Winter/Spring Runoff Is Earlier, More Protracted, and Increasing in Volume in the Laurentian Great Lakes Basin
Bibliographic record
Abstract
Abstract Winter/spring runoff has changed in streams worldwide due to climate change, particularly in temperate areas where winter/spring streamflow depends on snowmelt. Such changes potentially affect receiving waters through altered nutrient loading and mixing patterns. The Laurentian Great Lakes are an important freshwater resource and have experienced a myriad of impacts due to climate change. We analyzed 70 years of stream gauge data in the Great Lakes Basin to test for changes in timing, duration, and amount of winter/spring runoff during the period 1950–2019. We found strong evidence for earlier runoff in each of the Great Lakes except Lake Erie, protracted winter/spring runoff throughout the Great Lakes Basin, and a higher runoff depth during the winter‐spring period over time for all watersheds except Lake Superior. Lake Ontario had the greatest change in the date by which 50% of the Jan–May runoff had been discharged (6 days earlier from 1950 to 2019). For winter/spring runoff duration, the most extreme change was observed in Lake Erie (increase of 19 days), and for runoff depth, the greatest change was in the Lake Huron Basin (increase of 3.3 cm). Results were similar for natural and impacted streams. Our results demonstrate dramatic changes in runoff patterns over the last seven decades in the Great Lakes Basin concomitant with previously published changes in precipitation and snowpack. Shifts toward earlier, more protracted, and more voluminous runoff likely change nutrient loading and mixing patterns that influence primary producers, particularly in the nearshore areas of the Great Lakes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".