River Flood Forecasting System: An Interdisciplinary Approach

  • Viacheslav ZelentsovEmail author
  • Ilya Pimanov
  • Semen Potryasaev
  • Boris Sokolov
  • Sergey Cherkas
  • Andrey Alabyan
  • Vitaly Belikov
  • Inna Krylenko
Part of the Springer Remote Sensing/Photogrammetry book series (SPRINGERREMO)


The chapter presents a holistic system that implements an advanced river flood modeling and forecasting approach. This approach extends traditional methods based on separate satellite monitoring or river physical processes modeling, by integration of different technologies such as satellite and in situ data processing, input data clustering and filtering, digital mapping of river valleys relief, data crowdsourcing, hydrodynamic modeling, inundation visualization, and also duly warning of stakeholders.

The software of the suggested system was implemented on the base of open source code and service-oriented architecture (SOA). This allows the use of different program modules for data processing and modeling, integrated into a unified software suite. Forecast results are available as web services. Additionally, a special GIS platform has been developed to visualize the results of forecasting. It does not require the users to have any special skills or knowledge, and all the complexity relating to data processing and modeling is hidden from the users.

The results of case studies have shown that the suggested interdisciplinary approach provides highly accurate forecasting due to operational ingestion and integrated processing of the remote sensing and ground-based water flow data in real time. In these case studies, forecasting of flood areas and depths was performed on a time interval of 12–48 h, allowing performing the necessary steps to alert and evacuate the population.

In general, the developed software can be considered as a toolkit to create holistic monitoring and flood forecasting systems with an integrated use of diverse satellite and in situ data.


River floods Interdisciplinary approach Multimodel simulation Operational forecasting Remote sensing data Intelligent interface Service oriented architecture 



The research described in this chapter is partially supported by the Russian Foundation for Basic Research (grants 15-07-08391, 15-08-08459, 16-07-00779, 16-08-00510, 16-08-01277, 16-29-09482-оfi-i, 16-07-00925, 17-08-00797, 17-06-00108, 17-01-00139, 17-20-01214), by the Russian Research Foundation (grants 16-19-00199, 17-11-01254), by ITMO University’s grant 074-U01, project 6.1.1 (Peter the Great St. Petersburg Polytechnic University) supported by Government of Russian Federation, Program STC of Union State “Monitoring-SG” (project 1.4.1-1), state order of the Ministry of Education and Science of the Russian Federation №2.3135.2017/K, and state research 0073–2014–0009 and 0073–2015–0007.


