A novel algorithmic framework for identifying changing streamflow regimes: Application to Canadian natural streams (1966–2010)
Bibliographic record
Abstract
Abstract. Climate change significantly affects natural streamflow regime. To assess alterations in streamflow regime, typically few streamflow characteristics are considered and their significant variations in time and space are taken as a notion of change. Although, this approach is informative, intuitively appealing and widely-implemented, (1) it cannot see simultaneous changes in multiple streamflow characteristics; (2) it does not utilize all the available information contained in a streamflow hydrograph; and (3) it cannot describe how and to what extent one streamflow regime evolves into other regime types. To address these gaps, we conceptualize streamflow regimes as intersecting spectrums that are formed by multiple streamflow characteristics. Accordingly, we recognize that changes in streamflow regime should be diagnosed through gradual, yet continuous changes in an ensemble of streamflow characteristics. To incorporate these key considerations, we propose a fuzzy clustering-based approach to classify the natural streamflow into a finite set of intersecting regime types. Accordingly, by analyzing how the degrees of membership to regime types change, we quantify monotonic shifts between regime types in time and space. Our proposed algorithm eliminates the subjectivity in quantifying shift between flow regimes, and can extract valuable knowledge stored in the shape and variability of annual streamflow hydrographs. We apply this approach to the natural streamflow data, obtained from 106 Canadian gauges, during the period of 1966 to 2010. We show that natural streamflow in Canada can be categorized into six regime types, with clear physical and geographical distinctions. Analyses of trends in membership values during the study period show that alterations in natural streamflow regime are vibrant and can be different within and between major Canadian drainage basins. We show that gradual changes in natural streamflow regimes in Canada can be attributed to simultaneous changes in a large number of streamflow characteristics, some of which have been previously unknown or not well-attended. Our study introduces a generic algorithmic framework for identifying changing streamflow regime at regional and global scales, and provides a fresh look at streamflow alterations in Canada, which can be seen as another line of evidence for the complex and multifaceted impacts of climate change on streamflow regime, particularly in cold regions.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".