Empirical observations and numerical modelling of tides, channel morphology, and vegetative effects on accretion in a restored tidal marsh
Bibliographic record
Abstract
Abstract Tidal marshes form at the confluence between estuarine and marine environments where tidal movement regulates their developmental processes. Here, we investigate how the interplay between tides, channel morphology, and vegetation affect sediment dynamics in a low energy tidal marsh at the Paul S. Sarbanes Ecosystem Restoration Project at Poplar Island. Poplar Island is an active restoration site where fine‐grained material dredged from navigation channels in the upper Chesapeake Bay are being used to restore remote tidal marsh habitat toward the middle bay (Maryland, USA). Tidal currents were measured over multiple tidal cycles in the inlets and tidal creeks of one marsh at Poplar Island, Cell 1B, using Acoustic Doppler Current Profilers (ADCP) to estimate water fluxes throughout the marsh complex. Sediment fluxes were estimated using acoustic backscatter recorded by ADCPs and validated against total suspended solid measurements taken on site. A high‐resolution geomorphic survey was conducted to capture channel cross sections and tidal marsh morphology. We integrated simple numerical models built in Delft3d with empirical observations to identify which eco‐geomorphological factors influence sediment distribution in various channel configurations with differing vegetative characteristics. Channel morphology influences flood‐ebb dominance in marshes, where deep, narrow channels promote high tidal velocities and incision, increasing sediment suspension and reducing resilience in marshes at Poplar Island. Our numerical models suggest that accurately modelling plant phenology is vital for estimating sediment accretion rates. In‐situ observations indicate that Poplar Island marshes are experiencing erosion typical for many Chesapeake Bay islands. Peak periods of sediment suspension frequently coincide with the largest outflows of water during ebb tides resulting in large sediment deficits. Ebb dominance (net sediment export) in tidal marshes is likely amplified by sea‐level rise and may lower marsh resilience. We couple field observations with numerical models to understand how tidal marsh morphodynamics contribute to marsh resilience. © 2019 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".