  1. 1.
    Transboundary Flood Risk Management in the Unece Region. United Nations New York and Geneva (2009)Google Scholar
  2. 2.
    Porfiriev, B.N.: Economic consequences of the 2013 catastrophic flood in the Far East. Herald Russ. Acad. Sci. 85(40), pp. 40–48 (2015)Google Scholar
  3. 3.
    Alekseevskii, N.I., Frolova, N.L., Khristoforov, A.V.: Monitoring Hydrological Processes and Improving Water Management Safety. Izd. Mos. Gos. Univ, Moscow (2011) [in Russian]Google Scholar
  4. 4.
    Vasil’ev, O.F.: Designing systems of operational freshet and high water prediction. Herald Russ. Acad. Sci. 82(129), pp. 129–133 (2012)Google Scholar
  5. 5. [Accessed 6 April 2017]
  6. 6.
    MIKE Flood Watch, [Accessed 6 April 2017]
  7. 7.
    DHI-Water forecast, [Accessed 6 April 2017]
  8. 8.
    EFAS, [Accessed 6 April 2017]
  9. 9.
    LISPFLOOD-FP, University of Bristol, School of Geographical Sciences, Hydrology Group, (Accessed 6 Apr 2017)
  10. 10.
  11. 11.
  12. 12.
  13. 13.
  14. 14.
  15. 15. [Accessed 6 April 2017]
  16. 16.
  17. 17.
    WRF, [Accessed 6 April 2017]
  18. 18. [Accessed 6 April 2017]
  19. 19. [Accessed 6 April 2017]
  20. 20.
  21. 21.
  22. 22.
    GLCF, [Accessed 6 April 2017]
  23. 23. [Accessed 6 April 2017]
  24. 24.
    Frolov, A.V., Asmus, V.V., Zatyagalova, V.V., Krovotyntsev, V.A., Borshch, S.V., Vil’fand, R.M., Zhabina, I.I., Kudryavtseva, O.I., Leont’eva, E.A., Simonov, Y.A., Stepanov, Y.A.: GIS-Amur system of flood monitoring, forecasting, and early warning. Russian Meteorol. Hydrol. 41(3), 157–169 (2016)CrossRefGoogle Scholar
  25. 25. [Accessed 6 April 2017]
  26. 26.
    D’Addabbo, A., et al.: A Bayesian network for flood detection combining SAR imagery and ancillary data. IEEE Trans. 54(6), pp. 3612–3625 (2016)Google Scholar
  27. 27.
    Mason, D., Davenport, I., Neal, J., Schumann, G., Bates, P.D.: Near real-time flood detection in urban and rural areas using high resolution synthetic aperture radar images. IEEE Trans. Geosci. Remote Sens. 50(8), 3041–3052 (2012)CrossRefGoogle Scholar
  28. 28.
    Pierdicca, N., Pulvirenti, L., Chini, M., Guerriero, L., Candela, L.: Observing floods from space: experience gained from COSMO-SkyMed observations. Acta Astron. 84, 122–133 (2013)CrossRefGoogle Scholar
  29. 29.
    Romeiser, R., Runge, H., Suchandt, S., Sprenger, J., Weilbeer, H., Sohrmann, A., Stammer, D.: Current measurements in rivers by spaceborne along-track InSAR. IEEE Trans. Geosci. Remote Sens. 45(12), 4019–4031 (2007)CrossRefGoogle Scholar
  30. 30.
    Hahmann T.. Wessel B..: Surface Water Body Detection in High-Resolution TerraSAR-X Data using Active Contour Models. In: Proceedings of the 8th European Conference on Synthetic Aperture Radar (EUSAR 2010), Aachen, Germany. VDE Verlag GmbH (June 7–10, 2010)Google Scholar
  31. 31.
    Floyd, L., Prakash, A., Meyer, F.J., Gens, R., Liljedahl, A.: Using synthetic aperture radar to define spring breakup on the Kuparuk River, Northern Alaska. ARCTIC. 67(4), 462–471 (2014)CrossRefGoogle Scholar
  32. 32.
    Barneveld H.J., Silander J.T., Sane M., Malnes E..: Application of satellite data for improved flood forecasting and mapping. In:4th International Symposium on Flood Defence:Managing Flood Risk, Reliability and Vulnerability Toronto, Ontario, Canada, p. 77–1–77-8 (May 6–8 2008)Google Scholar
  33. 33.
    Wang, Y., Colby, J., Mulcahy, K.: An efficient method for mapping flood extent in a coastal floodplain using Landsat TM and DEM data. In. Int. J. Remote Sens. 23(18), 3681–3696 (2002)CrossRefGoogle Scholar
  34. 34.
    Sokolov B.V., Zelentsov V.A., Mochalov V.F., Potryasaev S.A., Brovkina O.V.: Complex Objects Remote Sensing Monitoring and Modelling: Methodology, Technology and Practice. In: Proc. 8th EUROSIM Congress on Modelling and Simulation, 10–13 September 2013, Cardiff, Wales, United Kingdom, pp.443–447 (2013)Google Scholar
  35. 35.
    Merkuryev, Y., Merkuryeva, G., Sokolov, B., Zelentsov, V. (eds.): Information Technologies and Tools for Space-Ground Monitoring of Natural and Technological Objects, Riga, Riga Technical University (2014)Google Scholar
  36. 36.
    Boris V. Sokolov, Anton Ev. Pashchenko, Semyon Potryasaev A., Alevtina V. Ziuban, Vyacheslav A. Zelentsov.: Operational Flood Forecasting As A Web-Service. In.: Proc. 29th European Conference on Modelling and Simulation (ECMS 2015), Albena (Varna), Bulgaria. P. 364–370 (2015)Google Scholar
  37. 37.
    Schumann G., Bates P., Horritt M., Matgen P., Pappenberger F.: Progress in integration of remote sensing-derived flood extent and stage data and hydraulic models. Rev. Geophys. 47(4), Art. no. RG4001 (2009)Google Scholar
  38. 38.
    Brivio, P.A., Colombo, R., Maggi, M., Tomasoni, R.: Integration of remote sensing data and GIS for accurate mapping of flooded areas. Int. J. Remote Sens. 23(3), 429–441 (2002)CrossRefGoogle Scholar
  39. 39.
    Matgen, P., Schumann, G., Henry, J., Hoffmann, L., Pfister, L.: Integration of SAR-derived river inundation areas, high-precision topographic data and a river flow model toward near real-time flood management. Int. J. Appl. Earth Observ. Geoinf. 9(3), 247–263 (2007)CrossRefGoogle Scholar
