13 Dec 2023
 | 13 Dec 2023

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

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

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 don't 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.

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

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-2190', Keith Beven, 19 Dec 2023
  • RC2: 'Comment on egusphere-2023-2190', Anonymous Referee #2, 06 Feb 2024
Brayden Noh, Omar Wani, Kieran B. J. Dunne, and Michael P. Lamb
Brayden Noh, Omar Wani, Kieran B. J. Dunne, and Michael P. Lamb


Total article views: 381 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
262 97 22 381 13 14
  • HTML: 262
  • PDF: 97
  • XML: 22
  • Total: 381
  • BibTeX: 13
  • EndNote: 14
Views and downloads (calculated since 13 Dec 2023)
Cumulative views and downloads (calculated since 13 Dec 2023)

Viewed (geographical distribution)

Total article views: 374 (including HTML, PDF, and XML) Thereof 374 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 04 Mar 2024
Short summary
In this manuscript, we propose a methodology to generate risk maps that provide the probabilities of erosion due to river migration. This methodology uses concepts from probability theory to learn the parameter values of the river migration model from satellite data while taking into account parameter uncertainty. Our analysis shows that such geomorphic risk estimation is more reliable than models that don't explicitly consider various sources of variability and uncertainty.