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Earth Surface Dynamics An interactive open-access journal of the European Geosciences Union
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© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 22 Apr 2020

Submitted as: research article | 22 Apr 2020

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This preprint is currently under review for the journal ESurf.

Dominant process zones in a mixed fluvial-tidal delta are morphologically distinct

Mariela Perignon1, Jordan Adams2, Irina Overeem2,3, and Paola Passalacqua1 Mariela Perignon et al.
  • 1Department of Civil, Architectural and Environmental Engineering, Cockrell School of Engineering, The University of Texas at Austin, Austin, Texas, USA
  • 2Community Surface Dynamics Modeling System (CSDMS), Institute of Arctic and Alpine Research, University of Coloradoat Boulder, Boulder, Colorado, USA
  • 3Department of Geological Sciences, University of Colorado at Boulder, Boulder, Colorado, USA

Abstract. The morphology of deltas is determined by the spatial extent and variability of the geomorphic processes that shape them. While in some cases resilient, deltas are increasingly threatened by natural and anthropogenic forces, such as sea level rise and land use change, which can drastically alter the rates and patterns of sediment transport. Quantifying process patterns can improve our predictive understanding of how different zones within delta systems will respond to future change. Available remotely sensed imagery can help but appropriate tools are needed for pattern extraction and analysis. We present a method for extracting information about the nature and spatial extent of active geomorphic processes across deltas from ten parameters quantifying the geometry of each of 1239 islands and the channels around them using machine learning. The method consists of a two-step unsupervised machine learning algorithm that clusters islands into spatially continuous zones based on the ten morphological metrics extracted from remotely sensed imagery. By applying this method to the Ganges–Brahmaputra–Meghna Delta, we find that the system can be divided into six major zones. Classification results show that active fluvial island construction and bar migration processes are limited to relatively narrow zones along the main Ganges River and Brahmaputra and Meghna corridors, whereas zones in the mature upper delta plain, with smaller fluvial distributary channels stand out as their own morphometric class. The classification also shows good correspondence with known gradients in the influence of tidal energy with distinct classes for islands in the backwater zone and in the purely tidally-controlled region of the delta. Islands at the delta front, under the mixed influence of tides, fluvial-estuarine construction, and local wave reworking have their own characteristic shape and channel configuration. The method does not distinguish between islands with embankments (polders) and natural islands in the nearby mangrove forest (Sundarbans), suggesting that human modifications have not yet altered the gross geometry of the islands beyond their previous natural morphology. These results demonstrate that machine learning and remotely sensed imagery are useful tools for identifying the spatial patterns of geomorphic processes across delta systems.

Mariela Perignon et al.

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Mariela Perignon et al.

Mariela Perignon et al.


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Latest update: 02 Jul 2020
Publications Copernicus
Short summary
We propose a machine learning approach for the classification and analysis of large delta systems. The approach uses remotely sensed data, channel network extraction, and the analysis of 10 metrics to identify clusters of island with similar characteristics. The 14 clusters are grouped into 6 main classes related to morphological processes acting on the system. The approach allows to identify spatial patterns in large river deltas to inform modeling and the collection of field observations.
We propose a machine learning approach for the classification and analysis of large delta...