Drought evolution in North American river basins: attribution analysis through a Lagrangian approach
Bibliographic record
Abstract
Drought events have become more frequent and severe across North America, threatening water availability in river basins and thus ecosystem and socio-economic development. This is why in this study, we investigate the occurrence, evolution, and attribution of drought conditions in nine major North American river basins, the Colorado, Columbia, Fraser, Mackenzie, Mississippi, Rio Grande, Saskatchewan-Nelson, St. Lawrence, and Yukon. The analysis was performed on a spatio-temporal scale for the period 1980-2018. Precipitation data from MSWEP and CRU were used, as well as terrestrial water storage from GRACE. In addition, the Lagrangian moisture contribution from oceanic and terrestrial origin to precipitation over the basins, named PLO and PLT, respectively, were used. Drought indices such as the Standardised Precipitation Index (SPI), Standardised Precipitation-Evapotranspiration Index (SPEI), and Drought Severity Index (DSI) were used to assess the occurrence of dry conditions at various temporal scales. In addition to the attribution of the occurrence and severity of drought extremes due to PLO and PLT deficits, the trend was assessed. The results show that despite the differentiated nature of precipitation origin between the western and eastern basins, in most of them, a joint coupling prevails in the occurrence of positive or negative trends of dry/wet conditions of oceanic and terrestrial origin, which ultimately modulate the evolution of dry/wet conditions in the basins.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".