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River Flood Forecasting System: An Interdisciplinary Approach

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Flood Monitoring through Remote Sensing

Part of the book series: Springer Remote Sensing/Photogrammetry ((SPRINGERREMO))

Abstract

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.

The original version of this chapter was revised. An erratum to this chapter can be found at https://doi.org/10.1007/978-3-319-63959-8_9

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References

  1. Transboundary Flood Risk Management in the Unece Region. United Nations New York and Geneva (2009)

    Google Scholar 

  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. 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. 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. http://waterwatch.usgs.gov [Accessed 6 April 2017]

  6. MIKE Flood Watch, https://www.mikepoweredbydhi.com/products/mikeflood [Accessed 6 April 2017]

  7. DHI-Water forecast, http://www.waterforecast.com [Accessed 6 April 2017]

  8. EFAS, http://www.efas.eu [Accessed 6 April 2017]

  9. LISPFLOOD-FP, University of Bristol, School of Geographical Sciences, Hydrology Group, www.bristol.ac.uk/geography/research/hydrology/models/lisflood/ (Accessed 6 Apr 2017)

  10. FEWS, https://www.deltares.nl/en/software/floodforecasting-system-delft-fews-2/ [Accessed 6 April 2017]

  11. https://www.mikepoweredbydhi.com/products/mike-11 [Accessed 6 April 2017]

  12. https://www.mikepoweredbydhi.com/products/mike-21 [Accessed 6 April 2017]

  13. https://oss.deltares.nl/web/delft3d/home [Accessed 6 April 2017]

  14. http://www.hec.usace.army.mil/software/hec-ras/ [Accessed 6 April 2017]

  15. https://www.flo-2d.com/ [Accessed 6 April 2017]

  16. http://www.opentelemac.org/index.php/presentation [Accessed 6 April 2017]

  17. WRF, www.wrf-model.org [Accessed 6 April 2017]

  18. http://www.meteorf.ru [Accessed 6 April 2017]

  19. https://lance-modis.eosdis.nasa.gov [Accessed 6 April 2017]

  20. http://emergency.copernicus.eu/mapping/ems/service-overview [Accessed 6 April 2017]

  21. https://www.digitalglobe.com/products/advancedelevation-series [Accessed 6 April 2017]

  22. GLCF, www.landcover.org [Accessed 6 April 2017]

  23. https://nrsc.gov.in/floods [Accessed 6 April 2017]

  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)

    Article  Google Scholar 

  25. http://eng.ntsomz.ru/ [Accessed 6 April 2017]

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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. 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. 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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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. Skotner C. et al.: MIKE FLOOD WATCH - managing real-time forecasting, www.researchgate.net/publication/242572811_MIKE_FLOOD_WATCH_-_Managing_Real-Time_Forecasting (Accessed 6 Apr 2017)

  48. Delft3D-FLOW Version 3.06 User Manual., oss.deltares.nl/web/delft3d (Accessed 6 Apr 2017)

  49. HEC-RAS river analysis system User’s Manual., www.hec.usace.army.mil/software/hec-ras. (Accessed 6 Apr 2017)

  50. Cunge, G.A., Holly, F.M., Verway, A.: Practical Aspects of Computational River Hydraulics. Pitman Publishing LTD, London (1980)

    Google Scholar 

  51. Belikov, V.V., Militeev, A.N.: The two-layered mathematical model of catastrophic floods. Vych.Tekhnol. 3, 167 (1992)

    Google Scholar 

  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. 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. Bukatova, I.L.: Evolutionary Modeling and Its Applications. Nauka, Moscow (1979.) [in Russian]

    Google Scholar 

  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. Baryshnikov, N.B.: Hydraulic Resistances of Riverbeds. Izd. Ross. Gos. Gidrometeorol. Univ, St. Petersburg (2003.) [in Russian]

    Google Scholar 

  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. 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. Vasiliev Y.: SOA and WS-BPEL: Composing Service-Oriented Solution with PHP and ActiveBPEL, Packt Publishing, Birmingham, United Kingdom (2007)

    Google Scholar 

  60. https://www.w3.org/TR/soap/ [Accessed 6 April 2017]

  61. Moscow, Russia, www.scanex.ru/ [Accessed 6 April 2017]

  62. NTs OMZ, http://eng.ntsomz.ru/ [Accessed 6 April 2017]

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Acknowledgments

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.

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Authors and Affiliations

  1. Saint Petersburg Institute of Informatics and Automation of the Russian Academy of Sciences, St. Petersburg, Russia

    Viacheslav Zelentsov, Ilya Pimanov, Semen Potryasaev, Boris Sokolov & Sergey Cherkas

  2. Lomonosov Moscow State University, Moscow, Russia

    Andrey Alabyan, Vitaly Belikov & Inna Krylenko

Authors
  1. Viacheslav Zelentsov
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  2. Ilya Pimanov
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  3. Semen Potryasaev
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  4. Boris Sokolov
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  5. Sergey Cherkas
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  6. Andrey Alabyan
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  7. Vitaly Belikov
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  8. Inna Krylenko
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Corresponding author

Correspondence to Viacheslav Zelentsov .

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Editors and Affiliations

  1. Istituto di Studi sui Sistemi, Intelligenti per l’Automazione (ISSIA), Consiglio Nazionale delle Ricerche (CNR), Bari, Italy

    Alberto Refice

  2. Istituto di Studi sui Sistemi, Intelligenti per l’Automazione (ISSIA), Consiglio Nazionale delle Ricerche (CNR), Bari, Italy

    Annarita D'Addabbo

  3. Department of Earth and Environmental Sciences, University of Bari, Bari, Italy

    Domenico Capolongo

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Zelentsov, V. et al. (2018). River Flood Forecasting System: An Interdisciplinary Approach. In: Refice, A., D'Addabbo, A., Capolongo, D. (eds) Flood Monitoring through Remote Sensing. Springer Remote Sensing/Photogrammetry. Springer, Cham. https://doi.org/10.1007/978-3-319-63959-8_4

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