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Deriving Flood-Mediated Connectivity between River Channels and Floodplains: Data-Driven Approaches.

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New methods, progressive nearest neighbor search (PNNS) and progressive iterative nearest neighbor search (PiNNS), accurately map river-floodplain connectivity for improved flood hazard assessment and ecological understanding.

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Area of Science:

  • Hydrology
  • Geomorphology
  • Environmental Science

Background:

  • River-floodplain connectivity is crucial for flood hazard mapping and ecological processes.
  • Traditional nearest neighbor search (NNS) methods struggle with spatial heterogeneity and continuity in deriving this connectivity.

Purpose of the Study:

  • To develop novel data-driven approaches for deriving river-floodplain connectivity.
  • To improve flood hazard mapping and ecological impact assessments.

Main Methods:

  • Developed progressive nearest neighbor search (PNNS) and progressive iterative nearest neighbor search (PiNNS).
  • Applied forward tracking to identify flood pathways and backward tracing to connect inundated floodplain cells to river channels.
  • Utilized spatial-temporal data, including hydrodynamic and remote sensing data.

Main Results:

  • PNNS and PiNNS successfully identified flood pathways and connectivity between river channels and floodplains.
  • PiNNS ensured continuous connectivity by considering intermediate inundated cells.
  • Identified specific river channel sections responsible for widespread floodplain inundation.

Conclusions:

  • The proposed PNNS and PiNNS methods offer a robust solution for deriving flood-mediated connectivity.
  • These approaches enhance flood hazard assessment, river basin planning, and management strategies.
  • The methods are adaptable for various spatial-temporal datasets, including hydrodynamic and remote sensing data.