Fluvial system dynamics derived from distributed sediment budgets: perspectives from an uncertainty‐bounded application
Bibliographic record
Abstract
Abstract The utility of sediment budget analysis is explored in revealing spatio‐temporal changes in the sediment dynamics and morphological responses of a fluvial system subject to significant human impacts during the recent Anthropocene. Sediment budgets require a data‐intensive approach to represent spatially‐differentiated impacts adequately and are subject to numerous estimation uncertainties. Here, field and topographic surveys, historical data, numerical modelling and a representative‐area extrapolation method are integrated to construct a distributed, process‐based sediment budget that addresses historical legacy factors for the highly regulated Lagunitas Creek (213 km2), California, USA, for the period 1983–2008. Independent corroboration methods and error propagation analysis produce an uncertainty assessment unique to a catchment of this size. Current sediment yields of ~20 000 t a‐1 ± 6000 t a‐1 equate to unit rates of ~300 t km‐2 a‐1 ± 90 t km‐2 a‐1 over the effective sediment contributing area of 64 km2. This is comparable with yields associated with early Euro‐American settlement in the catchment, despite loss of sediment supply upstream of the two large dams. It occurs because ~57% of the sediment is now derived from incision‐related channel erosion. Further, the highly efficient routing of channel‐derived sediments in these incised channels suggests an efflux of 84% of contemporary sediment production, contrasting with the efflux of ≈10–30% reported for unregulated agricultural catchments. The results highlight that sediment budgets for regulated rivers must accommodate channel morphological responses to avoid significantly misrepresenting catchment yields, and that volumetric precision in sediment budgets may best be improved by repeat, spatially dense, channel cross‐section surveys. Human activities have impacted every aspect of the sediment dynamics of Lagunitas Creek (production, storage, transfer, rates of movement through storage), confirming that, while distributed sediment budgets are data demanding and subject to numerous error sources, the approach can provide valuable insights into Anthropocene fluvial geomorphology. Copyright © 2017 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".