The ecohydrological vulnerability of a large inland delta to changing regional streamflows and upstream irrigation expansion
Bibliographic record
Abstract
Abstract Future climate change and anthropogenic interventions can alter historical streamflow conditions and consequently degrade the health and biodiversity of freshwater ecosystems. Future ecohydrological threats, however, are difficult to quantify using the cascade of climate and hydrological models due to various uncertainties involved. This study instead uses a fully bottom‐up approach to evaluate the ecohydrological vulnerability of the Saskatchewan River Delta (SRD), the largest inland delta in North America, to changing streamflow regime and irrigation expansion. An ensemble of perturbed streamflow sequences, along with scenarios of current and expanded irrigation, was generated and fed into a regional water resource system model. Results show that the streamflow regime in the delta is more sensitive to upstream changes in annual flow volume than peak flow timing and/or irrigation expansion. The sensitivity to changes in flow volume, however, may be intensified when combined with changes in peak timing. Shifts in the upstream peak flow timing can alter the magnitude and timing of peak flow to the delta, with prime importance to aquatic biota that are adapted to historical rhythmicity in peak flows and timing. Irrigation expansion decreases the magnitude and frequency of the peak flows, alters the frequency of average and low flows, and slightly shifts the timing of the mean annual peak flow in the SRD. This can lead to isolation of lakes and wetlands from the main stream. Our results highlight the ecohydrological vulnerability of the SRD under potential changing conditions and can assist in proposing adaptation policies to protect this ecosystem.
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".