Quantifying the effects of water management decisions on streambank stability
Bibliographic record
Abstract
Both natural processes and human activities alter streamflow conditions, which can significantly affect streambank erosion and stability, leading to consequences such as sedimentation of reservoirs, contamination of streams, loss of productive land, and damage to infrastructure. Hydrological conditions, which are often controlled by water management decisions and infrastructure (e.g., reservoirs and dams), are a major factor affecting streambank erosion and stability. Extensive research has explored the relationships between hydrology, water management, and streambank stability. However, limited studies directly address the impacts of water management decisions, particularly reservoir operations, on the driving mechanisms of streambank stability such as changes in pore water pressure, pressure differentials between the surface and subsurface, and gravitational forces versus shear stress. This study builds upon these existing concepts by integrating them into a model that accounts for both the effects of water management and inherent hydrologic conditions on streambank stability. The module estimates streambank stability using a factor of safety approach, with hydrologic conditions derived from an established integrated hydrologic model, HydroGeoSphere, coupled with the surface water operations model, OASIS. This module is validated and then demonstrated using simulations from the Lower Republican River Basin in Kansas, United States. Results indicate that several water management decisions, such as groundwater pumping and timing of reservoir releases, may negatively affect streambank stability by changing pore water pressure, the weight of the bank material, and the pressure differential between the surface and the subsurface. Given that most of the rivers and streams of the world are regulated by reservoir operations, this work demonstrates that water management practices need to be considered in simulations of streambank stability.
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 | 0.000 | 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) | 0.000 | 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".