Metodología basada en generadores meteorológicos para la estimación de avenidas extremas

C. Beneyto, J. A. Aranda, G. Benito, Félix Francés García

Resumen

Una adecuada caracterización de las avenidas extremas es clave para el correcto diseño de las infraestructuras y la estimación del riesgo de inundación de una determinada área. Sin embargo, la escasa longitud de los registros pluviométricos y de aforos unido con la baja probabilidad de ocurrencia de este tipo de eventos hace que, a día de hoy, su adecuada estimación presente todavía grandes dificultades. Este trabajo presenta una metodología para la estimación de las avenidas extremas mediante la generación continua de series de precipitación a través de generadores meteorológicos y la integración de información de varios tipos (sistemática y no sistemática). Los resultados obtenidos en el caso de estudio, la Rambla de la Viuda, indican que el uso conjunto de series sintéticas continúas generadas mediante un generador meteorológico estocástico, un modelo hidrológico y la integración de registros sistemáticos y no sistemáticos reduce la incertidumbre de la estimación de avenidas extremas.


Palabras clave

generador meteorológico estocástico; extremos; modelo hidrológico; paleocrecidas

Texto completo:

PDF

Referencias

Ballesteros-Cánovas, J.A., Sanchez-Silva, M., Bodoque, J.M., Díez-Herrero, A. 2013. An Integrated Approach to Flood Risk Management: A Case Study of Navaluenga (Central Spain). Water Resources Management, 27, 3051-3069. https://doi.org/10.1007/s11269-013-0332-1

Benito, G., Lang, M., Barriendos, M., Llasat, M.C., Francés, F., Ouarda, T., Thorndycraft, V., Enzel, Y., Bardossy, A., Coeur, D., Bobée, B. 2004. Use of Systematic, Palaeoflood and Historical Data for the Improvement of Flood Risk Estimation. Review of Scientific Methods. Natural Hazards, 31, 623-643. https://doi.org/10.1023/B:NHAZ.0000024895.48463.eb

Blazkova, 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, 153-172. https://doi.org/10.1016/j.jhydrol.2003.12.025

Brocca, L., Liersch, S., Melone, F., Moramarco, T., Volk, M. 2013. Application of a model-based rainfall-runoff database as efficient tool for flood risk management. Hydrology and Earth System Sciences, 17, 3159-3169. https://doi.org/10.5194/hess-17-3159-2013

Burton, A., Kilsby, C.G., Fowler, H.J., Cowpertwait, P.S.P., O’Connell, P.E. 2008. RainSim: A spatial-temporal stochastic rainfall modelling system. Environmental Modelling and Software, 23, 1356-1369. https://doi.org/10.1016/j.envsoft.2008.04.003

Camarasa Belmonte, A.M., Segura Beltrán, F. 2001. Flood events in Mediterranean ephemeral streams (ramblas) in Valencia region, Spain. Catena, 45, 229-249. https://doi.org/10.1016/S0341-8162(01)00146-1

Candela, A., Brigandì, G., Aronica, G.T. 2014. Estimation of synthetic flood design hydrographs using a distributed rainfall-runoff model coupled with a copula-based single storm rainfall generator. Natural Hazards and Earth System Sciences, 14, 1819-1833. https://doi.org/10.5194/nhess-14-1819-2014

Caron, A., Leconte, R., Brissette, F. 2009. An Improved Stochastic Weather Generator for Hydrological Impact Studies. Canadian Water Resources Journal, 33, 233-256. https://doi.org/10.4296/cwrj3303233

Cavanaugh, N.R., Gershunov, A., Panorska, A.K., Kozubowski, T.J. 2015. On the Probability Distribution of Daily Precipitation Extremes. Geophysical Research Letters, 42, 1560-1567. https://doi.org/10.1002/2015GL063238

CEDEX, 2011. Mapa de Caudales Máximos. Memoria Técnica. Madrid (España).

