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Geomorphic Flood Index 2.0: Enhanced Tools for Delineating Flood-Prone Areas in Data-Scarce Regions

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In recent years, significant advancements in geomorphic methods have offered a cost-effective and valuable alternative for large-scale flood mapping. Among these, the Geomorphic Flood Index (GFI) has gained widespread adoption for flood delineation applications globally (Samela et al., 2017). The development of the GFA-tool plug-in for QGIS has helped to disseminate and simplify the application of this approach and boosted its popularity (Samela et al., 2018).The GFI builds on Digital Elevation Models (DEMs) information on water levels in each drainage network cell and elevation differences between each river basin location and the closest stream channel cell hydrologically connected to it. However, in  its current original formulation, certain limitations exist that can affect its usability and reliability (Albertini et al., 2021). In fact, near confluences, floodwater may not strictly follow river connectivity patterns and secondary tributary floodplains may be partially submerged due to the backflow from the mainstream.To address these challenges, a new methodology has been developed that explicitly takes into consideration confluences.  This improved approach enhances the robustness of the index and provides more reliable flood mapping, even in complex settings such as large alluvial valleys. The method further improves the reliability of flood depth estimations obtained through this approach. References:Albertini, C., D. Miglino, V. Iacobellis, F. De Paola, S. Manfreda, Flood-prone areas delineation in coastal regions using the Geomorphic Flood Index, Journal of Flood Risk Management, e12766, 2021.Samela, C., R. Albano, A. Sole, S. Manfreda, A GIS tool for cost-effective delineation of flood-prone areas, Computers, Environment and Urban Systems, 70, 43-52, 2018.  Samela, C., T.J. Troy, S. Manfreda, Geomorphic classifiers for flood-prone areas delineation for data-scarce environments, Advances in Water Resources,  102, 13-28, 2017.
Title: Geomorphic Flood Index 2.0: Enhanced Tools for Delineating Flood-Prone Areas in Data-Scarce Regions
Description:
In recent years, significant advancements in geomorphic methods have offered a cost-effective and valuable alternative for large-scale flood mapping.
Among these, the Geomorphic Flood Index (GFI) has gained widespread adoption for flood delineation applications globally (Samela et al.
, 2017).
The development of the GFA-tool plug-in for QGIS has helped to disseminate and simplify the application of this approach and boosted its popularity (Samela et al.
, 2018).
The GFI builds on Digital Elevation Models (DEMs) information on water levels in each drainage network cell and elevation differences between each river basin location and the closest stream channel cell hydrologically connected to it.
However, in  its current original formulation, certain limitations exist that can affect its usability and reliability (Albertini et al.
, 2021).
In fact, near confluences, floodwater may not strictly follow river connectivity patterns and secondary tributary floodplains may be partially submerged due to the backflow from the mainstream.
To address these challenges, a new methodology has been developed that explicitly takes into consideration confluences.
  This improved approach enhances the robustness of the index and provides more reliable flood mapping, even in complex settings such as large alluvial valleys.
The method further improves the reliability of flood depth estimations obtained through this approach.
 References:Albertini, C.
, D.
Miglino, V.
Iacobellis, F.
De Paola, S.
Manfreda, Flood-prone areas delineation in coastal regions using the Geomorphic Flood Index, Journal of Flood Risk Management, e12766, 2021.
Samela, C.
, R.
Albano, A.
Sole, S.
Manfreda, A GIS tool for cost-effective delineation of flood-prone areas, Computers, Environment and Urban Systems, 70, 43-52, 2018.
  Samela, C.
, T.
J.
Troy, S.
Manfreda, Geomorphic classifiers for flood-prone areas delineation for data-scarce environments, Advances in Water Resources,  102, 13-28, 2017.

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