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MERLIN: a flood hazard forecasting system for coastal river reaches

  • Ignacio FragaEmail author
  • Luis Cea
  • Jerónimo Puertas
Original Paper

Abstract

This study presents MERLIN, an innovative flood hazard forecasting system for predicting discharges and water levels at flood prone areas of coastal catchments. Discharge forecasts are preceded by a hindcast stage. During this stage, the hydrological models assimilate soil moisture and hydro-meteorological observations to evaluate soil infiltration capacities at the beginning of the discharge forecast. Predicted discharges are converted to water-level forecasts using the hydraulic model Iber+, a GPU-parallelized bidimensional flow model. Hydraulic models also assimilate tidal-level forecasts in order to define the boundary conditions of the models. The performance of MERLIN was evaluated over 4 months at three coastal catchments of 4.95, 16.96, and 83.9 km2. Forecasted discharges and water levels presented a good fit to observed values, especially at the larger catchments, which confirmed the potential utility of the presented system.

Keywords

Flood hazard forecast Early warning system Hydraulic modelling Hydrological modelling Flood risk management 

Notes

Acknowledgements

Funding was provided by European Regional Development Fund.

References

  1. Abdullah J, Muhammad NS, Julien PY, Ariffin J, Shafie A (2018) Flood flow simulations and return period calculation for the Kota Tinggi watershed Malaysia. J Flood Risk Manag 11:S766–S782CrossRefGoogle Scholar
  2. Acreman MC (1994) Assessing the joint probability of fluvial and tidal floods in the river-roding. J Inst Water Environ Manag 8:490–496CrossRefGoogle Scholar
  3. Aguas de Galicia (2016) Anexo 1 Caracterización das ARPSIS. In Plan de Xestión do Risco de Inundación da Demarcación Hidrográfica de Galicia-Costa (ciclo 2015–2021) Official JournalGoogle Scholar
  4. Alvarez-Garreton C, Ryu D, Western AW, Su CH, Crow WT, Robertson E, Leahy C (2015) Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: comparison between lumped and semi-distributed schemes. Hydrol Earth Syst Sci 19(4):1659–1676CrossRefGoogle Scholar
  5. Arnell NW, Gosling SN (2016) The impacts of climate change on river flood risk at the global scale. Clim Change 134(3):387–401CrossRefGoogle Scholar
  6. Bennett TH, Peters JC (2000) Continuous soil moisture accounting in the hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS). In: Building partnerships, pp 1–10Google Scholar
  7. Bermúdez M, Neal JC, Bates PD, Coxon G, Freer JE, Cea L, Puertas J (2017) Quantifying local rainfall dynamics and uncertain boundary conditions into a nested regional-local flood modeling system. Water Resour Res 53(4):2770–2785CrossRefGoogle Scholar
  8. Beven K, Binley A (2014) GLUE: 20 years on. Hydrol Processes 28(24):5897–5918CrossRefGoogle Scholar
  9. 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. Rev Int Method Numer 30(1):1–10Google Scholar
  10. Brocca L, Moramarco T, Melone F, Wagner W, Hasenauer S, Hahn S (2011) Assimilation of surface-and root-zone ASCAT soil moisture products into rainfall–runoff modeling. IEEE Trans Geosci Remote Sens 50(7):2542–2555CrossRefGoogle Scholar
  11. Cabalar‐Fuentes M (2005) Los temporales de lluvia y viento en Galicia. Propuesta de clasificación y análisis de tendencias (1961–2001). Investigaciones Geográficas (Esp) 36Google Scholar
  12. Carracedo P (2003) Acoplamiento de un modelo hidrodinámico de escala global con uno de escala regional para Galicia. Revista Real Academia Galega de Ciencias 22:85Google Scholar
  13. Cea L, Bladé E (2015) A simple and efficient unstructured finite volume scheme for solving the shallow water equations in overland flow applications. Water Resour Res 51(7):5464–5486CrossRefGoogle Scholar
  14. Cea L, Fraga I (2018) Incorporating antecedent moisture conditions and intraevent variability of rainfall on flood frequency analysis in poorly gauged basins. Water Resour Res 54:8774–8791CrossRefGoogle Scholar
  15. Cea L, French JR (2012) Bathymetric error estimation for the calibration and validation of estuarine hydrodynamic models. Est Coastal Shelf Sci 100:124–132CrossRefGoogle Scholar
