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. (2017). A two-parameter design storm for Mediterranean convective rainfall. HYDROLOGY AND EARTH SYSTEM SCIENCES. 21(5):2377-2387. https://doi.org/10.5194/hess-21-2377-2017S23772387215Adams, B. J. and Howard, C. D. D.: Design Storm Pathology, Can. Water Resour. J., 11, 49–55, https://doi.org/10.4296/cwrj1103049, 1986.Alfieri, L., Laio, F., and Claps, P.: A simulation experiment for optimal design hyetograph selection, Hydrol. Process., 22, 813–820, https://doi.org/10.1002/hyp.6646, 2008.Andrés-Doménech, I., Montanari, A., and Marco, J. B.: Stochastic rainfall analysis for storm tank performance evaluation, Hydrol. Earth Syst. Sci., 14, 1221–1232, https://doi.org/10.5194/hess-14-1221-2010, 2010.Andrés-Doménech, I., García-Bartual, R., Rico Cortés, M., and Albentosa Hernández, E.: A Gaussian design-storm for Mediterranean convective events. Sustainable Hydraulics in the Era of Global Change, edited by: Erpicum, S., Dewals, B., Archambeau, P., and Pirotton, M., Taylor & Francis, London, ISBN 978-1-138-02977-4, 2016.Ball, J. E.: The influence of storm temporal patterns on catchment response, J. Hydrol., 158, 285–303, 1994.Barnolas, M., Rigo, T., and Llasat, M. C.: Characteristics of 2-D convective structures in Catalonia (NE Spain): an analysis using radar data and GIS, Hydrol. Earth Syst. Sci., 14, 129–139, https://doi.org/10.5194/hess-14-129-2010, 2010.Bonta, J. V. and Rao, R.: Factors affecting the identification of independent storm events, J. Hydrol., 98, 275–293, 1988.Brummer, J.: Rainfall events as paths of a stochastic process: Problems of design storm analysis, Water Sci. Technol., 16, 131–138, 1984.Capsoni, C., Luini, L., Paraboni, A., Riva, C., and Martellucci A.: A new prediction model of rain attenuation that separately accounts for stratiform and convective rain, IEEE T. Antenn. Propag., 57, 196–204, 2009.Chow, V. T., Maidment, D. R., and Mays, L. W.: Applied hydrology, Mc Graw-Hill, New York, 1988.De Luca, D. L.: Analysis and modelling of rainfall fields at different resolutions in southern Italy, Hydrolog. Sci. J., 59, 1536–1558, https://doi.org/10.1080/02626667.2014.926013, 2014.Di Baldassarre, G., Brath, A., and Montanari, A.: Reliability of different depth-duration-frequency equations for estimating short-duration design storms, Water Resour. Res., 42, W12501, https://doi.org/10.1029/2006WR004911, 2006.Dunkerley, D.: Identifying individual rain events from pluviography records: a review with analysis of data from an Australian dryland site, Hydrol. Process., 22, 5024–5036, 2008.Frances, F., García-Bartual, R., and Bussi, G.: High return period annual maximum reservoir water level quantiles estimation using synthetic generated flood events, in: “Risk Analysis, Dam Safety, Dam Security and Critical Infrastructure Management”, Taylor and Francis, ISBN 978-0-415-62078-9, 185–190, 2012.French, R. and Jones, M.: Design rainfall temporal patterns in Australian Rainfall and Runoff: Durations exceeding one hour, Australian Journal of Water Resources, 16, 21–27, 2012.Froehlich, D. C.: Mathematical formulations of NRCS 24-hour design storms, J. Irrig. Drain E.