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Using Sentinel-1 Imagery to Assess Predictive Performance of a Hydraulic Model

Author

Listed:
  • Ioanna Zotou

    (National Technical University οf Athens)

  • Vasilis Bellos

    (National Technical University οf Athens)

  • Angeliki Gkouma

    (National Technical University οf Athens)

  • Vassilia Karathanassi

    (National Technical University οf Athens)

  • Vassilios A. Tsihrintzis

    (National Technical University οf Athens)

Abstract

This study seeks to test the predictive performance of a hydraulic model using as reference the flood extent extracted through Sentinel-1 imagery. A precipitation event which took place between the 22nd and 28th of February 2018 in Pineios river basin, Central Greece, was analyzed. A threshold technique was performed to delineate the inundation extent from the satellite image, whereas both HEC-HMS and HEC-RAS software were coupled to simulate the examined storm event. To assess model response, the flooded area derived through the modeling approach was compared against that derived from the satellite image processing, using an area-based measure of fit. Furthermore, an uncertainty analysis on the parameters of the hydrologic model was elaborated to investigate their impact on the results of the hydraulic model. The sensitivity of the latter to the value of the roughness coefficient as well as to changes in the spatial resolution of the utilized topography was also examined. Considering as a perfect response of the model its complete coincidence with the satellite image product, it was found that the hydraulic model performance ranged between 61.04%-65.49%, depending on the selected upstream flow hydrograph, topography and roughness coefficient. The upstream flow conditions proved to play a more critical role, while roughness coefficient and topography were found to cause slighter changes in model response.

Suggested Citation

  • Ioanna Zotou & Vasilis Bellos & Angeliki Gkouma & Vassilia Karathanassi & Vassilios A. Tsihrintzis, 2020. "Using Sentinel-1 Imagery to Assess Predictive Performance of a Hydraulic Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(14), pages 4415-4430, November.
    • Handle: RePEc:spr:waterr:v:34:y:2020:i:14:d:10.1007_s11269-020-02592-7
    DOI: 10.1007/s11269-020-02592-7
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    Cited by:

    1. Debajit Das & Tilottama Chakraborty & Mrinmoy Majumder & Tarun Kanti Bandyopadhyay, 2023. "Estimation of Runoff Under Changed Climatic Scenario of a Meso Scale River by Neural Network Based Gridded Model Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(8), pages 2891-2907, June.
    2. Luis Garrote & Alvaro Sordo-Ward, 2020. "Preface to the Special Issue: Managing Water Resources for a Sustainable Future," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(14), pages 4307-4311, November.
    3. Jaka Budiman & Jarbou Bahrawi & Asep Hidayatulloh & Mansour Almazroui & Mohamed Elhag, 2021. "Volumetric Quantification of Flash Flood Using Microwave Data on a Watershed Scale in Arid Environments, Saudi Arabia," Sustainability, MDPI, vol. 13(8), pages 1-14, April.

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