A two-parameter design storm for Mediterranean convective rainfall

[EN] The following research explores the feasibility of building effective design storms for extreme hydrological regimes, such as the one which characterizes the rainfall regime of the east and south-east of the Iberian Peninsula, without employing intensity-duration-frequency (IDF) curves as a starting point. Nowadays, after decades of functioning hydrological automatic networks, there is an abundance of high-resolution rainfall data with a reasonable statistic representation, which enable the direct research of temporal patterns and inner structures of rainfall events at a given geographic location, with the aim of establishing a statistical synthesis directly based on those observed patterns. The authors propose a temporal design storm defined in analytical terms, through a two-parameter gamma-type function. The two parameters are directly estimated from 73 independent storms identified from rainfall records of high temporal resolution in Valencia (Spain). All the relevant analytical properties derived from that function are developed in order to use this storm in real applications. In particular, in order to assign a probability to the design storm (return period), an auxiliary variable combining maximum intensity and total cumulated rainfall is introduced. As a result, for a given return period, a set of three storms with different duration, depth and peak intensity are defined. The consistency of the results is verified by means of comparison with the classic method of alternating blocks based on an IDF curve, for the above mentioned study case.This work was supported by the Regional Government of Valencia (Generalitat Valenciana, Conselleria d'Educacio, Investigacio, Cultura i Esport) through the project "Formulacion de un hietograma sintetico con reproduccion de las relaciones de dependencia entre variables de evento y de la estructura interna espacio-temporal" (reference GV/2015/064).García Bartual, RL.; Andrés Doménech, I. 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Effect of Seasonality on the Quantiles Estimation of Maximum Floodwater Levels in a Reservoir and Maximum Outflows

[EN] Certain  relevant  variables  for  dam  safety  and  downstream  safety  assessments  are  analyzed using a stochastic approach. In particular, a method to estimate quantiles of maximum  outflow in a dam spillway and maximum water level reached in the reservoir during a flood event  is presented. The hydrological system analyzed herein is a small mountain catchment in north  Spain, whose main river is a tributary of Ebro river. The ancient Foradada dam is located in this  catchment. This dam has no gates, so that flood routing operation results from simple consideration  of fixed crest spillway hydraulics. In such case, both mentioned variables (maximum outflow and  maximum reservoir water level) are basically derived variables that depend on flood hydrograph  characteristics and the reservoir¿s initial water level. A Monte Carlo approach is performed to  generate very large samples of synthetic hydrographs and previous reservoir levels. The use of  extreme value copulas allows the ensembles to preserve statistical properties of historical samples  and the observed empirical correlations. Apart from the classical approach based on annual periods,  the modelling strategy is also applied differentiating two subperiods or seasons (i.e., summer and  winter). This allows to quantify the return period distortion introduced when seasonality is ignored  in the statistical analysis of the two relevant variables selected for hydrological risk assessment.  Results indicate significant deviations for return periods over 125 years. For the analyzed case study,  ignoring seasonal statistics and trends, yields to maximum outflows underestimation of 18% for T  = 500 years and 29% for T = 1000 years were obtained.The authors wish to acknowledge support from Confederación Hidrográfica del EbroAranda Domingo, JÁ.; García-Bartual, R. (2020). Effect of Seasonality on the Quantiles Estimation of  Maximum Floodwater Levels in a Reservoir and  Maximum Outflows. Water. 12(519):1-24. https://doi.org/10.3390/w12020519S12412519Blazkova, S., & Beven, K. (2004). Flood frequency estimation by continuous simulation of subcatchment rainfalls and discharges with the aim of improving dam safety assessment in a large basin in the Czech Republic. Journal of Hydrology, 292(1-4), 153-172. doi:10.1016/j.jhydrol.2003.12.025Mo, C., Mo, G., Yang, Q., Ruan, Y., Jiang, Q., & Jin, J. (2018). A quantitative model for danger degree evaluation of staged operation of earth dam reservoir in flood season and its application. Water Science and Engineering, 11(1), 81-87. doi:10.1016/j.wse.2017.07.001Liu, Z., Xu, X., Cheng, J., Wen, T., & Niu, J. (2018). Hydrological risk analysis of dam overtopping using bivariate statistical approach: a case study from Geheyan Reservoir, China. 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Real Time Flow Forecasting in a Mountain River Catchment Using Conceptual Models with Simple Error Correction Scheme

