IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i18p7371-d410668.html
   My bibliography  Save this article

Estimating Flood Characteristics Using Geomorphologic Flood Index with Regards to Rainfall Intensity-Duration-Frequency-Area Curves and CADDIES-2D Model in Three Iranian Basins

Author

Listed:
  • Farid Faridani

    (Department of European and Mediterranean Cultures, Environment, and Cultural Heritage (DiCEM), University of Basilicata, 75100 Matera, Italy
    National Council of Research-Institute of Methodologies for Environmental Analysis (CNR -IMAA), 85050 Tito Scalo, Italy)

  • Sirus Bakhtiari

    (Department of Water Engineering and Science, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran)

  • Alireza Faridhosseini

    (Department of Water Engineering and Science, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran
    College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter EX4 4QF, UK)

  • Micheal J. Gibson

    (College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter EX4 4QF, UK)

  • Raziyeh Farmani

    (College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter EX4 4QF, UK)

  • Rosa Lasaponara

    (Department of European and Mediterranean Cultures, Environment, and Cultural Heritage (DiCEM), University of Basilicata, 75100 Matera, Italy
    National Council of Research-Institute of Methodologies for Environmental Analysis (CNR -IMAA), 85050 Tito Scalo, Italy)

Abstract

There is not enough data and computational power for conventional flood mapping methods in many parts of the world, thus fast and low-data-demanding methods are very useful in facing the disaster. This paper presents an innovative procedure for estimating flood extent and depth using only DEM SRTM 30 m and the Geomorphic Flood Index (GFI). The Geomorphologic Flood Assessment (GFA) tool which is the corresponding application of the GFI in QGIS is implemented to achieved the results in three basins in Iran. Moreover, the novel concept of Intensity-Duration-Frequency-Area (IDFA) curves is introduced to modify the GFI model by imposing a constraint on the maximum hydrologically contributing area of a basin. The GFA model implements the linear binary classification algorithm to classify a watershed into flooded and non-flooded areas using an optimized GFI threshold that minimizes the errors with a standard flood map of a small region in the study area. The standard hydraulic model envisaged for this study is the Cellular Automata Dual-DraInagE Simulation (CADDIES) 2D model which employs simple transition rules and a weight-based system rather than complex shallow water equations allowing fast flood modelling for large-scale problems. The results revealed that the floodplains generated by the GFI has a good agreement with the standard maps, especially in the fluvial rivers. However, the performance of the GFI decreases in the less steep and alluvial rivers. With some overestimation, the GFI model is also able to capture the general trend of water depth variations in comparison with the CADDIES-2D flood depth map. The modifications made in the GFI model, to confine the maximum precipitable area through implementing the IDFAs, improved the classification of flooded area and estimation of water depth in all study areas. Finally, the calibrated GFI thresholds were used to achieve the complete 100-year floodplain maps of the study areas.

Suggested Citation

  • Farid Faridani & Sirus Bakhtiari & Alireza Faridhosseini & Micheal J. Gibson & Raziyeh Farmani & Rosa Lasaponara, 2020. "Estimating Flood Characteristics Using Geomorphologic Flood Index with Regards to Rainfall Intensity-Duration-Frequency-Area Curves and CADDIES-2D Model in Three Iranian Basins," Sustainability, MDPI, vol. 12(18), pages 1-16, September.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:18:p:7371-:d:410668
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/18/7371/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/12/18/7371/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Salvatore Manfreda & Caterina Samela & Andrea Gioia & Giuseppe Consoli & Vito Iacobellis & Luciana Giuzio & Andrea Cantisani & Aurelia Sole, 2015. "Flood-prone areas assessment using linear binary classifiers based on flood maps obtained from 1D and 2D hydraulic models," 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. 79(2), pages 735-754, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lin Lin & Zening Wu & Qiuhua Liang, 2019. "Urban flood susceptibility analysis using a GIS-based multi-criteria analysis framework," 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. 97(2), pages 455-475, June.
    2. Si Mokrane Siad & Andrea Gioia & Gerrit Hoogenboom & Vito Iacobellis & Antonio Novelli & Eufemia Tarantino & Pandi Zdruli, 2017. "Durum Wheat Cover Analysis in the Scope of Policy and Market Price Changes: A Case Study in Southern Italy," Agriculture, MDPI, vol. 7(2), pages 1-20, February.
    3. Kay Khaing Kyaw & Federica Bonaiuti & Huimin Wang & Stefano Bagli & Paolo Mazzoli & Pier Paolo Alberoni & Simone Persiano & Attilio Castellarin, 2024. "Fast-Processing DEM-Based Urban and Rural Inundation Scenarios from Point-Source Flood Volumes," Sustainability, MDPI, vol. 16(2), pages 1-22, January.
    4. Siad, Si Mokrane & Iacobellis, Vito & Zdruli, Pandi & Gioia, Andrea & Stavi, Ilan & Hoogenboom, Gerrit, 2019. "A review of coupled hydrologic and crop growth models," Agricultural Water Management, Elsevier, vol. 224(C), pages 1-1.
    5. Alahacoon, Niranga & Matheswaran, Karthikeyan & Pani, Peejush & Amarnath, Giriraj, "undated". "A decadal historical satellite data and rainfall trend analysis (2001–2016) for flood hazard mapping in Sri Lanka," Papers published in Journals (Open Access) H048581, International Water Management Institute.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:18:p:7371-:d:410668. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.