-ASCE, 135, 241–247, https://doi.org/10.1061/(ASCE)0733-9437(2009)135:2(241), 2009.García-Bartual, R. and Marco, J.: A stochastic model of the internal structure of convective precipitation in time at a raingauge site, J. Hydrol., 118, 129–142, https://doi.org/10.1016/0022-1694(90)90254-U, 1990.García-Bartual, R. and Schneider, M.: Estimating maximum expected short-duration rainfall intensities from extreme convective storms, Phys. Chem. Earth Pt. B, 26, 675–681, https://doi.org/10.1016/S1464-1909(01)00068-5, 2001.Hicks, W. I.: A method of computing urban runoff, T. Am. Soc. Civ. Eng., 109, 1217–1253, 1944.Hogg, W. D.: Time distribution of short duration rainfall in Canada, in: Proceedings Canadian Hydrology Symposium, 80, Ottawa, Ontario, 53–63, 1980.Hogg, W. D.: Distribution of design rainfall with time: design considerations. American Geophysical Union Chapman on Rainfall Rates, Urbana, Illinois, 27–29 April 1982.Hoppe, H.: Impact of input data uncertainties on urban drainage models: climate change – a crucial issue? In Proceedings of the 11th International Conference on Urban Drainage, Edinburgh, UK, 31 August–5 September, 10 pp., 2008.Huff, F. A.: Time distribution of rainfall in heavy storms, Water Resour. Res., 3, 1007–1019, https://doi.org/10.1029/WR003i004p01007, 1967.Huff, F. A. and Angel, J. R.: Rainfall Distributions and Hydroclimatic Characteristics of Heavy Rainstorms in Illinois (Bulletin 70), Illinois State Water Survey, 1989.Keifer, C. J. and Chu, H. H.: Synthetic storm pattern for drainage design, J. Hydraul. Eng-ASCE, 83, 1–25, 1957.Kuichling, E.: The relation between rainfall and the discharge in sewers in populous districts, T. Am. Soc. Civ. Eng., 20, 37–40, 1889.Llasat, M. C.: . An objective classification of rainfall events on the basis of their convective features: application to rainfall intensity in the northeast of Spain, Int. J. Climatol., 21, 1385–1400, 2001.McCuen, R. H.: Hydrologic analysis and design, Prentice-Hall, Englewood Cliffs, N. J., 1989.McPherson, M. B.: Urban runoff control planning, EPA-600/9-78-035, Environmental Protection Agency, Washington D.C., 1978.Northrop, P. J. and Stone, T. M.: A point process model for rainfall with truncated gaussian rain cells. Research Report No. 251, Department of Statistical Science, University College London, 2005.Packman, J. C. and Kidd, C. H. R.: A logical approach to the design storm concept, Water Resour. Res., 16, 994–1000, https://doi.org/10.1029/WR016i006p00994, 1980.Pilgrim, D. H.: Australian rainfall and runoff, a guide to flood estimation. The Institution of Engineers, ACT, Australia, 1987.Pilgrim, D. H. and Cordery, I.: Rainfall temporal patterns for design floods, J. Hydr. Eng. Div.-ASCE, 101, 81–95, 1975.Restrepo-Posada, P. J. and Eagleson, P. S.: Identification of independent rainstorms, J. Hydrol., 55, 303–319, 1982.Rigo, T. and Llasat, M. C.: Radar analysis of the life cycle of Mesoscale Convective Systems during the 10 June 2000 event, Nat. Hazards Earth Syst. Sci., 5, 959–970, https://doi.org/10.5194/nhess-5-959-2005, 2005.Salsón, S. and Garcia-Bartual, R.: A space-time rainfall generator for highly convective Mediterranean rainstorms, Nat. Hazards Earth Syst. Sci., 3, 103–114, https://doi.org/10.5194/nhess-3-103-2003, 2003.Témez, J.: Cálculo Hidrometeorológico de caudales máximos en pequeñas cuencas naturales, Dirección General de Carreteras, Madrid, España, 1978.Vaskova, I.: Cálculo de las curvas IDF mediante la incorporación de las propiedades de escala y de dependencia temporales, PhD Thesis, Universitat Politècnica de València, 2001 (in Spanish).Walesh, S. G., Lau, D. H., and Liebman, M. D.: Statistically based use of event models. Proceedings of the International Symposium on Urban Storm Runoff, University of Kentucky, Lexington, 75–81, 1979.Watt, E. and Marsalek, J.: Critical review of the evolution of design storm event concept, Can. J. Civil. Eng., 40, 105–113, https://doi.org/10.1139/cjce-2011-0594, 2013

