CSDMS 2016 annual meeting poster AlejandroTejedor
Quantifying delta complexity toward inference and classification
Anthony Longjas, St. Anthony Falls Laboratory Minneapolis Minnesota, United States. alongjas@umn.edu
Efi Foufoula-Georgiou, St. Anthony Falls Laboratory Minneapolis Minnesota, United States. efi@umn.edu
Abstract:
Delta channel networks self-organize to a variety of stunning and complex patterns in response to different forcings (e.g., river, tides and waves), sediment composition, incoming flow variability, sea level rise, etc. Recently, we presented a rigorous framework based on spectral graph theory to study delta channel networks from a topologic (channel connectivity) and dynamic (flux exchange) perspective for advancing our understanding of deltas as complex systems [Tejedor et al., 2015a,b]. The question that we aim to answer in this work is how the complexity of delta channel networks evolves as the delta grows and how it depends on a specific physical parameter namely the incoming sediment size. To explore the dependence of complexity with sediment composition, we have used numerical modeling (Delft3D) where the different geomorphic parameters can be controlled and/or isolated. We have analyzed the channel networks of river-dominated deltas that arise from using different size distributions of the incoming sediment. The results of our analysis show how complexity metrics (topologic and dynamic) are able not only to capture the variability in the delta structure, but also quantify the increase of complexity when the sediment composition transitions to coarser grains. Furthermore, from a joint analysis of field and simulated deltas within this quantitative framework, we showed encouraging results and provided preliminary evidence toward a path for quantitative delta classification by exploring similarities and discrepancies in the underlying processes and the resulting network complexity.
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