[EN] Methods in operational hydrology for real-time flash-flood forecasting need to be simple enough to match requirements of real-time system management. For this reason, hydrologic routing methods are widely used in river engineering. Among them, the popular Muskingum method is the most extended one, due to its simplicity and parsimonious formulation involving only two parameters. In the present application, two simple conceptual models with an error correction scheme were used. They were applied in practice to a mountain catchment located in the central Pyrenees (North of Spain), where occasional flash flooding events take place. Several relevant historical flood events have been selected for calibration and validation purposes. The models were designed to produce real-time predictions at the downstream gauge station, with variable lead times during a flood event. They generated accurate estimates of forecasted discharges at the downstream end of the river reach. For the validation data set and 2 h lead time, the estimated Nash-Sutcliffe coefficient was 0.970 for both models tested. The quality of the results, together with the simplicity of the formulations proposed, suggests an interesting potential for the practical use of these schemes for operational hydrology purposes.The authors wish to acknowledge support from Confederacion Hidrografica del Ebro.Montes, N.; Aranda Domingo, JÁ.; García-Bartual, R. (2020). Real Time Flow Forecasting in a Mountain River Catchment Using Conceptual Models with Simple Error Correction Scheme. Water. 12(5):1-18. https://doi.org/10.3390/w12051484S118125MORAMARCO, T., BARBETTA, S., MELONE, F., & SINGH, V. P. (2006). A real-time stage Muskingum forecasting model for a site without rating curve. Hydrological Sciences Journal, 51(1), 66-82. doi:10.1623/hysj.51.1.66Perumal, M., Moramarco, T., Barbetta, S., Melone, F., & Sahoo, B. (2011). Real-time flood stage forecasting by Variable Parameter Muskingum Stage hydrograph routing method. Hydrology Research, 42(2-3), 150-161. doi:10.2166/nh.2011.063Clark, C. O. (1945). Storage and the Unit Hydrograph. Transactions of the American Society of Civil Engineers, 110(1), 1419-1446. doi:10.1061/taceat.0005800Cunge, J. A. (1969). On The Subject Of A Flood Propagation Computation Method (Musklngum Method). Journal of Hydraulic Research, 7(2), 205-230. doi:10.1080/00221686909500264Dooge, J. C. I., Strupczewski, W. G., & Napiórkowski, J. J. (1982). Hydrodynamic derivation of storage parameters of the Muskingum model. Journal of Hydrology, 54(4), 371-387. doi:10.1016/0022-1694(82)90163-9Ponce, V. M., & Changanti, P. V. (1994). Variable-parameter Muskingum-Cunge method revisited. Journal of Hydrology, 162(3-4), 433-439. doi:10.1016/0022-1694(94)90241-0Ponce, V. M., & Theurer, F. D. (1983). Closure to « Accuracy Criteria in Diffusion Routing » by Victor Miguel Ponce and Fred D. Theurer (June, 1982). Journal of Hydraulic Engineering, 109(5), 806-807. doi:10.1061/(asce)0733-9429(1983)109:5(806)KUNDZEWICZ, Z. W. (1986). Physically based hydrological flood routing methods. Hydrological Sciences Journal, 31(2), 237-261. doi:10.1080/02626668609491042Singh, V. P., & Scarlatos, P. D. (1987). Analysis of Nonlinear Muskingum Flood Routing. Journal of Hydraulic Engineering, 113(1), 61-79. doi:10.1061/(asce)0733-9429(1987)113:1(61)Perumal, M. (1992). Multilinear muskingum flood routing method. Journal of Hydrology, 133(3-4), 259-272. doi:10.1016/0022-1694(92)90258-wTang, X., Knight, D. W., & Samuels, P. G. (1999). Variable parameter Muskingum-Cunge method for flood routing in a compound channel. Journal of Hydraulic Research, 37(5), 591-614. doi:10.1080/00221689909498519Al-Humoud, J. M., & Esen, I. I. (2006). Approximate Methods for the Estimation of Muskingum Flood Routing Parameters. Water Resources Management, 20(6), 979-990. doi:10.1007/s11269-006-9018-2Todini, E. (2007). A mass conservative and water storage consistent variable parameter Muskingum-Cunge approach. Hydrology and Earth System Sciences, 11(5), 1645-1659. doi:10.5194/hess-11-1645-2007Brakensiek, D. L. (1963). Estimating coefficients for storage flood routing. Journal of Geophysical Research, 68(24), 6471-6474. doi:10.1029/jz068i024p06471Birkhead, A. L., & James, C. S. (2002). Muskingum river routing with dynamic bank storage. Journal of Hydrology, 264(1-4), 113-132. doi:10.1016/s0022-1694(02)00068-9Perumal, M., & Price, R. K. (2013). A fully mass conservative variable parameter McCarthy–Muskingum method: Theory and verification. Journal of Hydrology, 502, 89-102. doi:10.1016/j.jhydrol.2013.08.023O’DONNELL, T. (1985). A direct three-parameter Muskingum procedure incorporating lateral inflow. Hydrological Sciences Journal, 30(4), 479-496. doi:10.1080/02626668509491013Kshirsagar, M. M., Rajagopalan, B., & Lal, U. (1995). Optimal parameter estimation for Muskingum routing with ungauged lateral inflow. Journal of Hydrology, 169(1-4), 25-35. doi:10.1016/0022-1694(94)02670-7Yadav, B., Perumal, M., & Bardossy, A. (2015). Variable parameter McCarthy–Muskingum routing method considering lateral flow. Journal of Hydrology, 523, 489-499. doi:10.1016/j.jhydrol.2015.01.068PERUMAL, M. (1994). Hydrodynamic derivation of a variable parameter Muskingum method: 1. Theory and solution procedure. Hydrological Sciences Journal, 39(5), 431-442. doi:10.1080/02626669409492766Perumal, M., E., O., & Raju, K. G. R. (2001). Field Applications of a Variable-Parameter Muskingum Method. Journal of Hydrologic Engineering, 6(3), 196-207. doi:10.1061/(asce)1084-0699(2001)6:3(196)Perumal, M., & Sahoo, B. (2007). Applicability criteria of the variable parameter Muskingum stage and discharge routing methods. Water Resources Research, 43(5). doi:10.1029/2006wr004909Gill, M. A. (1978). Flood routing by the