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. Stochastic Environmental Research and Risk Assessment, 32(9), 2515-2525. doi:10.1007/s00477-018-1550-0Goodarzi, E., Mirzaei, M., Shui, L. T., & Ziaei, M. (2011). Evaluation dam overtopping risk based on univariate and bivariate flood frequency analysis. Hydrology and Earth System Sciences Discussions, 8(6), 9757-9796. doi:10.5194/hessd-8-9757-2011Volpi, E., & Fiori, A. (2012). Design event selection in bivariate hydrological frequency analysis. Hydrological Sciences Journal, 57(8), 1506-1515. doi:10.1080/02626667.2012.726357Rizwan, M., Guo, S., Yin, J., & Xiong, F. (2019). Deriving Design Flood Hydrographs Based on Copula Function: A Case Study in Pakistan. Water, 11(8), 1531. doi:10.3390/w11081531Aranda, J., & 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. doi:10.3390/w10111570Waylen, P., & Woo, M. (1982). Prediction of annual floods generated by mixed processes. Water Resources Research, 18(4), 1283-1286. doi:10.1029/wr018i004p01283Villarini, G., & Smith, J. A. (2010). Flood peak distributions for the eastern United States. Water Resources Research, 46(6). doi:10.1029/2009wr008395Smith, J. A., Villarini, G., & Baeck, M. L. (2011). Mixture Distributions and the Hydroclimatology of Extreme Rainfall and Flooding in the Eastern United States. Journal of Hydrometeorology, 12(2), 294-309. doi:10.1175/2010jhm1242.1Strupczewski, W. G., Kochanek, K., Bogdanowicz, E., & Markiewicz, I. (2011). On seasonal approach to flood frequency modelling. Part I: Two-component distribution revisited. Hydrological Processes, 26(5), 705-716. doi:10.1002/hyp.8179Iacobellis, V., Fiorentino, M., Gioia, A., & Manfreda, S. (2010). Best Fit and Selection of Theoretical Flood Frequency Distributions Based on Different Runoff Generation Mechanisms. Water, 2(2), 239-256. doi:10.3390/w2020239Michele, C. D., & Salvadori, G. (2002). On the derived flood frequency distribution: analytical formulation and the influence of antecedent soil moisture condition. Journal of Hydrology, 262(1-4), 245-258. doi:10.1016/s0022-1694(02)00025-2Yan, L., Xiong, L., Ruan, G., Xu, C.-Y., Yan, P., & Liu, P. (2019). Reducing uncertainty of design floods of two-component mixture distributions by utilizing flood timescale to classify flood types in seasonally snow covered region. Journal of Hydrology, 574, 588-608. doi:10.1016/j.jhydrol.2019.04.056Lang, M., Ouarda, T. B. M. J., & Bobée, B. (1999). Towards operational guidelines for over-threshold modeling. Journal of Hydrology, 225(3-4), 103-117. doi:10.1016/s0022-1694(99)00167-5Ferreira, A., & de Haan, L. (2015). On the block maxima method in extreme value theory: PWM estimators. The Annals of Statistics, 43(1), 276-298. doi:10.1214/14-aos1280Dupuis, D. J. (1996). Estimating the probability of obtaining nonfeasible parameter estimates of the generalized pareto distribution. Journal of Statistical Computation and Simulation, 54(1-3), 197-209. doi:10.1080/00949659608811728Hosking, J. R. M., Wallis, J. R., & Wood, E. F. (1985). Estimation