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Record W2013480876 · doi:10.1080/07055900.2011.626757

Characterization and Summary of the 1999–2005 Canadian Prairie Drought

2011· article· en· W2013480876 on OpenAlexaffvenueabout
John Hanesiak, Ronald E. Stewart, Barrie Bonsal, Phillip Harder, R. G. Lawford, Rabah Aider, B. D. Amiro, Eyad H. Atallah, Alan Barr, T. A. Black, Paul Bullock, Julian Brimelow, Ross Brown, Hannah Carmichael, Chris Derksen, Lawrence B. Flanagan, Philippe Gachon, H. Greene, John R. Gyakum, William Henson, Edward H. Hogg, Bohdan Kochtubajda, H. G. Leighton, Charles A. Lin, Yi Luo, J. H. McCaughey, Alison Meinert, Amir Shabbar, K. R. Snelgrove, Kit K. Szeto, Alexander P. Trishchenko, Garth van der Kamp, Shusen Wang, Elaine Wheaton, C. Wielki, Yan Yang, Sitotaw Z. Yirdaw, Tianshan Zha

Bibliographic record

VenueATMOSPHERE-OCEAN · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Drought Analysis
Canadian institutionsMemorial University of NewfoundlandSaskatchewan Research Council (Canada)OuranosQueen's UniversityUniversity of LethbridgeUniversity of SaskatchewanNatural Resources CanadaAlberta Environment and Protected AreasEnvironment and Climate Change CanadaUniversity of British ColumbiaMcGill UniversityUniversity of Manitoba
Fundersnot available
KeywordsGeographyForestry

Abstract

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Droughts are among the world's most costly natural disasters and collectively affect more people than any other form of natural disaster. The Canadian Prairies are very susceptible to drought and have experienced this phenomenon many times. However, the recent 1999–2005 Prairie drought was one of the worst meteorological, agricultural and hydrologic droughts over the instrumental record. It also had major socio-economic consequences, adding up to losses in the billions of dollars. This recent drought was the focus of the Drought Research Initiative (DRI), the first integrated network focusing on drought in Canada. This article addresses some of the key objectives of DRI by providing a collective summary, understanding and synthesis of the 1999–2005 drought. Bringing together the many datasets used in this study was in itself a major accomplishment. This drought exhibited many important, and sometimes surprising, features. This includes, for example, (1) a non-steady large-scale atmospheric circulation (and sea surface temperature) pattern that mainly resulted in subsidence over the region but also cold and warm periods in its evolution; such features have typically not occurred in previous droughts; (2) large spatial gradients between wet and dry areas that, in some instances, were linked with major precipitation events; and (3) many impacts at and below the earth's surface that occurred with varying temporal lags from the meteorological conditions and, in response, these impacts would have fed back onto the character of the drought (e.g., the surface-convection feedback). The drought's complexity poses enormous challenges for its simulation and prediction at all temporal scales. High-resolution models coupled with the surface are needed to address these and many other issues identified in this article. R ésumé [Traduit par la rédaction] Les sécheresses sont parmi les catastrophes naturelles les plus coûteuses et, collectivement, affectent plus de gens que toute autre forme de catastrophe naturelle. Les Prairies canadiennes sont très vulnérables aux sécheresses et ont souvent subi ce phénomène. Toutefois, la récente sécheresse de 1999–2005 dans les Prairies a été l'une des pires sécheresses météorologiques, agricoles et hydrologiques enregistrées depuis que l'on effectue des relevés. Elle a aussi eu d'importantes conséquences socio-économiques, les dommages se chiffrant en milliards de dollars. C'est à cette sécheresse récente que s'est intéressé le Réseau de recherche sur la sécheresse (DRI), le premier réseau intégré consacré à la sécheresse au Canada. Le présent article traite de certains des objectifs clés du DRI en fournissant un résumé général, une compréhension et une synthèse de la sécheresse de 1999–2005. Le seul fait de rassembler les nombreux ensembles de données utilisés dans cette étude était en soi un accomplissement remarquable. Cette sécheresse présentait plusieurs caractéristiques importantes et quelquefois surprenantes. Parmi celles-ci : (1) une configuration de circulation atmosphérique transitoire à grande échelle (et de température de la surface de la mer) qui a généralement causé de la subsidence dans la région mais aussi des périodes de froid et de chaleur au cours de son évolution; ces caractéristiques ne se sont généralement pas produites lors des sécheresses précédentes; (2) de forts gradients spatiaux entre les zones humides et sèches qui, dans certains cas, étaient liés à des événements de précipitations extrêmes; et (3) plusieurs conséquences à la surface et sous la surface de la terre qui se sont produites avec des retards variables par rapport aux conditions météorologiques et qui auraient à leur tour rétroagi sur le caractère de la sécheresse (par exemple, rétroaction de convection de surface). La complexité de la sécheresse pose des défis énormes pour sa simulation et sa prévision à toutes les échelles temporelles. Des modèles à haute résolution couplés avec la surface sont nécessaires pour traiter ces questions et plusieurs autres mentionnées dans cet article.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.013
Science and technology studies0.0020.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.007
GPT teacher head0.187
Teacher spread0.179 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations81
Published2011
Admission routes3
Has abstractyes

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