Muskingum method. Journal of Hydrology, 36(3-4), 353-363. doi:10.1016/0022-1694(78)90153-1Tung, Y. (1985). River Flood Routing by Nonlinear Muskingum Method. Journal of Hydraulic Engineering, 111(12), 1447-1460. doi:10.1061/(asce)0733-9429(1985)111:12(1447)Yoon, J., & Padmanabhan, G. (1993). Parameter Estimation of Linear and Nonlinear Muskingum Models. Journal of Water Resources Planning and Management, 119(5), 600-610. doi:10.1061/(asce)0733-9496(1993)119:5(600)Mohan, S. (1997). Parameter Estimation of Nonlinear Muskingum Models Using Genetic Algorithm. Journal of Hydraulic Engineering, 123(2), 137-142. doi:10.1061/(asce)0733-9429(1997)123:2(137)Luo, J., & Xie, J. (2010). Parameter Estimation for Nonlinear Muskingum Model Based on Immune Clonal Selection Algorithm. Journal of Hydrologic Engineering, 15(10), 844-851. doi:10.1061/(asce)he.1943-5584.0000244Kang, L., & Zhang, S. (2016). Application of the Elitist-Mutated PSO and an Improved GSA to Estimate Parameters of Linear and Nonlinear Muskingum Flood Routing Models. PLOS ONE, 11(1), e0147338. doi:10.1371/journal.pone.0147338Geem, Z. W. (2006). Parameter Estimation for the Nonlinear Muskingum Model Using the BFGS Technique. Journal of Irrigation and Drainage Engineering, 132(5), 474-478. doi:10.1061/(asce)0733-9437(2006)132:5(474)Chu, H.-J., & Chang, L.-C. (2009). Applying Particle Swarm Optimization to Parameter Estimation of the Nonlinear Muskingum Model. Journal of Hydrologic Engineering, 14(9), 1024-1027. doi:10.1061/(asce)he.1943-5584.0000070Barati, R. (2011). Parameter Estimation of Nonlinear Muskingum Models Using Nelder-Mead Simplex Algorithm. Journal of Hydrologic Engineering, 16(11), 946-954. doi:10.1061/(asce)he.1943-5584.0000379Karahan, H., Gurarslan, G., & Geem, Z. W. (2013). Parameter Estimation of the Nonlinear Muskingum Flood-Routing Model Using a Hybrid Harmony Search Algorithm. Journal of Hydrologic Engineering, 18(3), 352-360. doi:10.1061/(asce)he.1943-5584.0000608Chen, X. Y., Chau, K. W., & Busari, A. O. (2015). A comparative study of population-based optimization algorithms for downstream river flow forecasting by a hybrid neural network model. Engineering Applications of Artificial Intelligence, 46, 258-268. doi:10.1016/j.engappai.2015.09.010Latt, Z. Z. (2015). Application of Feedforward Artificial Neural Network in Muskingum Flood Routing: a Black-Box Forecasting Approach for a Natural River System. Water Resources Management, 29(14), 4995-5014. doi:10.1007/s11269-015-1100-1Niazkar, M., & Afzali, S. H. (2016). Application of New Hybrid Optimization Technique for Parameter Estimation of New Improved Version of Muskingum Model. Water Resources Management, 30(13), 4713-4730. doi:10.1007/s11269-016-1449-9Kucukkoc, I., & Zhang, D. Z. (2015). Integrating ant colony and genetic algorithms in the balancing and scheduling of complex assembly lines. The International Journal of Advanced Manufacturing Technology, 82(1-4), 265-285. doi:10.1007/s00170-015-7320-yBazargan, J., & Norouzi, H. (2018). Investigation the Effect of Using Variable Values for the Parameters of the Linear Muskingum Method Using the Particle Swarm Algorithm (PSO). Water Resources Management, 32(14), 4763-4777. doi:10.1007/s11269-018-2082-6Ehteram, M., Mousavi, S. F., Karami, H., Farzin, S., Singh, V. P., Chau, K., & El-Shafie, A. (2018). Reservoir operation based on evolutionary algorithms and multi-criteria decision-making under climate change and uncertainty. Journal of Hydroinformatics, 20(2), 332-355. doi:10.2166/hydro.2018.094Pei, J., Su, Y., & Zhang, D. (2016). Fuzzy energy management strategy for parallel HEV based on pigeon-inspired optimization algorithm. Science China Technological Sciences, 60(3), 425-433. doi:10.1007/s11431-016-0485-8SCHUMM, S. A. (1956). EVOLUTION OF DRAINAGE SYSTEMS AND SLOPES IN BADLANDS AT PERTH AMBOY, NEW JERSEY. Geological Society of America Bulletin, 67(5), 597. doi:10.1130/0016-7606(1956)67[597:eodsas]2.0.co;2Baláž, M., Danáčová, M., & Szolgay, J. (2010). On the use of the Muskingum method for the simulation of flood wave movements. Slovak Journal of Civil Engineering, 18(3), 14-20. doi:10.2478/v10189-010-0012-6Franchini, M., & Lamberti, P. (1994). A flood routing Muskingum type simulation and forecasting model based on level data alone. Water Resources Research, 30(7), 2183-2196. doi:10.1029/94wr00536Yadav, O. P., Singh, N., Goel, P. S., & Itabashi-Campbell, R. (2003). A Framework for Reliability Prediction During Product Development Process Incorporating Engineering Judgments. Quality Engineering, 15(4), 649-662. doi:10.1081/qen-120018396Weinmann, P. E., & Laurenson, E. M. (1979). Approximate Flood Routing Methods: A Review. Journal of the Hydraulics Division, 105(12), 1521-1536. doi:10.1061/jyceaj.0005329Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models part I — A discussion of principles. Journal of Hydrology, 10(3), 282-290. doi:10.1016/0022-1694(70)90255-6Kitanidis, P. K., & Bras, R. L. (1980). Real-time forecasting with a conceptual hydrologic model: 2. Applications and results. Water Resources Research, 16(6), 1034-1044. doi:10.1029/wr016i006p01034Franchini, M., Bernini, A., Barbetta, S., & Moramarco, T. (2011). Forecasting discharges at the downstream end of a river reach through two simple Muskingum based procedures. Journal of Hydrology, 399(3-4), 335-352. doi:10.1016/j.jhydrol.2011.01.009Alhumoud, J., & Almashan, N. (2019). Muskingum Method with Variable Parameter Estimation. Mathematical Modelling of Engineering Problems, 6(3), 355-362. doi:10.18280/mmep.060306Yang, R., Hou, B., Xiao, W., Liang, C., Zhang, X., Li, B., & Yu, H. (2019). The applicability of real-time flood forecasting correction techniques coupled with the Muskingum method. Hydrology Research, 51(1), 17-29. doi:10.2166/nh.2019.12

