Assessment of the ability of the standardized precipitation evapotranspiration index (SPEI) to model historical streamflow in watersheds of Western Canada
Bibliographic record
Abstract
Knowledge of present-day spatial and temporal distribution of water resources is vital for successful water management and policies for planned adaptation to climate change. Measured quantities of hydroclimatic variables, including precipitation, evapotranspiration, streamflow, etc., are the primary indicators of water availability, and indices derived using several such primary variables provide a means to express water availability across a range of spatio-temporal scales. In this study, the ability of one such multi-scalar index, the Standardized Precipitation Evapotranspiration Index (SPEI), computed at a range of time scales, was examined to see how well it could model historically observed warm season monthly and annual streamflow in 24 natural-flowing watersheds of western Canada. The empirical relationships between the SPEI, computed at 1-, 3-, 6-, 9-, 12- and 24-month time scales, and monthly and annual streamflow were analyzed, showing significant correlations for all watersheds. The time scale of the SPEI with the strongest correlations varied seasonally. Based on these results, SPEI-based principal component regression (PCR) equations were calculated to model warm season monthly and annual historical streamflow. These PCR equations are able to adequately capture historical streamflow in these watersheds. Annual streamflow variability was better captured (mean Radj2 = 0.46) than monthly variability (mean Radj2 = 0.30 over March–October). Summer and fall streamflow variability was better captured (mean Radj2 = 0.42 over June–September) than spring variability (mean Radj2 = 0.15 over March–April).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".