Warmer Winters Drive Declines in Snowpack and Consequent Increases in Annual and Seasonal Runoff in a Headwater Region of the Northeastern United States
Bibliographic record
Abstract
ABSTRACT In montane, snow‐affected regions of the United States, a warming climate threatens the timing and amount of future water delivery. It is expected that winter precipitation falling as snow will continue decreasing and the frequency of winter snowmelt events will continue increasing, with unknown impacts on the partitioning of water between evapotranspiration and runoff, water quality, flooding, and drought. The northeastern United States represents a humid climate with uniform precipitation seasonality and a transient snowpack. Limited research on changing winter conditions and water availability has been conducted in the region, in part due to scarce observations. An observational network has been recently established (2022) to span a Summit‐to‐Shore (S2S) continuum in Vermont for improved understanding and characterisation of snowpack variability across the landscape. We leverage the S2S network alongside available multi‐decade records of meteorology, snow depth, and runoff to relate long‐term snowpack characteristics in Vermont to seasonal and annual runoff within the high‐elevation headwater Ranch Brook watershed (9.6 km2). In the last 57 years, average winter temperatures have increased by 2.6°C, snow season length has decreased by almost 3 weeks, average snow depth has decreased by 16%, and winter season rain‐on‐snow (ROS) event frequency has increased from 1 to 3.5 per year. In response, average daily winter runoff has increased, which is strongly related to increased annual runoff ratios (R2 = 0.70). Separating the 22‐year runoff record into water years with more versus less winter runoff revealed that years with more winter runoff corresponded to increased winter temperatures, 15% smaller snowpack, two times more ROS events, 52% more winter runoff, 31% larger annual runoff ratio, and increased summer rainfall variance. A steady decline in the regional snowpack and related impacts on downstream water resources may have implications for ecosystems and agricultural, industrial, and domestic water supply.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".