Assessment of the Performance of a Modified USBR Type II Stilling Basin by a Validated CFD Model

[EN] The adaptation of existing dams is of paramount importance to face the challenge posed by climate change and new legal frameworks. Thus, it is crucial to optimize the design of stilling basins to reduce the hydraulic jump dimensions without jeopardizing the energy dissipation in the structure. A numerical model was developed to simulate a US Bureau of Reclamation Type II basin. The model was validated with a specifically designed physical model and then was used to simulate and test the performance of the basin after adding a second row of chute blocks. The results showed a reduction in the hydraulic jump dimensions in terms of the sequent depth ratio and the roller length, which were respectively 2.5% and 1.4% lower in the modified design. These results would allow an estimated increase of the discharge in the basin close to 10%. Furthermore, this new design had 1.2% higher efficiency. Consequently, the modifications proposed for the basin design suggest improved performance of the structure. The issue of the hydraulic jump length estimation also was discussed, and different approaches were introduced and compared. These methods follow a structured and systematic procedure and gave consistent results for the developed models.The authors acknowledge the collaboration of the Hydraulics Laboratory of the Department of Hydraulic Engineering and Environment from Universitat Politecnica de Valencia (UPV) and their technicians Juan Carlos Edo and Joaquin Oliver in the construction of the experimental device used for the numerical model setup and validation. The work was supported by the research project "La aireacion del flujo y su implementacion en prototipo para la mejora de la disipacion de energia de la lamina vertiente por resalto hidraulico en distintos tipos de presas" (BIA2017-85412-C2-1-R), funded by the Spanish Agencia Estatal de Investigacion and FEDER.Macián-Pérez, JF.; Vallés-Morán, FJ.; García-Bartual, R. (2021). Assessment of the Performance of a Modified USBR Type II Stilling Basin by a Validated CFD Model. Journal of Irrigation and Drainage Engineering. 147(11):1-12. https://doi.org/10.1061/(ASCE)IR.1943-4774.00016231121471

