Natural Hazards

, Volume 91, Issue 2, pp 491–513 | Cite as

Application of a flood inundation model to analyze the potential impacts of a flood control plan in Mundeni Aru river basin, Sri Lanka

  • Shuhei Yoshimoto
  • Giriraj Amarnath
Original Paper


Capturing inundation extent by floods is indispensable for decision making for mitigating hazard. Satellite images have commonly been used for flood mapping, but there are limitations such as unavailability due to satellite’s orbital period or cloud cover. Additionally, it would also be beneficial for policy makers to figure out the impact of water management measures such as water storage options on flood mitigation and irrigation water strengthening. Utilization of flood inundation models would support providing information for these demands. In this study, the rainfall–runoff inundation (RRI) model was applied in a flood-prone basin in eastern Sri Lanka, and its applicability was discussed. The RRI model was capable of simulating discharge and inundation extent during flood events, although it should be noted that the model had been calibrated targeting only the flooding period. Satellite-observed rainfall data corrected with a scale factor were able to be used as the model input to simulate long-term trends in runoff just as well as when gauged rainfall data were applied. The calibrated model was also capable of evaluating flood mitigation effects of existing and proposed water storage options by simulating discharge with and without flood capture operations. By reproducing long-term inflow to the storage facilities using satellite rainfall data, it was possible to determine that water would reach the maximum level of the proposed storage facilities even during low-rainfall years.


Satellite data Rainfall–runoff inundation model Storage options Flood mitigation Sri Lanka 



This research was funded by the Japan’s Ministry of Agriculture, Forestry and Fisheries (MAFF), the CGIAR Research Program on Water, Land and Ecosystems (WLE), and the International Water Management Institute (IWMI). We would like to thank Mr. Niranga Alahacoon of IWMI for his kind support on data processing, Dr. Takahiro Sayama of Kyoto University for his kind advice, and also the Department of Irrigation, Sri Lanka, Survey Department of Sri Lanka, and Department of Meteorology, Sri Lanka, for sharing their data.