  16. Cools J, Vanderkimpen P, Afandi GE, Abdelkhalek A, Fockedey S, Sammany ME, Abdallah G, El Bihery M, Bauwens W, Huygens M (2012) An early warning system for flash floods in hyper-arid Egypt. Nat Hazards Earth Syst Sci 12(2):443–457CrossRefGoogle Scholar
  17. Costabile P, Macchione F (2015) Enhancing river model set-up for 2-D dynamic flood modelling. Environ Model Softw 67:89–107CrossRefGoogle Scholar
  18. Costabile P, Costanzo C, Macchione F (2011) Comparative analysis of overland flow models using finite volume schemes. J Hydroinform 14(1):122–135CrossRefGoogle Scholar
  19. Cronshey R (1986) Urban hydrology for small watersheds. US Department of Agriculture Soil Conservation Service Engineering DivisionGoogle Scholar
  20. Cronshey RG, Roberts RT, Miller N (1985) Urban hydrology for small watersheds (TR-55 Rev). In: Hydraulics and hydrology in the small computer age. ASCE, pp 1268–1273Google Scholar
  21. Delrieu G, Wijbrans A, Boudevillain B, Faure D, Bonnifait L, Kirstetter PE (2014) Geostatistical radar–raingauge merging: a novel method for the quantification of rain estimation accuracy. Adv Water Resour 71:110–124CrossRefGoogle Scholar
  22. Djordjevic S, Butler D, Gourbesville P, Mark O, Pasche E (2011) New policies to deal with climate change and other drivers impacting on resilience to flooding in urban areas: the CORFU approach. Environ Sci Policy 14:864–873CrossRefGoogle Scholar
  23. Ehret U, Götzinger J, Bárdossy A, Pegram GG (2008) Radar-based flood forecasting in small catchments exemplified by the Goldersbach catchment Germany. Int J River Basin Manag 6(4):323–329CrossRefGoogle Scholar
  24. Eiras-Barca J, Brands S, Miguez-Macho G (2016) Seasonal variations in North Atlantic atmospheric river activity and associations with anomalous precipitation over the Iberian Atlantic Margin. J Geophys Res Atmos 121(2):931–948CrossRefGoogle Scholar
  25. Emmanuel I, Andrieu H, Leblois E, Janey N, Payrastre O (2015) Influence of rainfall spatial variability on rainfall–runoff modelling: benefit of a simulation approach? J Hydrol 531:337–348CrossRefGoogle Scholar
  26. Engeland K, Steinsland I, Johansen SS, Petersen-Øverleir A, Kolberg S (2016) Effects of uncertainties in hydrological modelling. A case study of a mountainous catchment in Southern Norway. J Hydrol 536:147–160CrossRefGoogle Scholar
  27. Fleming MJ, Doan JH (2009) HEC-GeoHMS geospatial hydrologic modelling extension: User’s manual version 4.2. US Army Corps of Engineers Institute for Water Resources Hydrologic Engineering Centre Davis CAGoogle Scholar
  28. Fraga I (2018) Analysis of the effect of tidal level on the discharge capacity of two urban rivers using bidimensional numerical modelling. MDPI Proc 2(18):1175Google Scholar
  29. Fraga I, Cea L, Puertas J (2019) Effect of rainfall uncertainty on the performance of physically-based rainfall-runoff models. Hydrol Processes 33:160–173CrossRefGoogle Scholar
  30. 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):1459CrossRefGoogle Scholar
  31. Gimeno L, Nieto R, Vázquez M, Lavers DA (2014) Atmospheric rivers: a mini-review. Front Earth Sci 2:2–10CrossRefGoogle Scholar
  32. Goovaerts P (1997) Geostatistics for natural resource evaluation. Oxford University Press, OxfordGoogle Scholar
  33. Haberlandt U (2007) Geostatistical interpolation of hourly precipitation from rain gauges and radar for a large-scale extreme rainfall event. J Hydrol 332(1):144–157CrossRefGoogle Scholar
  34. Hawkes PJ (2003) Extreme water levels in estuaries and rivers. The combined influence of tides river flows and waves. DEFRA Defra/Environment Agency. R&D Technical Report FD0206/TR1. HR Wallingford Report SR 645Google Scholar
  35. Horita FE, Vilela R, Martins R, Bressiani D, Palma G, de Albuquerque JP (2018) Determining flooded areas using crowd sensing data and weather radar precipitation: a case study in Brazil. In: ISCRAMGoogle Scholar
  36. Hossain F, Siddique-E-Akbor AHM, Yigzaw W, Shah-Newaz S, Hossain M, Mazumder LC, Turk FJ (2014) Crossing the “valley of death”: lessons learned from implementing an operational satellite-based flood forecasting system. Bull Am Meteorol Soc 95(8):1201–1207CrossRefGoogle Scholar
  37. Huard D, Mailhot A (2006) A Bayesian perspective on input uncertainty in model calibration: application to hydrological model “abc”. Water Resour Res 42(7):W07416CrossRefGoogle Scholar
