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2D Finite Element Inundation Modelling in Anabranching Channels with Sparse Data: Examination of Uncertainties

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  • Joy Sanyal
  • Alexander Densmore
  • Patrice Carbonneau

Abstract

Flood inundation modelling in developing countries is severely limited by the lack of high resolution terrain data and suitable imagery to map flood extents. This study assessed the predictive uncertainty of modelled flood extents generated from TELEMAC2D model using low-cost, sparse input data commonly available in developing countries. We studied a river reach characterised by anabranching channels and river islands in eastern India. In this complex fluvial setting, we analysed computational uncertainty as a function of error in both satellite-derived flood-extent maps using a Generalised Likelihood Uncertainty Estimation (GLUE)-based approach. The model performance was quite sensitive to the uncertainty in the inflow hydrograph, particularly close to the flood peak. Evaluation of the flood inundation probability map, conditioned upon deterministic and probabilistic observed flood extents, reveals that the effect of using probabilistic observed data is only evident for portions of the model domain where the model output is free from consistent bias (over or under prediction) likely created by the imperfect terrain data. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • Joy Sanyal & Alexander Densmore & Patrice Carbonneau, 2014. "2D Finite Element Inundation Modelling in Anabranching Channels with Sparse Data: Examination of Uncertainties," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(8), pages 2351-2366, June.
    • Handle: RePEc:spr:waterr:v:28:y:2014:i:8:p:2351-2366
    DOI: 10.1007/s11269-014-0619-x
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    References listed on IDEAS

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    1. Sanjay Jain & R. Singh & M. Jain & A. Lohani, 2005. "Delineation of Flood-Prone Areas Using Remote Sensing Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 19(4), pages 333-347, August.
    2. Sanjay Jain & Arun Saraf & Ajanta Goswami & Tanvear Ahmad, 2006. "Flood inundation mapping using NOAA AVHRR data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 20(6), pages 949-959, December.
    3. Pradeep Adhikari & Yang Hong & Kimberly Douglas & Dalia Kirschbaum & Jonathan Gourley & Robert Adler & G. Robert Brakenridge, 2010. "A digitized global flood inventory (1998–2008): compilation and preliminary results," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 55(2), pages 405-422, November.
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    Cited by:

    1. Hriday Mani Kalita, 2016. "A New Total Variation Diminishing Predictor Corrector Approach for Two-Dimensional Shallow Water Flow," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(4), pages 1481-1497, March.
    2. Hriday Kalita, 2016. "A New Total Variation Diminishing Predictor Corrector Approach for Two-Dimensional Shallow Water Flow," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(4), pages 1481-1497, March.

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