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MERLIN: Una nueva herramienta para la predicción del riesgo de inundaciones en la demarcación hidrográfica Galicia-Costa

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MERLIN: Una nueva herramienta para la predicción del riesgo de inundaciones en la demarcación hidrográfica Galicia-Costa

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Fraga, I.; Cea, L.; Puertas, J.; Mosqueira, G.; Quinteiro, B.; Botana, S.; Fernández, L.... (2021). MERLIN: Una nueva herramienta para la predicción del riesgo de inundaciones en la demarcación hidrográfica Galicia-Costa. Ingeniería del agua. 25(3):215-227. https://doi.org/10.4995/ia.2021.15565

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/170995

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Título: MERLIN: Una nueva herramienta para la predicción del riesgo de inundaciones en la demarcación hidrográfica Galicia-Costa
Otro titulo: MERLIN: A new tool for flood hazard forecasting at the Galicia-Costa Hydrographic Demarcation
Autor: Fraga, Ignacio Cea, Luis Puertas, Jerónimo Mosqueira, Gonzalo Quinteiro, Belén Botana, Sonia Fernández, Laura Salsón, Santiago Fernández-García, Guillermo Taboada, Juan
Fecha difusión:
Resumen:
[EN] This article presents MERLIN, a tool for flood hazard evaluation, which forecasts discharges and water depths in flood prone areas of the Galicia Costa district. The warning system operates in two stages. During the ...[+]


[ES] Este artículo presenta MERLIN, una nueva herramienta para estimar el riesgo de inundaciones a partir de predicciones de caudales y calados en Áreas de Riesgo Potencial Significativo de Inundaciones (ARPSIS) de la ...[+]
Palabras clave: Inundación , Predicción , Gestión de riesgo de inundación , Floods , Early warning system , Forecasting , Flood risk management
Derechos de uso: Reconocimiento - No comercial - Compartir igual (by-nc-sa)
Fuente:
Ingeniería del agua. (issn: 1134-2196 ) (eissn: 1886-4996 )
DOI: 10.4995/ia.2021.15565
Editorial:
Universitat Politècnica de València
Versión del editor: https://doi.org/10.4995/ia.2021.15565
Agradecimientos:
El desarrollo del sistema MERLIN y el resto de trabajos presentados en este artículo fue posible gracias a la financiación aportada por Augas de Galicia dentro del Convenio de colaboración entre Augas de Galicia e a Fundación ...[+]
Tipo: Artículo

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References

Alvarez-Garreton, C., Ryu, D., Western, A.W., Su, C.H., Crow, W.T., Robertson, E., Leahy, C. 2015. Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: Comparison between lumped and semi-distributed schemes. Hydrology and Earth System Sciences, 19(4), 1659-1676. https://doi.org/10.5194/hess-19-1659-2015

Arnell, N.W., Gosling, S.N. 2016. The impacts of climate change on river flood risk at the global scale. Climatic Change, 134(3), 387-401. https://doi.org/10.1007/s10584-014-1084-5

Bennett, T.H., Peters, J.C. 2000. Continuous soil moisture accounting in the hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS). Building partnerships, 1-10. [+]
Alvarez-Garreton, C., Ryu, D., Western, A.W., Su, C.H., Crow, W.T., Robertson, E., Leahy, C. 2015. Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: Comparison between lumped and semi-distributed schemes. Hydrology and Earth System Sciences, 19(4), 1659-1676. https://doi.org/10.5194/hess-19-1659-2015

Arnell, N.W., Gosling, S.N. 2016. The impacts of climate change on river flood risk at the global scale. Climatic Change, 134(3), 387-401. https://doi.org/10.1007/s10584-014-1084-5

Bennett, T.H., Peters, J.C. 2000. Continuous soil moisture accounting in the hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS). Building partnerships, 1-10.

