Browsing by Author "Cedillo Galarza, Juan Sebastián"

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Comparative assessment of a mountain river flow resistance – 1D-: sensitivity and prediction using data-based approaches
(Universidad de Cuenca, 2022-11-21) Cedillo Galarza, Juan Sebastián; Alvarado Martínez, Andrés Omar
Mountain -rivers are, by far, the most challenging case to model because of its bed characteristics and their energy dissipation mechanisms depending on its irregular morphology. Resistance, roughness, or friction parameter are equivalent terms. It plays an important role in 1-D open channel models to estimate different variables. Moreover, this parameter contains all the dissipative processes in a mountain river, and it is usually valued through field measurements, existing different methodologies to estimate it. Consequently, it is essential to determine which methodology is the most adequate to predict it. The resistance parameter determined in field is not always the same as the one used in a hydrodynamic model. In this thesis; cascades, plane bed, and step-pool has been studied in the Quinuas river (Ecuador). “Non-dimensional hydraulic geometry equations” (NDHG) were the best option to predict velocity in all the mountain river reaches. The parameters of NDHG varies depending on the author, therefore a methodology based on some field measurements to estimate the NDHG parameters was developed. The differences between model and field resistance coefficient depends on the morphology and flow magnitude. A machine learning technique using the system physics was develop providing optimal results to predict water depths and to calibrate resistance parameter.
Determinación de la mancha de inundación variando el coeficiente de Manning–sector del coliseo Jefferson Pérez Quezada
(Universidad de Cuenca, 2025-03-27) Idrovo Piña, Pedro Alejandro; Peñaloza Mejía, Guido Santiago; Sánchez Cordero, Esteban Remigio; Cedillo Galarza, Juan Sebastián
Roughness is a fundamental variable in the analysis of hydraulic behavior in one-dimensional open channel models, as it determines the resistance that water flow experiences when moving over various surfaces. In river modeling, this variable is closely linked to the material properties that make up the riverbed. This study aims to evaluate the impact of Manning's roughness coefficient on the extent of the floodplain of the Tomebamba River, specifically in the area adjacent to the Jefferson Pérez Quezada Coliseum in Cuenca, Ecuador. For this purpose, topographic data were collected using a total station, allowing the generation of a terrain surface with adequate resolution for one-dimensional hydraulic modeling. The analysis was conducted using HEC-RAS software, simulating various combinations of flow rates and roughness coefficients. Three representative flow rates, obtained from a prior flow measurement study in the area of interest, were used, along with a range of roughness values based on specialized technical references. The results revealed a positive correlation between the increase in Manning's coefficient and the expansion of the floodplain. Additionally, significant variability in hydraulic behavior was identified along different sections of the river, attributed to the geometric and topographic characteristics of the channel. This study is crucial for improving the accuracy of flood predictions and strengthening risk management in vulnerable areas.
Diferencia entre fórmulas empíricas para la predicción del coeficiente de Manning físico y efectivo
(Universidad de Cuenca, 2022-11-18) Narea Cárdenas, Katherine Estefanía; Sánchez Cordero, Esteban Remigio; Cedillo Galarza, Juan Sebastián
In this research, a comparison between effective and physical roughness parameters is carried out, together with an analysis of the performance of empirical equations for the prediction of the roughness coefficient. For this reason, three most common morphologies present in mountain rivers are used: Cascade, Plane-bed and Steep-pool. The physical roughness parameters were previously obtained from studies carried out by Cedillo et al., (2021a) in a study that includes multiple field measurements of various hydraulic variables. On the other hand, the effective roughness parameters were estimated from an analysis of the GLUE methodology implemented in the 1D hydrodynamic model in HECRAS. The estimation of these effective parameters was achieved from the analysis of the degree of adjustment of the model in relation to the field data called likelihood. The difference between the effective and physical coefficients depends on the magnitude of the flow and the morphology. In addition, a predictability analysis is performed through the use of various empirical equations to find the roughness coefficient: Bathurst, (1985) and Bathurst's semi-logarithmic equations, (2002), Nondimensional Hydraulic Geometry Equations (NDHG) established by Ferguson, (2007), Rickenmann & Recking, (2011), Cedillo et al., (2021a) and Cedillo et al., (2021b). Thus, the comparisons are made with both effective and physical parameters and the results are analyzed using the metrics: Absolute Error (MAE), Root Mean Square Error (RMSE) and Efficiency (Ef). The results show that the Nondimensional Hydraulic Geometry Equations (NDHG) present better predictability compared to the exponential and semilogarithmic equations. The equation established of Cedillo et al., (2021b) that was calibrated with the physical roughness data has a better predictability than those obtained by (Ferguson, 2007) and (Rickenmann & Recking, 2011). For the case of effective roughness, the equation established of Cedillo et al., (2021b) for Plane-bed and Step-pool has better performance than those proposed by (Ferguson, 2007) and (Rickenmann & Recking, 2011). On the other hand, regarding a comparison of likelihood curves with the results of Pappenberger, Beven, Horritt, & Blazkova, (2005), the curves obtained in this research have similarity in a type of curve analyzed by Pappenberger, Beven, Horritt, & Blazkova, (2005) where a well-defined maximum value is used as the effective roughness coefficient
Evaluación de diferentes metodologías de predicción del coeficiente de resistencia al flujo de ríos de montaña considerando su incertidumbre
