For the estimation of bedload transport rates in steep Andean rivers with coarse bed material, the 𝑑50 and 𝑑84 nonlinear models can be applied with an acceptable level of confidence for parameters with values within the range of those corresponding to the data used to build the models. For extrapolation, the application of this model must be subject to a verification process.