Synthetic Hydrographs Generation Downstream of a River Junction Using a Copula Approach for Hydrological Risk Assessment in Large Dams

[EN] Peak flows values (Q) and hydrograph volumes (V) are obtained from a selected family of historical flood events (period 1957¿2017), for two neighboring mountain catchments located in the Ebro river basin, Spain: rivers Ésera and Isábena. Barasona dam is located downstream of the river junction. The peaks over threshold (POT) method is used for a univariate frequency analysis performed for both variables, Q and V, comparing several suitable distribution functions. Extreme value copulas families have been applied to model the bivariate distribution (Q, V) for each of the rivers. Several goodness-of-fit tests were used to assess the applicability of the selected copulas. A similar copula approach was carried out to model the dependence between peak flows of both rivers. Based on the above-mentioned statistical analysis, a Monte Carlo simulation of synthetic design flood hydrographs (DFH) downstream of the river junction is performed. A gamma-type theoretical pattern is assumed for partial hydrographs. The resulting synthetic hydrographs at the Barasona reservoir are finally obtained accounting for flow peak time lag, also described in statistical terms. A 50,000 hydrographs ensemble was generated, preserving statistical properties of marginal distributions as well as statistical dependence between variables. The proposed method provides an efficient and practical modeling framework for the hydrological risk assessment of the dam, improving the basis for the optimal management of such infrastructure.Aranda Domingo, JÁ.; García-Bartual, R. (2018). Synthetic Hydrographs Generation Downstream of a River Junction Using a Copula Approach for Hydrological Risk Assessment in Large Dams. Water. 10(11):1570-1589. https://doi.org/10.3390/w10111570S15701589101

A process-based flood frequency analysis within a trivariate statistical framework. Application to a semi-arid Mediterranean case study

[EN] This paper proposes a trivariate methodology for flood frequency estimation. It combines the flood peak, storm magnitude, and initial soil moisture condition (ISMC) as the main flood-related statistical variables to be considered. The semi-arid Mediterranean "Rambla del Poyo" catchment has been used as a representative case study where the influence of the spatio-temporal variability of the storms and the ISMC on floods can lead to differences of up to two orders of magnitude in quantiles when the most commonly used methods are applied. In order to incorporate the main flood-generating mechanisms, the integrated use of a multidimensional storm generator with distributed hydrological modelling is proposed. Flood quantiles are then estimated by combining the maximum flows with the storm magnitude and ISMC in a trivariate probability distribution function through the application of Bayes' theorem and Lagrange's Mean Value theorem. Although the methodology proposed in this paper has been applied and tested in only one case study, it can be extended to other case studies due to its process-based orientation.This research was funded by the Ministry of Science and Innovation of Spain through the research projects TETISMED (CGL2014-58127-C3-3-R) and TETISCHANGE (ref RTI2018-093717-B-I00). The authors thank both AEMET for the daily data and Jucar River Basin Water Authority for the sub-daily data provided for this research. We also thank the Associate Editor and the two anonymous reviewers for their valuable comments that contributed to the improvement of the manuscript.Salazar Galán, SA.; García-Bartual, R.; Salinas, JL.; Francés, F. (2021). A process-based flood frequency analysis within a trivariate statistical framework. Application to a semi-arid Mediterranean case study. Journal of Hydrology. 603(Part C):1-15. https://doi.org/10.1016/j.jhydrol.2021.127081S115603Part

Análisis interno de los chaparrones máximos para la ciudad de Valencia a partir de series pluviométricas de alta resolución temporal