Chen, J., Brissette, F.P., Leconte, R. 2010. A daily stochastic weather generator for preserving low-frequency of climate variability. Journal of Hydrology, 388, 480-490. https://doi.org/10.1016/j.jhydrol.2010.05.032

Chen, J., Brissette, F.P., Leconte, R. 2012. WeaGETS – a Matlab-based daily scale weather generator for generating precipitation and temperature. Procedia Environmental Sciences 13, 2222-2235. https://doi.org/10.1016/j.proenv.2012.01.211

Chen, J., Brissette, F., Zhang, X.J. 2014. Multi-Site Stochastic Weather Generator for Daily Precipitation and Temperature. Transactions of the ASABE, 57, 1375-1391. https://doi.org/10.13031/trans.57.10685

Chen, B., Krajewski, W.F., Liu, F., Fang, W., Xu, Z. 2017. Estimating instantaneous peak flow from mean daily flow. Hydrology Research, 48, 1474-1488. https://doi.org/10.2166/nh.2017.200

Cowpertwait, P., Ocio, D., Collazos, G., De Cos, O., Stocker, C. 2013. Regionalised spatiotemporal rainfall and temperature models for flood studies in the Basque Country, Spain. Hydrology and Earth System Sciences, 17, 479-494. https://doi.org/10.5194/hess-17-479-2013

Cowpertwait, P.S.P., O’Connell, P.E., Metcalfe, A. V., Mawdsley, J.A. 1996. Stochastic point process modelling of rainfall. II. Regionalisation and disaggregation. Journal of Hydrology, 175, 47-65. https://doi.org/10.1016/S0022-1694(96)80005-9

Cunnane, C. 1978. Unbiased plotting positions—a review. Journal of Hydrology, 37, 205-222. https://doi.org/10.1016/0022-1694(78)90017-3

de Andrés Conde, C., González Vallvé, J.L., Centeno Gutiérrez, S. 2019. Los sistemas automáticos de información hidrológica (SAIH) una innovación que se exporta. Revista Digital del Cedex, 101-106.

Devia, G.K., Ganasri, B.P., Dwarakish, G.S. 2015. A Review on Hydrological Models. Aquatic Procedia, 4, 1001-1007. https://doi.org/10.1016/j.aqpro.2015.02.126

England, J.F., Godaire, J.E., Klinger, R.E., Bauer, T.R., Julien, P.Y. 2010. Paleohydrologic bounds and extreme flood frequency of the Upper Arkansas River, Colorado, USA. Geomorphology, 124, 1-16. https://doi.org/10.1016/j.geomorph.2010.07.021

England, J.F., Julien, P.Y., Velleux, M.L. 2014. Physically-based extreme flood frequency with stochastic storm transposition and paleoflood data on large watersheds. Journal of Hydrology, 510, 228-245. https://doi.org/10.1016/j.jhydrol.2013.12.021

Evin, G., Favre, A.C., Hingray, B. 2018. Stochastic generation of multi-site daily precipitation focusing on extreme events. Hydrology and Earth System Sciences, 22, 655-672. https://doi.org/10.5194/hess-22-655-2018

Fathzadeh, A., Jaydari, A., Taghizadeh-Mehrjardi, R. 2017. Comparison of different methods for reconstruction of instantaneous peak flow data. Intelligent Automation and Soft Computing, 23, 41-49. https://doi.org/10.1080/10798587.2015.1120991

Fatichi, S., Ivanov, V.Y., Caporali, E. 2011. Simulation of future climate scenarios with a weather generator. Advances in Water Resources, 34, 448-467. https://doi.org/10.1016/j.advwatres.2010.12.013

Flores-Montoya, I., Sordo-Ward, Á., Mediero, L., Garrote, L. 2016. Fully stochastic distributed methodology for multivariate flood frequency analysis. Water (Switzerland), 8. https://doi.org/10.3390/w8060225

Foufoula-Georgiou, E. 1989. A probabilistic storm transposition approach for estimating exceedance probabilities of extreme precipitation depths. Water Resources Research, 25, 799-815. https://doi.org/10.1029/WR025i005p00799

Francés, F. 1998. Using the TCEV distribution function with systematic and non-systematic data in a regional flood frequency analysis. Stochastic Hydrology and Hydraulics, 12, 267-283. https://doi.org/10.1007/s004770050021

Francés, F., Salas, J.D., Boes, D.C. 1994. Flood frequency analysis with systematic and historical or paleoflood data based on the two-parameter general extreme value models. Water Resources Research, 30, 1653-1664. https://doi.org/10.1029/94WR00154

Francés, F., Vélez, J.I., Vélez, J.J. 2007. Split-parameter structure for the automatic calibration of distributed hydrological models. Journal of Hydrology, 332, 226-240. https://doi.org/10.1016/j.jhydrol.2006.06.032

Fuller, W. 1914. Flood flows. Transactions of the American Society of Civil Engineers, 77, 564-617.