(Universidad de Cuenca, 2022-07-19) Deleg Naula, Jonnathan Josue; Sánchez Cordero, Esteban Remigio; Cedillo Galarza, Juan Sebastián
The resistance coefficient is a very important parameter in the one-dimensional simulation of water flow. This parameter can be estimated using various methodologies, which can be equations, data tables and photographs. Like any other estimate, the prediction of the resistance coefficient is subject to an error or uncertainty due to various factors. In the present work, the performance of the different estimation methodologies of the Manning resistance coefficient was evaluated when considering a percentage variation of the coefficient. The data used for this purpose pertain to 3 morphologies of the Quinuas mountain river (cascade, step-pool y plane-bed). The performance was evaluated by comparing the water depth measured in various sections with the water depths obtained in the different one-dimensional models of the HEC-RAS software. The results indicate that the nondimensional geometry equations better estimate the resistance coefficient. In turn, in the cascade and step-pool morphologies, the nondimensional geometry equations have reached a maximum possible efficiency, therefore, in these cases, varying the estimated coefficient of resistance only worsens the results or a minor improvement is obtained. The remaining methodologies show in most cases that they underestimate the flow resistance, especially in flows of low magnitude. However, some methodologies under certain conditions show similar results to the nondimensional geometry equations. Finally, it was found that, in the cascade morphology, the performance of the methodologies is less sensitive to the variation of the resistance coefficient compared to the performance in the plane-bed morphology
Evaluación de los resultados de diferentes funciones Likelihoods de la metodología GLUE aplicadas en la obtención de coeficientes de rugosidad para ríos de montaña
(Universidad de Cuenca, 2023-07-24) Encalada Romero, Carlos Germán; Sánchez Cordero, Esteban Remigio; Cedillo Galarza, Juan Sebastián
This thesis work compares the behavior of different likelihood functions of the GLUE methodology in the classification of roughness coefficients of mountain rivers (three river morphologies: Cascade, Plane Bed and Step-pool) obtained from models. Bibliographic research on likelihood functions used to obtain roughness coefficients on rivers was carried out, and then these functions were used to classify a set of modeled data using as comparison parameters values measured in the field. The results showed an almost similar behavior between likelihood functions with the exception of some cases that tend to validate roughness coefficients that differ considerably with the values measured in the field. Finally, it can be seen that a likelihood function, depending on the parameters that are incorporated, can be as simple as the researcher wishes, making the complex function excessively particular to the river being analyzed, limiting its applicability to other rivers with similar characteristics.
Exploratory study of physic informed deep learning applied to a step-pool for different flow magnitudes
(Springer Science and Business Media Deutschland GmbH, 2022) Cedillo Galarza, Juan Sebastián; Alvarado Martínez, Andrés Omar; Sánchez Cordero, Esteban Remigio; Samaniego Alvarado, Esteban Patricio
Physical laws governing a certain phenomenon can be included in a deep-learning model within a new paradigm: the so-called physical informed deep learning (PIDL). Physical laws in hydraulics consist of partial differential equations (PDEs) resulting from balance laws. The potential use of PIDL in a step-pool reach having a complex flow and geometric characteristics is tested in this article. The studied morphology belongs to a hydraulic observatory in a mountain river in Ecuador where flow and geometric data are available. The water level profile of PIDL was compared to a stationary one-dimensional HEC-RAS model and water levels measured at three staff gauges in the reach. Saint–Venant equations, geometry data, and boundary conditions were used to implement a PIDL-based model. The chosen PIDL architecture is based on the one with the lowest value for the loss function. The resulting water level profile of the PIDL model does not have instabilities, and according to dimensionless RMSE is slightly less efficient in its predictions than the HEC RAS model. Moreover, the difference between HEC-RAS and PIDL water profile decreases as flow increases
Influencia de la resolución topográfica en los resultados de un modelo hidrodinámico unidimensional y su efecto en la construcción de mapas de inundación en ríos de montaña – sector Coliseo Jefferson Pérez
(Universidad de Cuenca, 2024-09-18) Caisán Velásquez, Edison Gonzalo; Campoverde Ureña, Jose Fernando; Sánchez Cordero, Esteban Remigio; Cedillo Galarza, Juan Sebastián
Flood maps are vital for community safety. By enhancing hydrodynamic models with high- resolution topographic data, disasters can be predicted more accurately. This study aims to investigate the influence of topographic resolution on the accuracy of one-dimensional hydrodynamic models and its impact on the creation of flood maps in mountain rivers. A quantitative methodology was employed to evaluate three types of topographic surveys: Total Station, Drone, and SIGTIERRAS in a specific section of the Tomebamba River (Jefferson Pérez Coliseum area). Three surfaces were generated using the same planimetric configuration, varying only the elevation component. Using the Total Station survey data as the benchmark, the vertical accuracy of each surface was assessed. The main metric used to evaluate this accuracy is the Nash-Sutcliffe Efficiency (NSE) coefficient. The results indicate that the Drone model, with a resolution of 2.23 cm/pixel, offers higher accuracy and correlation, especially in representing topographic elevations and flood areas, both in straight sections and curved alignments. In contrast, the SIGTIERRAS model, with a resolution of 3m/pixel, exhibited larger errors and lower correlation, highlighting the limitations of low-resolution data. These findings underscore the importance of using high-resolution topographic data to improve the accuracy of hydraulic simulations.