of the Generalized Extreme-Value Distribution by the Method of Probability-Weighted Moments. Technometrics, 27(3), 251-261. doi:10.1080/00401706.1985.10488049Serinaldi, F. (2007). Analysis of inter-gauge dependence by Kendall’s τK, upper tail dependence coefficient, and 2-copulas with application to rainfall fields. Stochastic Environmental Research and Risk Assessment, 22(6), 671-688. doi:10.1007/s00477-007-0176-4Dupuis, D. J. (2007). Using Copulas in Hydrology: Benefits, Cautions, and Issues. Journal of Hydrologic Engineering, 12(4), 381-393. doi:10.1061/(asce)1084-0699(2007)12:4(381)Genest, C., & Rémillard, B. (2008). Validity of the parametric bootstrap for goodness-of-fit testing in semiparametric models. Annales de l’Institut Henri Poincaré, Probabilités et Statistiques, 44(6), 1096-1127. doi:10.1214/07-aihp148Genest, C., Kojadinovic, I., Nešlehová, J., & Yan, J. (2011). A goodness-of-fit test for bivariate extreme-value copulas. Bernoulli, 17(1), 253-275. doi:10.3150/10-bej279Caperaa, P. (1997). A nonparametric estimation procedure for bivariate extreme value copulas. Biometrika, 84(3), 567-577. doi:10.1093/biomet/84.3.567Bhunya, P. K., Berndtsson, R., Ojha, C. S. P., & Mishra, S. K. (2007). Suitability of Gamma, Chi-square, Weibull, and Beta distributions as synthetic unit hydrographs. Journal of Hydrology, 334(1-2), 28-38. doi:10.1016/j.jhydrol.2006.09.022Nadarajah, S. (2007). Probability models for unit hydrograph derivation. Journal of Hydrology, 344(3-4), 185-189. doi:10.1016/j.jhydrol.2007.07.004Carvajal, C., Peyras, L., Arnaud, P., Boissier, D., & Royet, P. (2009). Probabilistic Modeling of Floodwater Level for Dam Reservoirs. Journal of Hydrologic Engineering, 14(3), 223-232. doi:10.1061/(asce)1084-0699(2009)14:3(223)Yevdjevich, V. M. (1959). Analytical integration of the differential equation for water storage. Journal of Research of the National Bureau of Standards Section B Mathematics and Mathematical Physics, 63B(1), 43. doi:10.6028/jres.063b.007Gioia, A. (2016). Reservoir Routing on Double-Peak Design Flood. Water, 8(12), 553. doi:10.3390/w8120553Rong, Zhang, Peng, & Feng. (2019). Three-Dimensional Numerical Simulation of Dam Discharge and Flood Routing in Wudu Reservoir. Water, 11(10), 2157. doi:10.3390/w11102157Todorovic, P., & Rousselle, J. (1971). Some Problems of Flood Analysis. Water Resources Research, 7(5), 1144-1150. doi:10.1029/wr007i005p01144Blöschl, G., Hall, J., Parajka, J., Perdigão, R. A. P., Merz, B., Arheimer, B., … Živković, N. (2017). Changing climate shifts timing of European floods. Science, 357(6351), 588-590. doi:10.1126/science.aan2506Alfieri, L., Burek, P., Feyen, L., & Forzieri, G. (2015). Global warming increases the frequency of river floods in Europe. Hydrology and Earth System Sciences, 19(5), 2247-2260. doi:10.5194/hess-19-2247-2015Soriano, E., Mediero, L., & Garijo, C. (2018). Selection of Bias Correction Methods to Assess the Impact of Climate Change on Flood Frequency Curves. Proceedings, 7(1), 14. doi:10.3390/ecws-3-0580

