Metodología basada en generadores meteorológicos para la estimación de avenidas extremas
DOI:
https://doi.org/10.4995/ia.2019.12153Palabras clave:
generador meteorológico estocástico, extremos, modelo hidrológico, paleocrecidasResumen
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.
Descargas
Citas
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)
Descargas
Publicado
Cómo citar
Número
Sección
Licencia
Esta revista se publica bajo una licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional