Earth Surface Dynamics
Earth Surface Dynamics
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Articles | Volume 12, issue 3
Articles | Volume 12, issue 3
https://doi.org/10.5194/esurf-12-691-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esurf-12-691-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 12, issue 3
Research article
 | Highlight paper
 | 
08 May 2024
Research article | Highlight paper |  | 08 May 2024

Geomorphic risk maps for river migration using probabilistic modeling – a framework

Brayden Noh, Omar Wani, Kieran B. J. Dunne, and Michael P. Lamb

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Cited articles

Benda, L. and Dunne, T.: Stochastic forcing of sediment supply to channel networks from landsliding and debris flow, Water Resour. Res., 33, 2849–2863, 1997a. a
Benda, L. and Dunne, T.: Stochastic forcing of sediment routing and storage in channel networks, Water Resour. Res., 33, 2865–2880, 1997b. a
Benda, L. E., Miller, D. J., Dunne, T., Reeves, G. H., and Agee, J. K.: Dynamic landscape systems, River ecology and management: lessons from the Pacific coastal ecoregion, Springer, New York, 261–288, ISBN 978-0-387-95246-8, 1998. a
Beven, K.: A manifesto for the equifinality thesis, J. Hydrol., 320, 18–36, https://doi.org/10.1016/j.jhydrol.2005.07.007, 2006. a
Beven, K. and Lane, S.: On (in)validating environmental models. 1. Principles for formulating a Turing‐like Test for determining when a model is fit-for purpose, Hydrol. Process., 36, e14704, https://doi.org/10.1002/hyp.14704, 2022. a, b, c
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Editor
River meandering is a long standing source of interest for scientists interested in how the Earths surface is shaped. Here, Noh et al., use a novel probablistic approach to create geomorphic risk maps where areas that have the potential for meandering can be assessed in an arguably more rigorous way than before.
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Short summary
In this paper, we propose a framework for generating risk maps that provide the probabilities of erosion due to river migration. This framework uses concepts from probability theory to learn the river migration model's parameter values from satellite data while taking into account parameter uncertainty. Our analysis shows that such geomorphic risk estimation is more reliable than models that do not explicitly consider various sources of variability and uncertainty.
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