Analysis of historical drought conditions based on SPI and SPEI at various timescales in the South Saskatchewan River Watershed, Alberta, Canada
Bibliographic record
Abstract
Abstract With global warming and alterations of climatic patterns, greater frequency and severity of extreme events are observed in different regions. Drought is one of the most challenging extreme events, one which can cause catastrophic and long-lasting impacts on water availability, environment, agriculture, economy, and societies. This study investigates the historic changes of drought conditions in Southern Alberta which is home to communities whose main economic activity is agriculture, or otherwise related to environmental variables, and has experienced severe droughts in the past. We employed SPI (standardized precipitation index) and SPEI (standardized precipitation-evapotranspiration index) as drought indicators at 1-, 3-, 6-, 9-, and 12-month(s) timescales, to explore significant changes during 1980–2018. The indices were computed using a Daymet-gridded dataset for the South Saskatchewan River Watershed (SSRW). Constructed drought occurrence maps revealed that, on average, in nearly half of the analysis period, the region experienced drought. Comparing four past decades showed that drought impacted a larger area in 2000–2010, and the percentage of the area impacted by severe or extreme droughts significantly increased in the last two decades, compared to 1980–2000. Results show decreasing trends of drought in some areas, mainly in the headwaters of Bow River in the Rocky Mountains, and increasing trends in larger areas, mostly found downstream. The result denotes expansion of the dry/wet conditions to larger areas at longer timescales. The extent of areas with a significant trend (increasing/decreasing) was greater with longer timescales of drought indices. Comparing the two drought indices suggests significant differences, and outperformance of SPEI in detecting dry conditions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| 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".