Drought Research in Canada: A Review
Bibliographic record
Abstract
Since human activities and ecosystem health are dependent on adequate, reliable water supplies, droughts pose a serious threat to society and the environment. Large-area, prolonged droughts are among Canada's costliest natural disasters having major impacts on a wide range of sectors including agriculture, forestry, industry, municipalities, recreation, health and society, and aquatic ecosystems. Although most regions of Canada experience drought, southern regions of the Canadian Prairies are more susceptible mainly because they experience high precipitation variability in time and space. This paper reviews relevant scientific research and program activities on droughts in Canada with an emphasis on the Canadian Prairies. Investigations into past trends and variability of drought occurrence in the instrumental and paleo-records are first examined. This is followed by a description of the existing body of knowledge regarding the large-scale atmospheric causes of Canadian drought. Studies into the potential occurrence of future droughts are also summarized. Current monitoring and modelling techniques, prediction capabilities and adaptation strategies related to Canadian droughts are then presented. The paper concludes with the identification of major research gaps and program needs that will aid our ability to understand and predict Canadian droughts, monitor and model their status, and adapt to their negative effects. R ésumé [Traduit par la rédaction] Puisque les activités humaines et la santé des écosystèmes dépendent d'approvisionnements en eau adéquats et fiables, les sécheresses constituent une menace sérieuse à la société et à l'environnement. Les sécheresses prolongées touchant de grandes superficies sont parmi les désastres naturels les plus coûteux au Canada et ont un impact important dans de nombreux secteurs, y compris l'agriculture, la foresterie, l'industrie, les municipalités, les activités récréatives, la santé et la société et les écosystèmes aquatique. Bien que la plupart des régions du Canada connaissent des sécheresses, c'est dans les régions du sud des Prairies canadiennes qu'il s'en produit le plus souvent, surtout parce que les précipitations y sont très variables dans le temps et dans l'espace. Le présent article passe en revue les activités de recherches et de programmes scientifiques portant sur les sécheresses au Canada, en accordant une attention particulière aux Prairies canadiennes. Nous examinons d'abord les études concernant les tendances et la variabilité des sécheresses dans le passé d'après les relevés instrumentaux et les renseignements paléolithiques. Nous faisons ensuite une description des connaissances actuelles sur les causes atmosphériques à grande échelle des sécheresses au Canada. Nous résumons aussi les études sur l'occurrence possible de sécheresses dans le futur. Nous présentons ensuite les techniques de surveillance et de modélisation, les capacités de prévision et les stratégies d'adaptation actuelles se rapportant aux sécheresses au Canada. En guise de conclusion, nous identifions les recherches nécessaires et les besoins des programmes visant à améliorer notre capacité de comprendre et de prévoir les sécheresses au Canada, de surveiller et de modéliser leur état et de nous adapter à leurs effets nuisibles.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.025 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".