Spatio-temporal patterns and trends in MODIS-retrieved radiative forcing by snow impurities over the Western US from 2001 to 2022
Bibliographic record
Abstract
Abstract The seasonal mountain snowpack of the Western US (WUS) is a key water resource to millions of people and an important component of the regional climate system. Impurities at the snow surface can affect snowmelt timing and rate through snow radiative forcing (RF), resulting in earlier streamflow, snow disappearance, and less water availability in dry months. Predicting the locations, timing, and intensity of impurities is challenging, and little is known concerning whether snow RF has changed over recent decades. Here we analyzed the relative magnitude and spatio-temporal variability of snow RF across the WUS at three spatial scales (pixel, watershed, regional) using remotely sensed RF from spatially and temporally complete (STC) MODIS data sets (STC-MODIS Snow Covered Area and Grain Size/MODIS Dust Radiative Forcing on Snow) from 2001 to 2022. To quantify snow RF impacts, we calculated a pixel-integrated metric over each snowmelt season (1st March–30th June) in all 22 years. We tested for long-term trend significance with the Mann–Kendall test and trend magnitude with Theil–Sen’s slope. Mean snow RF was highest in the Upper Colorado region, but notable in less-studied regions, including the Great Basin and Pacific Northwest. Watersheds with high snow RF also tended to have high spatial and temporal variability in RF, and these tended to be near arid regions. Snow RF trends were largely absent; only a small percent of mountain ecoregions (0.03%–8%) had significant trends, and these were typically decreasing trends. All mountain ecoregions exhibited a net decline in snow RF. While the spatial extent of significant RF trends was minimal, we found declining trends most frequently in the Sierra Nevada, North Cascades, and Canadian Rockies, and increasing trends in the Idaho Batholith. This study establishes a two-decade chronology of snow impurities in the WUS, helping inform where and when RF impacts on snowmelt may need to be considered in hydrologic models and regional hydroclimate studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.006 | 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 teacher head, 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".