Simulating debris flow and levee formation in the 2D shallow flow model D-Claw: Channelized and unconfined flow
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Abstract
Debris flow runout poses a hazard to life and infrastructure. The expansion of human population into mountainous areas and onto alluvial fans increases the need to predict and mitigate debris flow runout hazards. Debris flows on unconfined alluvial fans can exhibit spontaneous self-channelization through levee formation that reduces lateral spreading and extends runout distances compared to unchannelized flows. Here we modify the D-Claw shallow flow model in two ways that are hypothesized to generate levees. We evaluate these modifications with observations from a large-scale flume experiment. We investigate model performance when including the effect of two different friction sub-models, as well as the inclusion of segregation effects on granular permeability. Results show that, for a wide range of plausible model input parameters, simulations including the effects of segregation promoted modeled levee formation, whereas simulations without the effects of segregation did not create levees. Further, using a forward predictive framework, simulations with the effects of segregation were more likely to better model the magnitude of debris flow depth and runout distance, whereas simulation timing of the debris flow was affected by the choice of friction sub-model. Our results indicate that including the effects of segregation on granular permeability can improve the likelihood of better predictions of debris flow depth and runout prior to an event occurring.
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Simulating debris flow and levee formation in the 2D shallow flow model D-Claw: Channelized and unconfined flow |
| Series title | Earth and Space Science |
| DOI | 10.1029/2022EA002590 |
| Volume | 10 |
| Issue | 2 |
| Year Published | 2023 |
| Language | English |
| Publisher | Wiley |
| Contributing office(s) | Geologic Hazards Science Center, Volcano Science Center, Advanced Research Computing (ARC) |
| Description | e2022EA002590, 20 p. |
| Google Analytic Metrics | Metrics page |