Furrer, E.M., Katz, R.W. 2008. Improving the simulation of extreme precipitation events by stochastic weather generators. Water Resources Research, 44, 1-13. https://doi.org/10.1029/2008WR007316

Haberlandt, U., Hundecha, Y., Pahlow, M., Schumann, A.H. 2011. Rainfall Generators for Application in Flood Studies. En: Schumann, A.H. (Ed.), Flood Risk Assessment and Management: How to Specify Hydrological Loads, Their Consequences and Uncertainties. Springer Netherlands, Dordrecht, pp. 117-147. https://doi.org/10.1007/978-90-481-9917-4_7

Hargreaves, G., Samani, Z. 1985. Reference Crop Evapotranspiration from Temperature. Applied Engineering in Agriculture, 1, 96-99. https://doi.org/10.13031/2013.26773

Herrera, S., Fernández, J., Gutiérrez, J.M. 2016. Update of the Spain02 gridded observational dataset for EURO-CORDEX evaluation: Assessing the effect of the interpolation methodology. International Journal of Climatology, 36, 900-908. https://doi.org/10.1002/joc.4391

Jimeno-Sáez, P., Senent-Aparicio, J., Pérez-Sánchez, J., Pulido-Velazquez, D., Cecilia, J. 2017. Estimation of Instantaneous Peak Flow Using Machine-Learning Models and Empirical Formula in Peninsular Spain. Water, 9, 347. https://doi.org/10.3390/w9050347

Kay, A.L., Reynard, N.S., Jones, R.G. 2006. RCM rainfall for UK flood frequency estimation. I. Method and validation. Journal of Hydrology, 318, 151-162. https://doi.org/10.1016/j.jhydrol.2005.06.012

Khalili, M., Brissette, F., Leconte, R. 2011. Effectiveness of Multi-Site Weather Generator for Hydrological Modeling. Journal of the American Water Resources Association, 47, 303-314. https://doi.org/10.1111/j.1752-1688.2010.00514.x

Lam, D., Thompson, C., Croke, J., Sharma, A., Macklin, M. 2017. Reducing uncertainty with flood frequency analysis: The contribution of paleoflood and historical flood information. Water Resources Research, 53, 2312-2327. https://doi.org/10.1002/2016WR019959

Linsley, R.., Kohler, M.., Paulhus, J.L.. 1968. Applied hydrology. Journal of Hydrology, 6, 224-225. https://doi.org/10.1016/0022-1694(68)90169-8

Machado, M.J., Medialdea, A., Calle, M., Rico, M.T., Sánchez-Moya, Y., Sopeña, A., Benito, G. 2017. Historical palaeohydrology and landscape resilience of a Mediterranean rambla (Castellón, NE Spain): Floods and people. Quaternary Science Reviews, 171, 182-198. https://doi.org/10.1016/j.quascirev.2017.07.014

Mateu, J.F. 1974. La Rambla de la Viuda. Clima e Hidrología. Cuadernos de Geografia.

Mckague, K., Rudra, R., Ogilvie, J. 2003. CLIMGEN - a convenient weather generation tool for canadian climate stations 26.

Mehan, S., Guo, T., Gitau, M., Flanagan, D.C. 2017. Comparative Study of Different Stochastic Weather Generators for Long-Term Climate Data Simulation. Climate, 5, 26. https://doi.org/10.3390/cli5020026

Ministerio de Fomento. Dirección General de Carreteras, 1999. Máximas lluvias diarias en España Peninsular, Serie monográfica.

Montalvo, C., Francés, F. 2017. Análisis integral del impacto del Cambio Climático en los regímenes de agua, crecidas y sedimentos de una rambla mediterránea. Ingeniería del agua, 21, 263-272. https://doi.org/10.4995/ia.2017.8775

Montes, J., Álvarez, M., Pertierra, L., Moralo, J., Baztán, J. 2018. Análisis regional de frecuencia de avenidas en la vertiente cantábrica y noratlántica de España. Ingeniería del agua, 22, 93-107. https://doi.org/10.4995/ia.2018.8782

Nicks, A.D., Gander, L.J. 1995. Weather generator. En: In USDA-Water Erosion Prediction Project: Hillslope Profile and Watershed Model Documentation. West Lafayette.

Nicks, A.D., Williams, R.D., Gander, G.A. 1994. Estimating the impacts of global change on erosion with stochastically generated climate data and erosion models.

Racsko, P., Szeidl, L., Semenov, M. 1991. A serial approach to local stochastic weather models. Ecological Modelling, 57, 27-41. https://doi.org/10.1016/0304-3800(91)90053-4

Richardson, C.W. 1981. Stochastic modelling of daily precipitation, temperature and solar radiation. Water Resources Research, 17, 182-190. https://doi.org/10.1029/WR017i001p00182

Richardson, C.W., Wright, D.A. 1984. WGEN: A model for generating daily weather variables. U.S. Department of Agriculture Research and Service, ARS, 8, 235.