  38. 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)Google Scholar
  39. Jewell SA, Gaussiat N (2015) An assessment of kriging-based rain-gauge–radar merging techniques. Q J R Meteorol Soc 141(691):2300–2313CrossRefGoogle Scholar
  40. Kasiviswanathan KS, He J, Sudheer KP, Tay JH (2016) Potential application of wavelet neural network ensemble to forecast streamflow for flood management. J Hydrol 536:161–173CrossRefGoogle Scholar
  41. 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 Resour Manag 27(10):3585–3606CrossRefGoogle Scholar
  42. Krajewski WF, Ceynar D, Demir I, Goska R, Kruger A, Langel C, Small SJ (2017) Real-time flood forecasting and information system for the state of Iowa. Bull Am Meteorol Soc 98(3):539–554CrossRefGoogle Scholar
  43. Kumar M, Sahay RR (2018) Wavelet-genetic programming conjunction model for flood forecasting in rivers. Hydrol Res 49(6):1880–1889CrossRefGoogle Scholar
  44. Lamichhane N, Sharma S (2017) Development of flood warning system and flood inundation mapping using field survey and LiDAR data for the Grand River near the city of Painesville Ohio. Hydrol 4(2):24CrossRefGoogle Scholar
  45. Lehbab-Boukezzi Z, Boukezzi L, Errih M (2016) Uncertainty analysis of HEC-HMS model using the GLUE method for flash flood forecasting of Mekerra watershed, Algeria. Arab J Geosci 9(20):751CrossRefGoogle Scholar
  46. Lievens H, Tomer SK, Al Bitar A, De Lannoy GJ, Drusch M, Dumedah G, Fransen HJ, Kerr YH, Martens B, Pan M, Roundy JK (2015) SMOS soil moisture assimilation for improved hydrologic simulation in the Murray Darling Basin Australia. Remote Sens Environ 168:146–162CrossRefGoogle Scholar
  47. Massari C, Brocca L, Barbetta S, Papathanasiou C, Mimikou M, Moramarco T (2014) Using globally available soil moisture indicators for flood modelling in Mediterranean catchments. Hydrol Earth Syst Sci 18(2):839–853CrossRefGoogle Scholar
  48. Massari C, Brocca L, Tarpanelli A, Moramarco T (2015) Data assimilation of satellite soil moisture into rainfall-runoff modelling: a complex recipe? Remote Sens 7(9):11403–11433CrossRefGoogle Scholar
  49. Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50(3):885–900CrossRefGoogle Scholar
  50. Moulin L, Gaume E, Obled C (2009) Uncertainties on mean areal precipitation: assessment and impact on streamflow simulations. Hydrol Earth Syst Sci Discuss 13(2):99–114CrossRefGoogle Scholar
  51. Mure-Ravaud M, Binet G, Bracq M, Perarnaud JJ, Fradin A, Litrico X (2016) A web based tool for operational real-time flood forecasting using data assimilation to update hydraulic states. Environ Modell Softw 84:35–49CrossRefGoogle Scholar
  52. Nester T, Komma J, Blöschl G (2016) Real time flood forecasting in the Upper Danube basin. J Hydrol Hydromech 64(4):404–414CrossRefGoogle Scholar
  53. Neto JG, Ribeiro-Neto A, Montenegro SM (2014) Assessment of rainfall-runoff models for flood river extreme event simulations. In: Proceedings of the 6th international conference on flood management. Sau Paulo, Brazil, pp 1–10Google Scholar
  54. 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. J Hydrol 541:401–420CrossRefGoogle Scholar
  55. Noh SJ, Lee JH, Lee S, Kawaike K, Seo DJ (2018) Hyper-resolution 1D-2D urban flood modelling using LiDAR data and hybrid parallelization. Environ Modell Softw 103:131–145CrossRefGoogle Scholar
  56. Oleyiblo JO, Li ZJ (2010) Application of HEC-HMS for flood forecasting in Misai and Wan’an catchments in China. Water Sci Eng 3(1):14–22Google Scholar
  57. Rosburg TT, Nelson PA, Bledsoe BP (2017) Effects of urbanization on flow duration and stream flashiness: a case study of Puget Sound streams, western Washington, USA. JAWRA J Am Water Resour As 53(2):493–507CrossRefGoogle Scholar
  58. Sanders BF, Schubert JE (2019) PRIMo: parallel raster inundation model. Adv Water Resour 126:79–95CrossRefGoogle Scholar
  59. Sanz Ramos M, Amengual A, BladéiCastellet E, Romero R, Roux H (2018) Flood forecasting using a coupled hydrological and hydraulic model (based on FVM) and highresolution meteorological model. In: Proceedings of river flow 2018-ninth international conference on fluvial hydraulics. Lyon, France, pp 1–8CrossRefGoogle Scholar
