Assessment of the Laurentian Great Lakes’ hydrological conditions in a changing climate
Bibliographic record
Abstract
Abstract A set of 28 simulations from five regional climate models are used in this study to assess the Great Lakes’ water supply from 1953 to 2100 following emissions scenarios RCP4.5 and 8.5 with a focus on bi-weekly changes in the means and extremes of hydrological variables. Models are first evaluated by comparing annual cycles of precipitation, runoff, evaporation and net basin supply (NBS) with observations. Trends in mean values are then studied for each variable using Theil-Sen’s statistical test. Changes in extreme conditions are analyzed using generalized extreme values distributions for a reference period (1971–2000) and two future periods (2041–2070 and 2071–2100). Ensemble trend results show evaporation increases of 136 and 204 mm (RCP4.5 and RCP8.5) over the Great Lakes between 1953 and 2100. Precipitation increases by 83 and 140 mm and runoff increases by 68 and 135 mm. Trends are not equally distributed throughout the year as seasonal changes differ greatly. As a result, Great Lakes net basin supply is expected to increase in winter and spring and decrease in summer. Over the entire year, NBS increases of 14 and 70 mm are projected for scenarios RCP4.5 and 8.5 respectively by the year 2100. An analysis of extreme values reveals that precipitation and NBS maxima increase by 11 to 27% and 1 to 9% respectively, while NBS minima decrease by 18 to 29% between 1971–2000 and 2041–2100.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".