Quantifying trends in indicator hydroecological variables for regime‐based groups of Canadian rivers
Bibliographic record
Abstract
Abstract As a key contribution to Canada's Ecosystem Status and Trends (ESTR) national assessment, the goal of our study was to utilize available flow data as a surrogate of habitat suitability for aquatic ecological communities, and examine temporal trends in hydroecological variables over the 1970–2005 period. Daily flow data were extracted from the Reference Hydrological Basin Network, and an agglomerative hierarchical classification method was used to identify homogenous regions with similar seasonality of the flow regime. Six regime regions were identified reflecting the timing of the annual peaks and low flows in addition to the patterns in the rising and falling limbs. For each of the gauging station sites, the magnitude, duration, timing, frequency, and rate of change of annual hydrological events were quantified through 32 ecologically important hydrological variables. Long‐term patterns in the hydroecological variables were quantified using the nonparametric Mann‐Kendall trend statistic. The results revealed more trends than would be expected to occur by chance for most variables. Clear regional trend patterns were observed within individual regime groups demonstrating the often differing response to environmental variability within the different regions. Results at the national scale were highly variable, but trends towards increased variability in river flows were observed with a predominant increasing trend in the number of flow reversals over the water year and decreasing trend in the annual low‐flow indices. The identified river regime regions offer an initial framework for scientific investigation of hydroecological patterns and an opportunity to move towards a more predictive approach to environmental flows assessment in sustainable resource management and planning. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".