MERLINUna nueva herramienta para la predicción del riesgo de inundaciones en la demarcación hidrográfica Galicia-Costa

  1. Fraga, Ignacio 1
  2. Cea, Luis 1
  3. Puertas, Jerónimo 1
  4. Mosqueira, Gonzalo 2
  5. Quinteiro, Belén 2
  6. Botana, Sonia 2
  7. Fernández, Laura 2
  8. Salsón, Santiago 3
  9. Fernández-García, Guillermo 3
  10. Taboada, Juan 3
  1. 1 Universidade da Coruña
    info

    Universidade da Coruña

    La Coruña, España

    ROR https://ror.org/01qckj285

  2. 2 Augas de Galicia. Consellería de Infraestructuras e Mobilidade
  3. 3 Meteogalicia. Consellería de Medio Ambiente, Territorio e Vivenda
Show affiliations +
Journal:
Ingeniería del agua

ISSN: 1134-2196

Year of publication: 2021

Volume: 25

Issue: 3

Pages: 215-227

Type: Article

DOI: 10.4995/IA.2021.15565 DIALNET GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Ingeniería del agua

Abstract

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 hindcast stage, hydrological models of the basins included in the system assimilate hydro-meteorological data in order to characterize soil infiltration capacity. During the forecast stage, hydrological models are fed with meteorological predictions and discharge forecasts along the basins. Forecasted discharges define boundary conditions of hydraulic models, which compute the flood extent and the water depths over the upcoming days. The performance of MERLIN was evaluated in 4 areas using discharge data from the winter months of 2019-2020. Results proved MERLIN’s ability of predicting the discharges observed afterwards.

Bibliographic 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.
  • 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
Data source: Dialnet