Investigating Terrestrial Water Storage Response to Meteorological Drought in the Canadian Prairies
Bibliographic record
Abstract
The Canadian Prairies region is considered a climate change hot spot due to the extreme drought events and their impacts on water resources. The overall goal of this research is to understand the linkage between meteorological droughts and Total Water Storage (TWS) variations in the Canadian Prairies. To achieve this goal, a diversified database is collected and analyzed by geostatistical tools and cross-wavelet transform approach. It concerns a multitude of climatic data (four CMIP6 multi-model datasets) and satellite observations (GRACE data). The results indicate that: (1) the models overestimate the precipitation rate over the Canadian Prairies, and the Norwegian Earth System Model version 2 (NorESM2–LM) is the most suitable model for the context of the Canadian Prairies; (2) Sen’s slope estimator of annual rainfall can reach −2.5 mm/year/year, with a decreasing magnitude of trends in the NE to SW direction; (3) the Standardized Precipitation Index (SPI) and the Modified China-Z Index (MCZI) demonstrate that, in the past, most of the climatological years were near normal with some extremely dry years (1952, 2000, 2003, and 2015) and one extremely wet year (1960); (4) the projections in the far future indicate an increase in the number of extremely dry years (2037, 2047, 2080, 2089, and 2095); (5) the combined analysis of GRACE-derived TWS and drought indices show the direct impact of the meteorological drought periods on the water resources. The TWS values decreased from 23 cm in 2002 to −54 cm in 2020, indicating a significant water reserve decline in the region. The results of this study are expected to provide a valuable perspective to understand the dynamic of hydrosystems in a climate change context in the Canadian Prairies.
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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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".