High-resolution assessment of riverbank erosion and stabilization techniques with associated water quality implications
Bibliographic record
Abstract
Agriculture is a key contributor to poor water quality, but the sources of sediment and nutrient losses from agricultural catchments – including from riverbank erosion – are highly variable. Riverbank erosion is particularly difficult to quantify and control. Here, we developed a quick assessment approach to quantify riverbank erosion rates and associated sediment and nutrient loading rates into waterways using airborne LiDAR combined with field-collected data. We applied this approach and explored its relationships to water quality at four sites within the Blackwater catchment in Northern Ireland for two analysis periods. GIS LiDAR image differencing revealed that volume changes in riverbank elevation equated to average erosion rates which indicated spatial and temporal variability in erosion rates. Combining the erosion rates with in-situ riverbank bulk density and total extractable phosphorus content provided sediment and phosphorus loading rates. The relative differences between estimated erosion at the different sites corresponded well with in-stream suspended sediment variations, but patterns for total phosphorus concentrations were more complex. We conclude that the use of LiDAR combined with field data is an innovative means for riverbank erosion quantification. Furthermore, by using LiDAR-to-LiDAR analyses, the reductions in erosion, sediment, and phosphorus loading rates following riverbank stabilization techniques can be determined.
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 | 0.001 | 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) | 0.000 | 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".