Putting local wiggles back into rivers: a design workflow to reincorporate river width variations into historically straightened reaches
Bibliographic record
Abstract
Historical river management practices commonly involved river reach straightening (Wolf et al., 2021) where the planform variations of river location over some length were removed and replaced by a relatively straight downstream trend. Notably, river reach straightening generally also included a simplification of downstream river width variation such that re- constructed reaches were designed to convey specific flood magnitudes. Many decades later river management practices have changed to include river restoration and related efforts aimed at reviving river dynamics, associated functions and more recently resiliency in the face of climate change. Here, we offer a relatively straightforward approach in an attempt to meet these goals in some measure by reincorporating downstream river width variations into reaches that have been historically straightened.There is growing recognition that downstream river width variations at the local scale of order the channel width are a basic attribute of rivers (e.g. Richards, 1976; DeAlmeida et al., 2012), and therefore likely correlate with a more dynamic riverscape characerized, for example, by spatial differences of the local flow velocity and depth. Ecological theory suggests that a more dynamic riverscape with environmental gradients can promote biological recovery (Wohl et al., 2015), thus providing a link between potential recovery and resilience, and the reincorporation of downstream width variations along straightened river reaches. We use scaling theory (Chartrand et al., 2018) and an analytical model (Lei et al., 2024) to develop an open-source basic design workflow which produces example river reach geometries with downstream width variations which are evaluated using an open-source morphodynamic model. The design workflow can be incorporated into broader approaches and procedures used to develop testable restoration design alternatives, and, importantly, the proposed workflow can also help the restoration community work towards an improved conceptualization of river restoration (Wohl et al., 2015) for circumstances where river-adjacent land is not available and restoration options are constrained.References1. Wolf, S. et al., Environ Sci Eur 33, 15 (2021), https://doi.org/10.1186/s12302-021-00460-8.2. Richards, K. S., Geological Society of America Bulletin, 87, 883–883, 1976.3. de Almeida, G. A. M. et al., Geophysical Research Letters, 39, L06407–L06407, https://doi.org/10.1029/2012GL051059, 2012.4. Wohl, E.et al., Water Resources Research, 51, 5974–5997, https://doi.org/10.1002/2014WR016874, 2015.5. Chartrand, S. M. et al., Journal of Geophysical Research: Earth Surface, 123, 2735–2766, https://doi.org/10.1029/2017JF004533, 2018.6. Lei, Y. et al., Journal of Geophysical Research: Earth Surface, 129, e2024JF007641, https://doi.org/10.1029/2024JF007641, 2024.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.011 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".