[ES] Se analizan los datos pluviométricos cinco-minutales de la ciudad de Valencia, separando los eventos independientes y llevando a cabo un exhaustivo estudio estadístico sobre sus propiedades, incluyendo duración, total acumulado, máxima intensidad e índice de convectividad. Como resultado, se proponen relaciones simples con ajuste satisfactorio entre duración y volumen, para tres grupos de eventos clasificados en función de su máxima intensidad.Los resultados publicados en el presente artículo han sido desarrollados en el marco del Proyecto FORMULACIÓN DE UN HIETOGRAMA SINTÉTICO CON REPRODUCCIÓN DE LAS RELACIONES DE DEPENDENCIA ENTRE VARIABLES DE EVENTO Y DE LA ESTRUCTURA INTERNA ESPACIO-TEMPORAL , nú ero de e ped ente GV 2 15/064 de las ayudas para la realización de proyectos de I+D para grupos de investigación emergentes financiados por la Conselleria d Educació, Cultura i Esport de la Generalitat ValencianaRico Cortés, M.; García-Bartual, R.; Andrés Doménech, I. (2015). Análisis interno de los chaparrones máximos para la ciudad de Valencia a partir de series pluviométricas de alta resolución temporal. Universidad de Córdoba. 1-10. http://hdl.handle.net/10251/142690S11

Analysis of the Flow in a Typified USBR II Stilling Basin through a Numerical and Physical Modeling Approach