  1. Amarnath G, Umer YM, Alahacoon N, Inada Y (2015) Modelling the flood-risk extent using LISFLOOD-FP in a complex watershed: case study of Mundeni Aru River Basin, Sri Lanka. Proc Int As Hydrol Sci 370:131–138Google Scholar
  2. Amarnath G, Alahacoon N, Smakhtin V, Aggarwal P (2017) Mapping multiple climate-related hazards in South Asia. Colombo, Sri Lanka. IWMI Res Rep 170:1–41Google Scholar
  3. Bates PD, De Roo APJ (2000) A simple raster based model for flood inundation simulation. J Hydrol 236:54–77CrossRefGoogle Scholar
  4. Central Bank of Sri Lanka (2014) Economic and social statistics of Sri Lanka 2014. Central Bank of Sri Lanka, ColomboGoogle Scholar
  5. Chow VT (1959) Open-channel hydraulics. McGraw-Hill, New YorkGoogle Scholar
  6. Department of Meteorology (2014) Climate in Sri Lanka. Accessed 7 June 2016
  7. EM-DAT (2017) The CRED/OFDA international disaster database. Accessed 16 Oct 2017
  8. Freeze RA, Cherry JA (1979) Groundwater. Prentice Hall, Englewood CliffsGoogle Scholar
  9. Graham DN, Butts MB (2005) Flexible, integrated watershed modelling with MIKE SHE. In: Singh VP, Frevert DK (eds) Watershed models. CRC Press, Boca Raton, pp 245–272Google Scholar
  10. Hirabayashi Y, Mahendran R, Koirala S, Konoshima L, Yamazaki D, Watanabe S, Kim H, Kanae S (2013) Global flood risk under climate change. Nat Clim Change 3:816–821CrossRefGoogle Scholar
  11. IPCC (2014) Summary for policymakers. In: Field CB et al (eds) Climate change 2014: impacts, adaptation, and vulnerability. Part A: global and sectoral aspects. Contribution of working group II to the fifth assessment report of the intergovernmental panel on climate change. Cambdridge University Press, Cambridge, pp 1–32Google Scholar
  12. IWMI (2017) Vector soil map, Sri Lanka. Accessed 17 Oct 2017
  13. Jongman B, Ward PJ, Aerts JCJH (2012) Global exposure to river and coastal flooding: long term trends and changes. Glob Environ Change 22:823–835CrossRefGoogle Scholar
  14. Legates DR, McCabe GJ Jr (1999) Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resour Res 35(1):233–241CrossRefGoogle Scholar
  15. Lehner B, Verdin K, Jarvis A (2008) New global hydrography derived from spaceborne elevation data. EOS Trans AGU 89(10):93–94CrossRefGoogle Scholar
  16. Madsen H, Wilson G, Ammentorp HC (2002) Comparison of different automated strategies for calibration of rainfall-runoff models. J Hydrol 261(1–4):48–59CrossRefGoogle Scholar
  17. Marambe B, Punyawardena R, Silva P, Premalal S, Rathnabharathie V, Kekulandala B, Nidumolu U, Howden M (2015) Climate, climate risk, and food security in Sri Lanka: the need for strengthening adaptation strategies. In: Filho WL (ed) Handbook of climate change adaptation. Springer, Berlin, pp 1759–1789CrossRefGoogle Scholar
  18. Mirza MMQ (2011) Climate change, flooding in South Asia and implications. Reg Environ Change 11(sup1):95–107CrossRefGoogle Scholar
  19. Miyan MA (2015) Droughts in Asian least developed countries: vulnerability and sustainability. Weather Clim Extrem 7:8–23CrossRefGoogle Scholar
  20. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models, 1. A discussion of principles. J Hydrol 10:282–290CrossRefGoogle Scholar
  21. Nastiti KD, Kim Y, Jung K, An H (2015) The application of rainfall-runoff-inundation (RRI) model for inundation case in upper Citarum watershed, West Java-Indonesia. Procedia Eng 125:166–172CrossRefGoogle Scholar
  22. Pfannerstill M, Guse B, Fohrer N (2014) Smart low flow signature metrics for an improved overall performance evaluation of hydrological models. J Hydrol 510:447–458CrossRefGoogle Scholar
  23. Prudhomme CI, Giuntoli EL, Robinson DB, Clark NW, Arnell R, Dankers BM, Fekete Franssen W, Gerten D, Gosling SN, Hagemann S, Hannah DM, Kim H, Masaki Y, Satoh Y, Stacke T, Wada Y, Wisser D (2014) Hydrological droughts in the 21st century, hotspots and uncertainties from a global multimodel ensemble experiment. Proc Natl Acad Sci USA 111:3262–3267CrossRefGoogle Scholar
  24. Sayama T (2015) Rainfall-runoff-inundation (RRI) model ver. 1.4.2. Accessed 27 Jan 2017
  25. Sayama T, Fukami K, Tanaka S, Takeuchi K (2010) Rainfall-runoff-inundation analysis for flood risk assessment at the regional scale. In: Proceedings of the 5th conference of Asia Pacific association of hydrology and water resources (APHW), pp 568–576Google Scholar
  26. Sayama T, Ozawa G, Kawakami T, Nabesaka S, Fukami K (2012) Rainfall-runoff-inundation analysis of the 2010 Pakistan flood in the Kabul River basin. Hydrol Sci J 57:298–312CrossRefGoogle Scholar
  27. Sayama T, Tatebe Y, Tanaka S (2015) An emergency response-type rainfall-runoff-inundation simulation for 2011 Thailand floods. J Flood Risk Manag. Google Scholar
  28. Singh J, Knapp HV, Arnold JG, Demissie M (2005) Hydrologic modeling of the Iroquois River watershed using HSPF and SWAT. J Am Water Resour As 41:343–360CrossRefGoogle Scholar
  29. Sorooshian S, Hsu KL, Gao X, Gupta HV, Imam B, Braithwaite D (2000) Evaluation of PERSIANN system satellite-based estimates of tropical rainfall. B Am Meteorol Soc 81(9):2035–2046CrossRefGoogle Scholar
  30. UNOCHA (2011) Revision of the Sri Lanka flash appeal 2011. 25 Jan 2016
  31. UNOCHA (2012) Extreme weather situation in Sri Lanka (21 December 2012 at 16:00 hrs). Accessed 2 June 2016
  32. World Bank (2015) South Asia overview. Accessed 14 Jan 2016
  33. Yoshimoto S, Amarnath G (2017) Applications of satellite-based rainfall estimates in flood inundation modeling—a case study in Mundeni Aru River Basin, Sri Lanka. Remote Sens 9(10):998CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2017

Authors and Affiliations

  1. 1.International Water Management Institute (IWMI)Pelawatte, BattaramullaSri Lanka

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