Surface storage dynamics in large rivers: Comparing three‐dimensional particle transport, one‐dimensional fractional derivative, and multirate transient storage models
Bibliographic record
Abstract
Large rivers are major conduits for sediment and nutrient transport and play an important role in global biogeochemical cycles. While smaller rivers received attention in recent decades for hyporheic exchange and nutrient uptake, fewer studies have focused on the dynamics of surface storage zones in large rivers. We investigate transport dynamics in the St. Clair River, an international river straddling the U.S.–Canadian border, using a combination of modeling and dye tracer studies. We describe a calibrated three‐dimensional hydrodynamic model to generate (synthetic) breakthrough data to evaluate several classes of 1‐D solute transport models for their ability to describe surface storage dynamics. Breakthrough data from the 3‐D particle transport model exhibited multimodal behavior and complex dynamics that could not be described using a single first‐order exchange coefficient—an approach often used to describe surface storage in transient storage models for small rivers. The 1‐D models examined include multirate transient storage (MRTS) models in which storage zones were arranged either in series or parallel as well as 1‐D models based on fractional derivatives. Results indicate that for 1‐D models to describe data adequately, the timing of solute pulses that correspond to various in‐channel features such as sandbars, islands or meander bends should be taken into account. As a result, the MRTS model with storage zones arranged in series (i.e., exchange rates triggered sequentially) provided the best description of the data. In contrast, fractional derivative models that assume storage zones were arranged in parallel failed to capture the multimodal nature of the breakthrough curves.
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 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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".