Print Email Facebook Twitter Geomorphic risk maps for river migration using probabilistic modeling Title Geomorphic risk maps for river migration using probabilistic modeling: A framework Author Noh, Brayden (California Institute of Technology) Wani, Omar (California Institute of Technology; New York University) Dunne, K.B.J. (TU Delft Rivers, Ports, Waterways and Dredging Engineering; California Institute of Technology) Lamb, Michael P. (California Institute of Technology) Date 2024 Abstract Lateral migration of meandering rivers poses erosional risks to human settlements, roads, and infrastructure in alluvial floodplains. While there is a large body of scientific literature on the dominant mechanisms driving river migration, it is still not possible to accurately predict river meander evolution over multiple years. This is in part because we do not fully understand the relative contribution of each mechanism and because deterministic mathematical models are not equipped to account for stochasticity in the system. Besides, uncertainty due to model structure deficits and unknown parameter values remains. For a more reliable assessment of risks, we therefore need probabilistic forecasts. Here, we present a workflow to generate geomorphic risk maps for river migration using probabilistic modeling. We start with a simple geometric model for river migration, where nominal migration rates increase with local and upstream curvature. We then account for model structure deficits using smooth random functions. Probabilistic forecasts for river channel position over time are generated by Monte Carlo runs using a distribution of model parameter values inferred from satellite data. We provide a recipe for parameter inference within the Bayesian framework. We demonstrate that such risk maps are relatively more informative in avoiding false negatives, which can be both detrimental and costly, in the context of assessing erosional hazards due to river migration. Our results show that with longer prediction time horizons, the spatial uncertainty of erosional hazard within the entire channel belt increases – with more geographical area falling within 25 % < probability < 75 %. However, forecasts also become more confident about erosion for regions immediately in the vicinity of the river, especially on its cut-bank side. Probabilistic modeling thus allows us to quantify our degree of confidence – which is spatially and temporally variable – in river migration forecasts. We also note that to increase the reliability of these risk maps, we need to describe the first-order dynamics in our model to a reasonable degree of accuracy, and simple geometric models do not always possess such accuracy. To reference this document use: http://resolver.tudelft.nl/uuid:ab8ff955-41c1-4b75-b81c-ba75e4ae984b DOI https://doi.org/10.5194/esurf-12-691-2024 ISSN 2196-632X Source Earth Surface Dynamics (ESurf), 12 (3), 691-708 Part of collection Institutional Repository Document type journal article Rights © 2024 Brayden Noh, Omar Wani, K.B.J. Dunne, Michael P. Lamb Files PDF esurf-12-691-2024.pdf 8 MB Close viewer /islandora/object/uuid:ab8ff955-41c1-4b75-b81c-ba75e4ae984b/datastream/OBJ/view