Development and Experimental Validation of a Lagrangian Particle-Tracking Model to Simulate Wave-Driven Transport of Coastal Driftwood
Bibliographic record
Abstract
Wood deposits on beaches can contribute to coastal ecosystem function. However, driftwood and debris mobilized by storm waves pose hazards to coastal communities, infrastructure, and valued assets, including sensitive ecosystems. Predictive numerical tools are needed to guide risk assessment and management and opportunities to leverage the benefits of coastal driftwood. Models developed to predict driftwood fate and transport in rivers or by tsunami do not incorporate the effects of wind waves, surf-zone processes, or the wave–driftwood–shore interactions that exert important controls on coastal driftwood dynamics. Lagrangian transport models, developed to simulate oil spill and marine debris transport at oceanic scales, typically do not resolve beaching and washoff processes despite sensitivity to these mechanisms. Here, a novel Lagrangian model for simulating coastal driftwood transport by waves, which includes an efficient, dynamics-based beaching and washoff algorithm, was developed and compared to observations from a previous experimental study by the authors. Hydrodynamic forcing by two phase-resolving, nonlinear shallow water equation solvers (XBeach and SWASH) reveals that driftwood dynamics are sensitive to the vertical resolution of wave-induced velocities in the surf zone and swash zone velocity residuals. SWASH performed better than XBeach in driving the onshore-directed transport of driftwood observed in the experiments. For simulations where beaching occurred, the driftwood model (WOODRIFTSIM) reasonably reproduced mean transport and dispersion rates from the experiments, with some tuning of driftwood-beach interfacial friction coefficients. The sensitivity to friction coefficients confirmed that driftwood roughness is an important factor controlling mobility in wave-dominated settings. Residence time distributions of beached driftwood generally fit well to existing stochastic washoff models, except when extreme wave runup event interactions with beach morphologies resulted in fat tails in the washoff probability distributions. The driftwood model provides insight into factors affecting beaching and parameterization of probabilistic washoff algorithms for simulating buoyant debris transport on wave-dominated coasts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".