Assessment of the hydrological drought risk in Calgary, Canada using weekly river flows of the past millennium
Bibliographic record
Abstract
Abstract Planning and management of water resource infrastructure requires a depth of knowledge on the characteristics of hydrological extremes, floods and droughts. Infrastructure design is traditionally based upon historically observed extreme events, assuming that they are independent and identically distributed (i.i.d.) and stationary, i.e. they fluctuate within a fixed envelope of variability. Information on historical hydroclimate provides a limited range of hydrological extremes, which rarely includes long-term worst droughts. This study demonstrates the application of a paleo-environmental dataset, 900 years of weekly streamflow stochastically derived from a tree-ring reconstruction of annual streamflow, to assess the hydrological drought risk. The historic and prehistoric hydrological drought characteristics, i.e. severity–duration–frequency (SDF) relationships, are evaluated. The results indicate that the severity and duration of hydrological drought with the same recurrence interval is substantially larger and longer than those observed over the 100-year historical period. Historic and prehistoric drought SDF relationships established in this study demonstrate the implications of non-stationary climate in the analysis of extreme droughts. Therefore, projected droughts of the 21st century may not exceed the drought severity found in the prehistoric record to the same extent that they exceed historical droughts in the instrumental record. This study emphasizes the importance of paleohydrology in comprehending the region's drought.
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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) | 0.001 | 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 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".