Patterns of difference between physical and 1-D calibrated effective roughness parameters in mountain rivers
(2021) Timbe Castro, Luis Manuel; Alvarado Martínez, Andrés Omar; Samaniego Alvarado, Esteban Patricio; Cedillo Galarza, Juan Sebastián; Sánchez Cordero, Esteban Remigio
Due to the presence of boulders and different morphologies, mountain rivers contain various resistance sources. To correctly simulate river flow using 1-D hydrodynamic models, an accurate estimation of the flow resistance is required. In this article, a comparison between the physical roughness parameter (PRP) and effective roughness coefficient (ERC) is presented for three of the most typical morphological configurations in mountain rivers: cascade, step-pool, and plane-bed. The PRP and its variation were obtained through multiple measurements of field variables and an uncertainty analysis, while the ERC range was derived with a GLUE procedure implemented in HEC-RAS, a 1-D hydrodynamic model. In the GLUE experiments, two modes of the Representative Friction Slope Method (RFSM) between two cross-sections were tested, including the variation in the roughness parameter. The results revealed that the RFSM effect was limited to low flows in cascade and step-pool. Moreover, when HEC-RAS selected the RSFM, only acceptable results were presented for plane-bed. The difference between ERC and PRP depended on the flow magnitude and the morphology, and as shown in this study, when the flow increased, the ERC and PRP ranges approached each other and even overlapped in cascade and step-pool. This research aimed to improve the roughness value selection process in a 1-D model given the importance of this parameter in the predictability of the results. In addition, a comparison was presented between the results obtained with the numerical model and the values calculated with the field measurements.
Resistance analysis of morphologies in headwater mountain streams
(2021) Cedillo Galarza, Juan Sebastián; Sánchez Cordero, Esteban Remigio; Timbe Castro, Luis Manuel; Samaniego Alvarado, Esteban Patricio; Alvarado Martínez, Andrés Omar
River flow velocity is determined by the energy available for flow motion and the energy fraction lost by flow resistance. We compared the performance of different equations for the Darcy-Weisbach resistance coefficient (f ) and empirical equations to predict flow velocity. The set of equations was tested using data from the Quinuas headwater mountain river in the Andean region. The data was collected in three Cascades, two Step-pools, and one Plane-bed covering a wide range of velocity magnitudes. The results reveal that nondimensional hydraulic geometry equations (NDHG) with a Nash-Sutcliffe efficiency index (EF) varying from 0.6–0.85 provide the most accurate velocity prediction. Furthermore, the study proposes a methodology applicable to all morphologies for defining the NDHG parameters using easily measured field data. The results show an improvement in predictability with EF values in the range of 0.81–0.86. Moreover, the methodology was tested against data from the literature, which was not divided into morphologies providing EF values of around 0.9. The authors encourage the application of the presented methodology to other reaches to obtain additional data about the NDHG parameters. Our findings suggest that those parameters could be related to reach characteristics (e.g., certain characteristic grain size), and in that case, the methodology could be useful in ungauged streams. © 2021 by the authors. Licensee MDPI, Basel, Switzerland
Validación de modelos computacionales de flujo dinámico con pruebas de trazadores en lagunas de gran escala
(2011) Cedillo Galarza, Juan Sebastián; Matailo Quituisaca, María Magdalena; Alvarado Martínez, Andrés Omar; Alvarado Martínez, Andrés Omar