[EN] Adaptation of stilling basins to higher discharges than those considered for their design implies deep knowledge of the flow developed in these structures. To this end, the hydraulic jump occurring in a typified United States Bureau of Reclamation Type II (USBR II) stilling basin was analyzed using a numerical and experimental modeling approach. A reduced-scale physical model to conduct an experimental campaign was built and a numerical computational fluid dynamics (CFD) model was prepared to carry out the corresponding simulations. Both models were able to successfully reproduce the case study in terms of hydraulic jump shape, velocity profiles, and pressure distributions. The analysis revealed not only similarities to the flow in classical hydraulic jumps but also the influence of the energy dissipation devices existing in the stilling basin, all in good agreement with bibliographical information, despite some slight differences. Furthermore, the void fraction distribution was analyzed, showing satisfactory performance of the physical model, although the numerical approach presented some limitations to adequately represent the flow aeration mechanisms, which are discussed herein. Overall, the presented modeling approach can be considered as a useful tool to address the analysis of free surface flows occurring in stilling basins.This research was funded by 'Generalitat Valenciana predoctoral grants (Grant number [2015/7521])', in collaboration with the European Social Funds and by the research project: 'La aireacion del flujo y su implementacion en prototipo para la mejora de la disipacion de energia de la lamina vertiente por resalto hidraulico en distintos tipos de presas' (BIA2017-85412-C2-1-R), funded by the Spanish Ministry of Economy.Macián Pérez, JF.; García-Bartual, R.; Huber, B.; Bayón, A.; Vallés-Morán, FJ. (2020). Analysis of the Flow in a Typified USBR II Stilling Basin through a Numerical and Physical Modeling Approach. Water. 12(1):1-20. https://doi.org/10.3390/w12010227S120121Bayon, A., Valero, D., García-Bartual, R., Vallés-Morán, F. ​José, & López-Jiménez, P. A. (2016). Performance assessment of OpenFOAM and FLOW-3D in the numerical modeling of a low Reynolds number hydraulic jump. Environmental Modelling & Software, 80, 322-335. doi:10.1016/j.envsoft.2016.02.018Chanson, H. (2008). Turbulent air–water flows in hydraulic structures: dynamic similarity and scale effects. Environmental Fluid Mechanics, 9(2), 125-142. doi:10.1007/s10652-008-9078-3Heller, V. (2011). Scale effects in physical hydraulic engineering models. Journal of Hydraulic Research, 49(3), 293-306. doi:10.1080/00221686.2011.578914Chanson, H. (2013). Hydraulics of aerated flows:qui pro quo? Journal of Hydraulic Research, 51(3), 223-243. doi:10.1080/00221686.2013.795917Blocken, B., & Gualtieri, C. (2012). Ten iterative steps for model development and evaluation applied to Computational Fluid Dynamics for Environmental Fluid Mechanics. Environmental Modelling & Software, 33, 1-22. doi:10.1016/j.envsoft.2012.02.001Wang, H., & Chanson, H. (2015). Experimental Study of Turbulent Fluctuations in Hydraulic Jumps. Journal of Hydraulic Engineering, 141(7), 04015010. doi:10.1061/(asce)hy.1943-7900.0001010Valero, D., Viti, N., & Gualtieri, C. (2018). Numerical Simulation of Hydraulic Jumps. Part 1: Experimental Data for Modelling Performance Assessment. Water, 11(1), 36. doi:10.3390/w11010036Viti, N., Valero, D., & Gualtieri, C. (2018). Numerical Simulation of Hydraulic Jumps. Part 2: Recent Results and Future Outlook. Water, 11(1), 28. doi:10.3390/w11010028Bayon-Barrachina, A., & Lopez-Jimenez, P. A. (2015). Numerical analysis of hydraulic jumps using OpenFOAM. Journal of Hydroinformatics, 17(4), 662-678. doi:10.2166/hydro.2015.041Teuber, K., Broecker, T., Bayón, A., Nützmann, G., & Hinkelmann, R. (2019). CFD-modelling of free surface flows in closed conduits. Progress in Computational Fluid Dynamics, An International Journal, 19(6), 368. doi:10.1504/pcfd.2019.103266Chachereau, Y., & Chanson, H. (2011). Free-surface fluctuations and turbulence in hydraulic jumps. Experimental Thermal and Fluid Science, 35(6), 896-909. doi:10.1016/j.expthermflusci.2011.01.009Zhang, G., Wang, H., & Chanson, H. (2012). Turbulence and aeration in hydraulic jumps: free-surface fluctuation and integral turbulent scale measurements. Environmental Fluid Mechanics, 13(2), 189-204. doi:10.1007/s10652-012-9254-3Mossa, M. (1999). On the oscillating characteristics of hydraulic jumps. Journal of Hydraulic Research, 37(4), 541-558. doi:10.1080/00221686.1999.9628267Chanson, H., & Brattberg, T. (2000). Experimental study of the air–water shear flow in a hydraulic jump. International Journal of Multiphase Flow, 26(4), 583-607. doi:10.1016/s0301-9322(99)00016-6Murzyn, F., Mouaze, D., & Chaplin, J. R. (2005). Optical fibre probe measurements of bubbly flow in hydraulic jumps. International Journal of Multiphase Flow, 31(1), 141-154. doi:10.1016/j.ijmultiphaseflow.2004.09.004Gualtieri, C., & Chanson, H. (2007). Experimental analysis of Froude number effect on air entrainment in the hydraulic jump. Environmental Fluid Mechanics, 7(3), 217-238. doi:10.1007/s10652-006-9016-1Chanson, H., & Gualtieri, C. (2008). Similitude and scale effects of air entrainment in hydraulic jumps. Journal of Hydraulic Research, 46(1), 35-44. doi:10.1080/00221686.2008.9521841Ho, D. K. H., & Riddette, K. M. (2010). Application of computational fluid dynamics to evaluate hydraulic performance of spillways in australia. Australian Journal of Civil Engineering, 6(1), 81-104. doi:10.1080/14488353.2010.11463946Dong, Wang, Vetsch, Boes, & Tan. (2019). Numerical Simulation of Air–Water Two-Phase Flow on Stepped Spillways Behind X-Shaped Flaring Gate Piers under Very High Unit Discharge. Water, 11(10), 1956. doi:10.3390/w11101956Toso, J. W., & Bowers, C. E. (1988). Extreme Pressures in Hydraulic‐Jump Stilling Basins. Journal of Hydraulic Engineering, 114(8), 829-843. doi:10.1061/(asce)0733-9429(1988)114:8(829)Houichi, L., Ibrahim, G., & Achour, B. (2006). Experiments for the Discharge Capacity of the Siphon Spillway Having the Creager-Ofitserov Profile. International Journal of Fluid Mechanics Research, 33(5), 395-406. doi:10.1615/interjfluidmechres.v33.i5.10Padulano, R., Fecarotta, O., Del Giudice, G., & Carravetta, A. (2017). Hydraulic Design of a USBR Type II Stilling Basin. Journal of Irrigation and Drainage Engineering, 143(5), 04017001. doi:10.1061/(asce)ir.1943-4774.0001150Hirt, C. ., & Nichols, B. . (1981). Volume of fluid (VOF) method for the dynamics of free boundaries. 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Physics of Fluids A: Fluid Dynamics, 4(7), 1510-1520. doi:10.1063/1.858424Li, S., & Zhang, J. (2018). Numerical Investigation on the Hydraulic Properties of the Skimming Flow over Pooled Stepped Spillway. Water, 10(10), 1478. doi:10.3390/w10101478Zhang, W., Wang, J., Zhou, C., Dong, Z., & Zhou, Z. (2018). Numerical Simulation of Hydraulic Characteristics in A Vortex Drop Shaft. Water, 10(10), 1393. doi:10.3390/w10101393Xiang, M., Cheung, S. C. P., Tu, J. Y., & Zhang, W. H. (2014). A multi-fluid modelling approach for the air entrainment and internal bubbly flow region in hydraulic jumps. Ocean Engineering, 91, 51-63. doi:10.1016/j.oceaneng.2014.08.016Procedure for Estimation and Reporting of Uncertainty Due to Discretization in CFD Applications. (2008). Journal of Fluids Engineering, 130(7), 078001. doi:10.1115/1.2960953Cartellier, A., & Achard, J. L. (1991). Local phase detection probes in fluid/fluid two‐phase flows. 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Characterization of Structural Properties in High Reynolds Hydraulic Jump Based on CFD and Physical Modeling Approaches