  40. 40.
    Loumagne C., Weisse A., Normand M., Riffard M., Quesney A., Le. Hegarat-mascle S., Alem F.: Integration of remote sensing data into hydrological models for flood forecasting. In: Remote Sensing and Hydrology 2000 (Proceedings of a symposium held at Santa Fe, New Mexico, USA). IAHS Publ. no. 267, 2001, p. 592–594 (Apr 2000)Google Scholar
  41. 41.
    Hartanto, I.M., Almeida, C., Alexandridis, T.K., Weynants, M., Timoteo, G., Chambel-Leitao, P., Araujo, A.M.S.: Merging earth observation data, weather predictions, in-situ measurements and hydrological models for water information services. Environ. Eng. Manag. J. 14(9), 2031–2042 (2015)Google Scholar
  42. 42.
    van Dijk, A.I.J.M., Renzullo, L.J.: Water resource monitoring systems and the role of satellite observations. Hydrol. Earth Syst. Sci. 15, 39–55 (2011)CrossRefGoogle Scholar
  43. 43.
    Lehmann, A., Giuliani, G., Ray, N., Rahman, K., Abbaspour, K.C., Nativi, S., Craglia, M., Cripe, D., Quevauviller, P., Beniston, M.: Reviewing innovative Earth observation solutions for filling science-policy gaps in hydrology. J. Hydrol. 518(Part B), 267–277 (2014)CrossRefGoogle Scholar
  44. 44.
    Thomas Erl.: Next generation SOA: a concise introduction to service technology & service-orientation. In: The Prentice Hall Service Technology Series from Thomas Erl). М.: Prentice Hall. Upper Saddle River, New Jersey, 234 p. (2014)Google Scholar
  45. 45.
    Emma, B., Danie, B., Michael, C., de Annemargreet, L., Leonore B., Ferdinand, D., Dirk, E., de Karin, B., Albrecht, W., Caroline, H., Joost, B.: Methods and tools to support real time risk-based flood forecasting – a UK pilot application. In: FLOODrisk 2016 – 3rd European Conference on Flood Risk Management, E3S Web of Conferences 7, Lyon, France, 18019 (2016)Google Scholar
  46. 46.
    Belikov V.V., Krylenko I.N., Alabyan A.M., Sazonov A.A., Glotko A.V.: Two-dimensional hydrodynamic flood modelling for populated valley areas of Russian rivers. In: Proceedings International Association of Hydrological Sciences, Prague, Czech Republic, pp. 69–74 (2015)Google Scholar
  47. 47.
    Skotner C. et al.: MIKE FLOOD WATCH - managing real-time forecasting, (Accessed 6 Apr 2017)
  48. 48.
    Delft3D-FLOW Version 3.06 User Manual., (Accessed 6 Apr 2017)
  49. 49.
    HEC-RAS river analysis system User’s Manual., (Accessed 6 Apr 2017)
  50. 50.
    Cunge, G.A., Holly, F.M., Verway, A.: Practical Aspects of Computational River Hydraulics. Pitman Publishing LTD, London (1980)Google Scholar
  51. 51.
    Belikov, V.V., Militeev, A.N.: The two-layered mathematical model of catastrophic floods. Vych.Tekhnol. 3, 167 (1992)Google Scholar
  52. 52.
    Sokolov, B.V., Yusupov, R.M.: Conceptual foundations of quality estimation and analysis for models and multiple-model systems. J. Comput. Syst. Sci. Int. 43(6), 831 (2004)Google Scholar
  53. 53.
    Okhtilev, M.Y., Sokolov, B.V., Yusupov, R.M.: Intelligent Technologies for Monitoring and Controlling the Structural Dynamics of Complex Technological Objects. Nauka, Moscow (2006.) [in Russian]Google Scholar
  54. 54.
    Bukatova, I.L.: Evolutionary Modeling and Its Applications. Nauka, Moscow (1979.) [in Russian]Google Scholar
  55. 55.
    Merkuryev Y., Okhtilev M., Sokolov B., Trusina I., Zelentsov V.: Intelligent Technology for Space and Ground based Monitoring of Natural Objects in Cross-Border EU-Russia Territory. In: Proc. International Geoscience and Remote Sensing Symposium (IGARSS 2012), Munich, Germany, pp. 2759–2762 (2012)Google Scholar
  56. 56.
    Baryshnikov, N.B.: Hydraulic Resistances of Riverbeds. Izd. Ross. Gos. Gidrometeorol. Univ, St. Petersburg (2003.) [in Russian]Google Scholar
  57. 57.
    Sokolov B.V., Zelentsov V.A., Brovkina O., et al.: Complex objects remote sensing forest monitoring and modeling. In: Ed. Silhavy, R., Senkerik, R., Oplatkova, Z.K., Silhavy P., Prokopova, Z. (eds.) Modern Trends and Techniques in Computer Science: Advances in Intelligent Systems and Computing, Cham, Switzerland, Vol. 285, pp. 445–453. Springer (2014)Google Scholar
  58. 58.
    Sokolov, B.V., Zelentsov, V.A., Yusupov, R.M., Merkuryev, Y.A.: Multiple models of information fusion processes: quality definition and estimation. J. Comput. Sci. 5(380), pp. 380–386 (2014)Google Scholar
  59. 59.
    Vasiliev Y.: SOA and WS-BPEL: Composing Service-Oriented Solution with PHP and ActiveBPEL, Packt Publishing, Birmingham, United Kingdom (2007)Google Scholar
  60. 60. [Accessed 6 April 2017]
  61. 61.
    Moscow, Russia, [Accessed 6 April 2017]
  62. 62.
    NTs OMZ, [Accessed 6 April 2017]

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Viacheslav Zelentsov
    • 1
    Email author
  • Ilya Pimanov
    • 1
  • Semen Potryasaev
    • 1
  • Boris Sokolov
    • 1
  • Sergey Cherkas
    • 1
  • Andrey Alabyan
    • 2
  • Vitaly Belikov
    • 2
  • Inna Krylenko
    • 2
  1. 1.Saint Petersburg Institute of Informatics and Automation of the Russian Academy of SciencesSt. PetersburgRussia
  2. 2.Lomonosov Moscow State UniversityMoscowRussia

Personalised recommendations