Regional Differences in High Elevation Snowpack Decline Along the North American Rocky Mountains
Bibliographic record
Abstract
ABSTRACT The Rocky Mountains (RM) provide the ‘water towers’ for western North America, with deep winter snowpack accumulations that melt to contribute flows for the extensively utilised Columbia, Colorado, Saskatchewan, Missouri and Rio Grande River systems. With climate change, winter and spring warming are increasing seasonal and elevational rain versus snow proportions and altering the annual patterns of snowpack accumulation and melt. Prior studies have reported declines in snowpack extent or water content, especially on an index date, April 1. These declines could reflect reductions in the total annual snowpacks or earlier transitions to snowmelt. To resolve these influences, we assessed daily snowpack patterns at 314 snow pillow stations in the higher elevations along the 2500 km transboundary RM corridor, over three decades from 1991 to 2020. We found regional differentiation, with little change in the maximum snow water equivalent (SWE max ) or its timing (Day max ) in the most‐northerly, Canadian RM region (BC, AB); slight declines in the Northern US (ID, MT, WY) and Central US (UT, CO); and major declines in the Southern US (AZ, NM; average ΔSWE max : −2%/yr; ΔDay max : −0.75%/yr). With compound influences of declining SWE max and earlier Day max , the April 1 SWE (SWE Apr1 ) was more responsive, with progressive decline at some Northern US and Central US stations, and steep decline in the Southern US region (ΔSWE Apr1 : −6.5%/yr). Due to these compound influences, we recommend that future analyses include snowpack maxima and seasonality as well as April 1 measures, since that precedes the peak snowpack for higher elevation and northern sites, but follows the peak for lower and southern sites, confounding trend comparisons. Thus, higher elevation RM snowpacks are declining but with considerable latitudinal variation, displaying slight change in magnitude and seasonality in the northern regions, and greater change southward. These patterns contrast with some other climate change patterns that display increasing responsivity with higher latitude.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".