[EN] A classical hydraulic jump with Froude number (Fr1=6) and Reynolds number (Re1=210,000) was characterized using the computational fluid dynamics (CFD) codes OpenFOAM and FLOW-3D, whose performance was assessed. The results were compared with experimental data from a physical model designed for this purpose. The most relevant hydraulic jump characteristics were investigated, including hydraulic jump efficiency, roller length, free surface profile, distributions of velocity and pressure, and fluctuating variables. The model outcome was also compared with previous results from the literature. Both CFD codes were found to represent with high accuracy the hydraulic jump surface profile, roller length, efficiency, and sequent depths ratio, consistently with previous research. Some significant differences were found between both CFD codes regarding velocity distributions and pressure fluctuations, although in general the results agree well with experimental and bibliographical observations. This finding makes models with these characteristics suitable for engineering applications involving the design and optimization of energy dissipation devices.The research presented herein was possible thanks to the Generalitat Valenciana predoctoral grants [Ref. (2015/7521)], in collaboration with the European Social Funds and to the research project La aireacion del flujo y su implementacion en prototipo para la mejora de la disipacion de energia de la lamina vertiente por resalto hidraulico en distintos tipos de presas (BIA2017-85412-C2-1-R), funded by the Spanish Ministry of Economy.Macián Pérez, JF.; Bayón, A.; García-Bartual, R.; López Jiménez, PA.; Vallés-Morán, FJ. (2020). 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Macián-Pérez F. J. Vallés-Morán and P. A. López-Jiménez. 2019. “Effect of RANS turbulence model in hydraulic jump CFD simulations.” In E-proc. 38th IAHR World Congress. Panama City Panama: Spanish Ministry of Economy.Bayon, A., Toro, J. P., Bombardelli, F. A., Matos, J., & López-Jiménez, P. A. (2018). Influence of VOF technique, turbulence model and discretization scheme on the numerical simulation of the non-aerated, skimming flow in stepped spillways. Journal of Hydro-environment Research, 19, 137-149. doi:10.1016/j.jher.2017.10.002Bayon, A., Valero, D., García-Bartual, R., Vallés-Morán, F. ​José, & López-Jiménez, P. A. (2016). Performance assessment of OpenFOAM and FLOW-3D in the numerical modeling of a low Reynolds number hydraulic jump. Environmental Modelling & Software, 80, 322-335. doi:10.1016/j.envsoft.2016.02.018Bennett, N. D., Croke, B. F. W., Guariso, G., Guillaume, J. H. A., Hamilton, S. H., Jakeman, A. J., … Andreassian, V. (2013). 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Meditenanean rainfull space-time variabili| and its relation with runoff modelling

[ES] El objetivo de esta investigación es estudiar el efecto de la variabilidad espacio-temporal de la lluvia en la respuesta hidrológica de la cuenca (caudal pico y volumen del hidrograma de crecida). Para ello se generaron 100 episodios sintéticos con el modelo estocástico multidimensional de lluvia RAINGEN, que sirvieron como entrada al modelo distribuido lluvia-escorrentía TETIS. La aplicación se llevó a cabo en la cuenca de la Rambla del Poyo, ubicada en la costa mediterránea de España. Se consideraron densidades de medición equivalentes a 1, 4, 16 y 64 Km2/pluviómetro e intervalo temporal de 10 minutos, para áreas entre 2 km2 y 421 km2. Se estudió también la respuesta de las cuencas considerando como entraba el hietograma medio de precipitación[EN] The aim of this paper is to study the effecto of the space-time rainfall variability in runoff modelling, specially the peak flow and the runoff volume. The distributed rain-fall-runoff model TETIS has been used for the discharge simulation, while the rainfall input consisted on a family of one hundred synthetics events, generated with the multidimentional stochastic rainfall model RAINGEN. teh study area is the Rambla del Poyo basin, located in the mediterranean coast in Spain. Rain gauge densities of 1, 4, 16 and 64 km2/gauge were considered, and time level of aggregation of 10 minutes considering basins between 2 km2 and 421 km2. The cahtchment's response to the average precipitation hyetograph was also compuetd and compared to the others.Guichard Romero, D.; García Bartual, RL.; Francés, F.; Domínguez Mora, R. (2006). La variabilidad espacio-temporal de la lluvia mediterránea y su influencia en la respuesta hidrológica. Quehacer cientifico en Chiapas. 1(1):9-21. http://hdl.handle.net/10251/99489S9211