Multivariate synthetic streamflow generation using a hybrid model based on artificial neural networks

International audienceA model for multivariate streamflow generation is presented, based on a multilayer feedforward neural network. The structure of the model results from two components, the neural network (NN) deterministic component and a random component which is assumed to be normally distributed. It is from this second component that the model achieves the ability to incorporate effectively the uncertainty associated with hydrological processes, making it valuable as a practical tool for synthetic generation of streamflow series. The NN topology and the corresponding analytical explicit formulation of the model are described in detail. The model is calibrated with a series of monthly inflows to two reservoir sites located in the Tagus River basin (Spain), while validation is performed through estimation of a set of statistics that is relevant for water resources systems planning and management. Among others, drought and storage statistics are computed and compared for both the synthetic and historical series. The performance of the NN-based model was compared to that of a standard autoregressive AR(2) model. Results show that NN represents a promising modelling alternative for simulation purposes, with interesting potential in the context of water resources systems management and optimisation. Keywords: neural networks, perceptron multilayer, error backpropagation, hydrological scenario generation, multivariate time-series.

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

Climate and hydrological variability: the catchment filtering role

Abstract. Measuring the impact of climate change on flood frequency is a complex and controversial task. Identifying hydrological changes is difficult given the factors, other than climate variability, which lead to significant variations in runoff series. The catchment filtering role is often overlooked and thus may hinder the correct identification of climate variability signatures on hydrological processes. Does climate variability necessarily imply hydrological variability? This research aims to analytically derive the flood frequency distribution based on realistic hypotheses about the rainfall process and the rainfall–runoff transformation. The annual maximum peak flow probability distribution is analytically derived to quantify the filtering effect of the rainfall–runoff process on climate change. A sensitivity analysis is performed according to typical semi-arid Mediterranean climatic and hydrological conditions, assuming a simple but common scheme for the rainfall–runoff transformation in small-size ungauged catchments, i.e. the CN-SCS model. Variability in annual maximum peak flows and its statistical significance are analysed when changes in the climatic input are introduced. Results show that depending on changes in the annual number of rainfall events, the catchment filtering role is particularly significant, especially when the event rainfall volume distribution is not strongly skewed. Results largely depend on the return period: for large return periods, peak flow variability is significantly affected by the climatic input, while for lower return periods, infiltration processes smooth out the impact of climate change

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

Multivariate synthetic streamflow generation using a hybrid model based on artificial neural networks

A model for multivariate streamflow generation is presented, based on a multilayer feedforward neural network. The structure of the model results from two components, the neural network (NN) deterministic component and a random component which is assumed to be normally distributed. It is from this second component that the model achieves the ability to incorporate effectively the uncertainty associated with hydrological processes, making it valuable as a practical tool for synthetic generation of streamflow series. The NN topology and the corresponding analytical explicit formulation of the model are described in detail. The model is calibrated with a series of monthly inflows to two reservoir sites located in the Tagus River basin (Spain), while validation is performed through estimation of a set of statistics that is relevant for water resources systems planning and management. Among others, drought and storage statistics are computed and compared for both the synthetic and historical series. The performance of the NN-based model was compared to that of a standard autoregressive AR(2) model. Results show that NN represents a promising modelling alternative for simulation purposes, with interesting potential in the context of water resources systems management and optimisation.</p> <p style='line-height: 20px;'><b>Keywords: </b>neural networks, perceptron multilayer, error backpropagation, hydrological scenario generation, multivariate time-series.</p>

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

Ensayo de cultivares de alcachofa (Cynara scolymus l.) en dos ciclos de cultivo

La aparición en el mercado de nuevos cultivares de alcachofa reproducidos mediante semilla hace necesario el análisis de las características agronómicas de cada uno de ellos, así como la adaptación de estos materiales a nuestra zona de cultivo. En este trabajo estudiamos 10 cultivares de alcachofa procedentes de semilla por campaña, durante dos campañas, 2018-19 y 2019-20. De estos, 7 cultivares se repitieron en los dos ensayos y tres se cambiaron, por lo que finalmente analizamos un total de 13 cultivares diferentes. Como testigos empleamos Blanca de Tudela y Calicó, multiplicados por zueca, que son los cultivares empleados tradicionalmente por nuestros agricultores. También estudiamos la influencia que ejerce sobre su ciclo productivo la aplicación de ácido giberélico (AG3), ya que sabemos por trabajos previos que sin el empleo de dicha hormona se retrasa mucho la entrada en producción. Para este trabajo diseñamos un ensayo de bloques al azar con dos repeticiones por tratamiento con aplicación de ácido giberélico y otras dos repeticiones sin tratamiento como testigo

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