Forms and drivers of annual streamflow variability in the headwaters of Canadian Prairies during the 20th century
Bibliographic record
Abstract
Abstract Headwater streamflows in the Rocky Mountain foothills are the key to water availability in the Canadian Prairies. Headwater characteristics, however, have been and continue to be subject to major variability and change. Here, we identify various forms of change in the annual mean streamflow and timing of the annual peak and attempt to distinguish between the effects of multiple drivers using a generalized regression scheme. Our investigation shows that the Pacific Decadal Oscillation (PDO) is the main driver of significant monotonic trends and shifts in the central tendency of annual mean streamflow in major headwaters. In parallel, the cumulative effects of non‐PDO climatic drivers and human‐induced land use and land management are the main causes of significant variations in the timing of the annual peak. Additional analyses show that time sequences with significant trends in annual mean streamflow and timing of the annual peak coincide with those that show significant trends in the PDO or non‐PDO component of the air temperature, respectively. The natural streamflow characteristics are substantially perturbed by anthropogenic river flow regulation, depending on the form of change and/or the level of regulation. Evidence suggests that the general tendency of human regulation is to alleviate the severity of above‐ and below‐average streamflow conditions; however, it may also intensify the variability in natural streamflow characteristics during drier years and/or those with earlier annual peak timing. These are circumstances to which the regional water resource system is vulnerable. Our findings are important for the provision of effective regional water resource management in the Canadian Prairies and contribute to a better understanding of the complex interactions between natural and anthropogenic drivers in coupled human–water systems.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".