  60. Scharffenberg WA, Fleming MJ (2006) Hydrologic modeling system HEC‐HMS: user’s manual. US Army Corps of Engineers Hydrologic Engineering CenterGoogle Scholar
  61. Schelfaut K, Pannemans B, Van der Craats I, Krywkow J, Mysiak J, Cools J (2011) Bringing flood resilience into practice: the FREEMAN project. Environ Sci Pollu 14(7):825–833CrossRefGoogle Scholar
  62. Schiemann R, Erdin R, Willi M, Frei C, Berenguer M, Sempere-Torres D (2011) Geostatistical radar-raingauge combination with nonparametric correlograms: methodological considerations and application in Switzerland. Hydrol Earth Syst Sci 15(5):1515–1536CrossRefGoogle Scholar
  63. Schwanenberg D, Natschke M, Todini E, Reggiani P (2018) Scientific technical and institutional challenges towards next-generation operational flood risk management decision support systems. Int J River Basin Manag 16(3):345–352CrossRefGoogle Scholar
  64. Shchepetkin AF, McWilliams JC (2005) The regional oceanic modeling system (ROMS): a split-explicit free-surface topography-following-coordinate oceanic model. Ocean Model 9(4):347–404CrossRefGoogle Scholar
  65. Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Wang W, Powers JG (2008) A description of the Advanced Research WRF version 3. NCAR Technical note-475+ STRGoogle Scholar
  66. 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. Nat Hazards 93(3):1337–1358CrossRefGoogle Scholar
  67. Svensson C, Jones DA (2002) Dependence between extreme sea surge river flow and precipitation in eastern Britain. Int J Climatol 22:1149–1168CrossRefGoogle Scholar
  68. Tayfur G, Zucco G, Brocca L, Moramarco T (2014) Coupling soil moisture and precipitation observations for predicting hourly runoff at small catchment scale. J Hydrol 510:363–371CrossRefGoogle Scholar
  69. Thielen J, Bartholmes J, Ramos MH, Roo AD (2009) The European flood alert system–part 1: concept and development. Hydrol Earth Syst Sci 13(2):125–140CrossRefGoogle Scholar
  70. Thiemig V, Bisselink B, Pappenberger F, Thielen J (2015) A pan-African medium-range ensemble flood forecast system. Hydrol Earth Syst Sci 19(8):3365–3385CrossRefGoogle Scholar
  71. Van Steenbergen N, Willems P (2013) Increasing river flood preparedness by real-time warning based on wetness state conditions. J Hydrol 489:227–237CrossRefGoogle Scholar
  72. Villarini G, Mandapaka PV, Krajewski WF, Moore RJ (2008) Rainfall and sampling uncertainties: a rain gauge perspective. J Geophys Res Atmos 113(D11):1–12CrossRefGoogle Scholar
  73. Wallemarq P, Below R, McLean D (2018) UNISDR and CRED report: Economic Losses, Poverty & Disasters (1998–2017). Technical reportGoogle Scholar
  74. Wanders N, Karssenberg D, Roo AD, De Jong SM, Bierkens MFP (2014) The suitability of remotely sensed soil moisture for improving operational flood forecasting. Hydrol Earth Syst Sci 18(6):2343–2357CrossRefGoogle Scholar
  75. Weerts AH, Winsemius HC, Verkade JS (2011) Estimation of predictive hydrological uncertainty using quantile regression: examples from the National Flood Forecasting System (England and Wales). Hydrol Earth Syst Sci 15(1):255–265CrossRefGoogle Scholar
  76. Wick GA, Neiman PJ, Ralph FM, Hamill TM (2013) Evaluation of forecasts of the water vapor signature of atmospheric rivers in operational numerical weather prediction models. Weather Forecast 28(6):1337–1352CrossRefGoogle Scholar
  77. Xia X, Liang Q, Ming X (2019) A full-scale fluvial flood modelling framework based on a high-performance integrated hydrodynamic modelling system (HiPIMS). Adv Water Resour 132:103392CrossRefGoogle Scholar
  78. Xunta de Galicia (2016) Plan especial de protección civil ante el riesgo de inundaciones en Galicia. Official JournalGoogle Scholar
  79. Zhong H, Van Overloop PJ, Van Gelder PHAJM (2013) A joint probability approach using a 1D hydrodynamic model for estimating high water level frequencies in the Lower Rhine Delta. Nat Hazards Earth Syst Sci 13:1841–1852CrossRefGoogle Scholar

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© Springer Nature B.V. 2020

Authors and Affiliations

  1. 1.CITIC, University of A CoruñaA CoruñaSpain
  2. 2.Environmental and Water Engineering Group, Department of Civil EngineeringUniversity of A CoruñaA CoruñaSpain

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