Four Years of Meander‐Bend Evolution Captured by Drone‐Based Lidar Reveals Lack of Width Maintenance on the White River, Indiana, USA
Bibliographic record
Abstract
Abstract Meandering rivers experience fluctuations in width whenever riverbanks migrate in different directions or at different rates, which can be observed after individual floods. However, meandering rivers maintain approximately constant widths over decadal timescales. This implies some timescale below which width fluctuates as banks migrate independently, and above which width is maintained by a bank‐coupling process. This coupling is thought to occur either as point bar deposition events induce cutbank erosion (bar‐push), or as cutbank erosion events induce point bar deposition (bank‐pull). This coupling, however, has been challenging to observe in natural rivers due to limited event‐scale field data. We present results from a 4.5‐year campaign with 22 drone‐based lidar surveys of a single point bar and cutbank (∼0.35 km2 in area) on the White River near Worthington, Indiana, USA. The middle point bar experienced net erosion (5,400 m3), but net aggradation (17,100 m3) between 2019 and 2022 when including perennially submerged regions. This aggradation was less than the 35,700 m3 of cutbank erosion over the same period. Combined, we have observed widening (1.58 m/yr bend‐averaged; 3.08 m/yr near apex) over the study period as point bar deposition has not kept up with cutbank erosion. Finally, we suggest that the difference between bar‐push and bank‐pull as width‐maintenance mechanisms may not be resolvable by observing bend widening or narrowing alone without an advancement of current theory, such as determining a long‐term equilibrium width and measuring deviations relative thereto.
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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 | 0.000 |
| 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) | 0.001 | 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".