Berghuijs, W.R., Aalbers, E.E., Larsen, J.R., Trancoso, R., Woods, R.A. 2017. Recent changes in extreme floods across multiple continents. Environmental Research Letters, 12(11), 114035. https://doi.org/10.1088/1748-9326/aa8847

Bladé, E., Cea, L., Corestein, G., Escolano, E., Puertas, J., Vázquez-Cendón, E., Dolz, J., Coll, A. 2014. Iber: herramienta de simulación numérica del flujo en ríos. Revista Internacional de Metodos Numericos en Ingeniería, 30(1), 1-10. https://doi.org/10.1016/j.rimni.2012.07.004

Carracedo, P. 2003. Acoplamiento de un modelo hidrodinámico de escala global con uno de escala regional para Galicia. Revista Real Academia Galega de Ciencias, 22, 85.

Cea, L., Fraga, I. 2018. Incorporating antecedent moisture conditions and intraevent variability of rainfall on flood frequency analysis in poorly gauged basins. Water Resources Research, 54, 8774-8791. https://doi.org/10.1029/2018WR023194

Cronshey, R. 1986. Urban hydrology for small watersheds. US Department of Agriculture Soil Conservation Service Engineering Division.

García-Feal, O., González-Cao, J., Gómez-Gesteira, M., Cea, L., Domínguez, J., Formella, A. 2018. An accelerated tool for flood modelling based on Iber. Water, 10(10) 1459. https://doi.org/10.3390/w10101459

Hossain, F., Siddique-E-Akbor, A.H.M., Yigzaw, W., Shah-Newaz, S., Hossain, M., Mazumder, L.C., Turk, F.J. 2014. Crossing the "valley of death": lessons learned from implementing an operational satellite-based flood forecasting system. Bulletin of the American Meteorological Society, 95(8), 1201-1207. https://doi.org/10.1175/BAMS-D-13-00176.1

IPCC (2018). Global warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways in the context of strengthening the global response to the threat of climate change sustainable development and efforts to eradicate poverty. In Press.

Jewell, S.A., Gaussiat, N. 2015. An assessment of kriging-based rain-gauge-radar merging techniques. Quarterly Journal of the Royal Meteorological Society, 141(691), 2300-2313. https://doi.org/10.1002/qj.2522

Kasiviswanathan, K.S., He, J., Sudheer, K.P., Tay, J.H. 2016. Potential application of wavelet neural network ensemble to forecast streamflow for flood management. Journal of hydrology, 536, 161-173. https://doi.org/10.1016/j.jhydrol.2016.02.044

Kellens, W., Vanneuville, W., Verfaillie, E., Meire, E., Deckers, P., De Maeyer, P. 2013. Flood risk management in Flanders: past developments and future challenges. Water Resources Management, 27(10), 3585-3606. https://doi.org/10.1007/s11269-013-0366-4

Krajewski, W.F., Ceynar, D., Demir, I., Goska, R., Kruger, A., Langel, C., Small, S.J. 2017. Real-time flood forecasting and information system for the state of Iowa. Bulletin of the American Meteorological Society, 98(3), 539-554. https://doi.org/10.1175/BAMS-D-15-00243.1

Kumar, M., Sahay, R.R. 2018. Wavelet-genetic programming conjunction model for flood forecasting in rivers. Hydrology Research, 49(6), 1880-1889. https://doi.org/10.2166/nh.2018.183

Massari, C., Brocca, L., Tarpanelli, A., Moramarco, T. 2015. Data assimilation of satellite soil moisture into rainfall-runoff modelling: A complex recipe?. Remote Sensing, 7(9), 11403-11433. https://doi.org/10.3390/rs70911403

McKay, M.D., Beckman, R.J., Conover, W.J. 1979 A Comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics, 21(2), 239-245.

Mure-Ravaud, M., Binet, G., Bracq, M., Perarnaud, J.J., Fradin, A., Litrico, X. 2016. A web based tool for operational realtime flood forecasting using data assimilation to update hydraulic states. Environmental Modelling and Software, 84, 35-49. https://doi.org/10.1016/j.envsoft.2016.06.002

Naranjo, L., Taboada, J.J., Lage, A., Salsón, S., Montero, P., Souto, J.A., Pérez-Muñuzuri, V. 2001. Estudio de las anómalas condiciones meteorológicas sobre Galicia durante el otoño de los años 2000 y 2001. Revista Real Academia Galega de Ciencias, 20, 113-133

