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A multi-algorithm approach for modeling coastal wetland eco-geomorphology
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  • Zeli Tan,
  • L. Ruby Leung,
  • Chang Liao,
  • Luca Carniello,
  • Jose F. Rodriguez,
  • Patricia M. Saco,
  • Steven G. Sandi
Zeli Tan
Pacific Northwest National Laboratory (DOE)

Corresponding Author:[email protected]

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L. Ruby Leung
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Chang Liao
Pacific Northwest National Laboratory
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Luca Carniello
University of Padova
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Jose F. Rodriguez
University of Newcastle
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Patricia M. Saco
The University of Newcastle
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Steven G. Sandi
Deakin University
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Coastal wetlands play an important role in the global water and biogeochemical cycles. Climate change is making them more difficult to adapt to the fluctuation of sea levels and other environment changes. Given the importance of eco-geomorphological processes for coastal wetland resilience, many eco-geomorphology models differing in complexity and numerical schemes have been developed in recent decades. But their divergent estimates on the response of coastal wetlands to climate change indicate that substantial structural uncertainties exist in these models. To investigate the structural uncertainty of coastal wetland eco-geomorphology models, we developed a multi-algorithm model framework of eco-geomorphological processes, such as mineral accretion and organic matter accretion, within a single hydrodynamics model. The framework is designed to explore possible ways to represent coastal wetland eco-geomorphology in Earth system models and reduce the related uncertainties in global applications. We tested this model framework at three representative coastal wetland sites: two saltmarsh wetland (Venice Lagoon and Plum Island Estuary) and a mangrove wetland (Hunter Estuary). Through the model-data comparison, we showed the importance to use a multi-algorithm ensemble approach for more robust predictions of the evolution of coastal wetlands. We also find that more observations of mineral and organic matter accretion at different elevations of coastal wetlands and evaluation of the coastal wetland models at different sites of diverse environments can help reduce the model uncertainty.
12 Sep 2023Submitted to ESS Open Archive
13 Sep 2023Published in ESS Open Archive