Sangal, B.P. 1983. Practical Method of Estimating Peak Flow. Journal of Hydraulic Engineering, 109, 549-563. https://doi.org/10.1061/(ASCE)0733-9429(1983)109:4(549)

Semenov, M. 2002. LARS-WG: A stochastic weather generator for use in climate impact studies. User Manual: Hertfordshire, … 28.

Semenov, M.A., Barrow, E.M. 1997. Use of a stochastic weather generator in the development of climate change scenarios. Climatic Change, 35, 397-414. https://doi.org/10.1023/A:1005342632279

Singh, V.P., Strupczewski, W.G. 2002. On the status of flood frequency analysis. Hydrological Processes, 16, 3737-3740. https://doi.org/10.1002/hyp.5083

Sordo-Ward, A., Garrote, L., Bejarano, M.D., Castillo, L.G. 2013. Extreme flood abatement in large dams with gate-controlled spillways. Journal of Hydrology, 498, 113-123. https://doi.org/10.1016/j.jhydrol.2013.06.010

Stöckle, C.O., Campbell, G.S., Nelson, R. 1999. Climgen Manual. Biological Systems Engineering Department, Washington State University, Pullman,WA, USA.

Taguas, E.V., Ayuso, J.L., Pena, A., Yuan, Y., Sanchez, M.C., Giraldez, J.V., Pérez, R. 2008. Testing the relationship between instantaneous peak flow and mean daily flow in a Mediterranean Area Southeast Spain. CATENA, 75, 129-137. https://doi.org/10.1016/j.catena.2008.04.015

Taylor, R.H., Wilson, P.R. 1990. Recent increase and southern expansion of Adelie Penguin populations in the Ross Sea, Antarctica, related to climatic warming. New Zealand Journal of Ecology, 14, 25-29.

Tucci, C., Silva, E. 2016. Relação entre vazões máximas diária e instantânea. Revista Brasileira de Recursos Hídricos, 3, 133-151. https://doi.org/10.21168/rbrh.v3n1.p133-151

Vašková, I., Francés, F., Vélez, J.J. 2004. Empleo de la modelación distribuida en el estudio de los recursos hídricos del País Vasco. En: 4a Asamblea Hispano-Portuguesa de Geodesia y Geofísica. Da Foz, Portugal.

Vélez, J.J., López Unzu, F., Puricelli, M., Francés, F. 2007. Parameter extrapolation to ungauged basins with a hydrological distributed model in a regional framework. Hydrology and Earth System Sciences Discussions, 4, 909-956. https://doi.org/10.5194/hessd-4-909-2007

Verdin, A., Rajagopalan, B., Kleiber, W., Katz, R.W. 2015. Coupled stochastic weather generation using spatial and generalized linear models. Stochastic Environmental Research and Risk Assessment, 29, 347-356. https://doi.org/10.1007/s00477-014-0911-6

Wallis, J.R., Heights, Y. 1986. The Value of Historical Data in Flood Frequency Analysis. Water Resources Research, 22, 1606-1612. https://doi.org/10.1029/WR022i011p01606

Wilks, D.S., Wilby, R.L. 1999. The weather generation game: a review of stochastic weather models. Progress in Physical Geography, 23, 329-357. https://doi.org/10.1191/030913399666525256

Wilson, L.L., Foufoula-Georgiou, E. 2007. Regional Rainfall Frequency Analysis via Stochastic Storm Transposition. Journal of Hydraulic Engineering, 116, 859-880. https://doi.org/10.1061/(ASCE)0733-9429(1990)116:7(859)

Abstract Views

1464
Metrics Loading ...

Metrics powered by PLOS ALM


 

Citado por (artículos incluidos en Crossref)

This journal is a Crossref Cited-by Linking member. This list shows the references that citing the article automatically, if there are. For more information about the system please visit Crossref site

1. Probabilistic Model for Real-Time Flood Operation of a Dam Based on a Deterministic Optimization Model
Víctor Cuevas-Velásquez, Alvaro Sordo-Ward, Jaime H. García-Palacios, Paola Bianucci, Luis Garrote
Water  vol: 12  num.: 11  primera página: 3206  año: 2020  
doi: 10.3390/w12113206



Esta revista se publica bajo una licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional.

Universitat Politècnica de València

Fundación para el Fomento de la Ingeniería del Agua

e-ISSN: 1886-4996  ISSN: 1134-2196

https://doi.org/10.4995/ia