Nguyen, P., Thorstensen, A., Sorooshian, S., Hsu, K., AghaKouchak, A., Sanders, B., Koren, V., Cui, Z., Smith, M. 2016. A high resolution coupled hydrologic-hydraulic model (HiResFlood-UCI) for flash flood modeling. Journal of Hydrology, 541, 401-420. https://doi.org/10.1016/j.jhydrol.2015.10.047

Razmkhah, H. 2016. Comparing performance of different loss methods in rainfall-runoff modeling. Water resources, 43(1), 207-224. https://doi.org/10.1134/S0097807816120058

Rosburg, T.T., Nelson, P.A., Bledsoe, B.P. 2017. Effects of urbanization on flow duration and stream flashiness: a case study of Puget Sound streams, western Washington, USA. Journal of the American Water Resources Association, 53(2), 493-507. https://doi.org/10.1111/1752-1688.12511

Sanz-Ramos, M., Amengual, A., Bladé i Castellet, E., Romero, R., Roux, H. 2018. Flood forecasting using a coupled hydrological and hydraulic model (based on FVM) and highresolution meteorological model. Proceedings of River Flow 2018-Ninth International Conference on Fluvial Hydraulics (pp. 1-8) Lyon France. https://doi.org/10.1051/e3sconf/20184006028

Scharffenberg, W.A, Fleming, M.J. 2006. Hydrologic modeling system HEC-HMS: User's manual. US Army Corps of Engineers Hydrologic Engineering Center.

Shchepetkin, A.F., McWilliams, J.C. 2005. The regional oceanic modeling system (ROMS): a split-explicit free-surface topographyfollowing-coordinate oceanic model. Ocean Modelling, 9(4), 347-404. https://doi.org/10.1016/j.ocemod.2004.08.002

Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Barker, D.M., Wang, W., Powers, J.G. 2008. A description of the Advanced Research WRF version 3. NCAR Technical note-475+ STR.

Sopelana, J., Cea, L., Ruano, S. 2018. A continuous simulation approach for the estimation of extreme flood inundation in coastal river reaches affected by meso and macro tides. Natural Hazards, 93(3) 1337-1358. https://doi.org/10.1007/s11069-018-3360-6

Thielen, J., Bartholmes, J., Ramos, M. H., & Roo, A. D. 2009. The European flood alert system-part 1: concept and development. Hydrology and Earth System Sciences, 13(2), 125-140. https://doi.org/10.5194/hess-13-125-2009

Thiemig, V., Bisselink, B., Pappenberger, F., Thielen, J. 2015. A pan-African medium-range ensemble flood forecast system. Hydrology and Earth System Sciences, 19(8), 3365-3385. https://doi.org/10.5194/hess-19-3365-2015

U.S. Department of Agriculture, Natural Resources Conservation Service. 2010. National Engineering Handbook, Washington, DC

Venâncio, A., Montero, P., Costa, P., Regueiro, S., Brands, S., Taboada, J. 2019. An Integrated Perspective of the Operational Forecasting System in Rías Baixas (Galicia, Spain) with Observational Data and End-Users. In International Conference on Computational Science (pp. 229-239). Springer, Cham. https://doi.org/10.1007/978-3-030-22747-0_18

Wallemarq, P., Below, R., McLean, D. 2018. UNISDR and CRED report: Economic Losses, Poverty & Disasters (1998-2017).

Wanders, N., Karssenberg, D., Roo, A.D., De Jong, S.M., Bierkens, M.F.P. 2014. The suitability of remotely sensed soil moisture for improving operational flood forecasting. Hydrology and Earth System Sciences, 18(6), 2343-2357. https://doi.org/10.5194/hess-18-2343-2014

Weerts, A.H., Winsemius, H.C., Verkade, J.S. 2011. Estimation of predictive hydrological uncertainty using quantile regression: examples from the National Flood Forecasting System (England and Wales). Hydrology and Earth System Sciences, 15(1), 255-265. https://doi.org/10.5194/hess-15-255-2011

Xia, X., Liang, Q., Ming, X. 2019. A full-scale fluvial flood modelling framework based on a high-performance integrated hydrodynamic modelling system (HiPIMS). Advances in Water Resources, 132, 103392. https://doi.org/10.1016/j.advwatres.2019.103392

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