Do Meteorological, Agricultural, and Hydrological Indicators All Point to an Increased Frequency and Intensity of Droughts Across Canada Under a Changing Climate?
Bibliographic record
Abstract
Droughts, one of the most significant natural hazards, are complex in nature with varying definitions typically tailored to the timing and/or duration of the episode along with associated impacts. Although previous investigations have assessed future drought occurrence across Canada, none have comprehensively and collectively assessed changes to meteorological, agricultural, and hydrological drought indicators using CMIP6 GCM projections. The main objective of this study was to assess future drought conditions across Canada at various temporal scales using standardized indices representing meteorological, agricultural, and hydrological droughts under multiple shared socio-economic pathways for the near (2041–2060) and far (2081–2100) future. On an annual basis, projected changes to all three drought indicators signify increased drying across the Prairies, portions of interior British Columbia, and most of Ontario. This drying is greater and covers more of the country during the warm season (April to September), while in summer and to a lesser extent autumn, widespread changes are only projected for meteorological and agricultural indicators. In spring, increased dry conditions are only prevalent in meteorological and hydrological indices. The cold season of October to March essentially shows little to no drying in any type of drought. Changes in all drought indices are amplified for higher SSPs and during the late century. This study improves an understanding of the spatial and temporal variations in projected changes to various drought types across Canada in response to human-induced warming. While results from this analysis are applicable for nation-wide drought assessments and drought management plans, they are less suitable for application at local scales where more detailed modelling may be required.
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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 | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".