Hydrological Responses of Headwater Basins to Monthly Perturbed Climate in the North American Cordillera
Bibliographic record
Abstract
Abstract How mountain hydrology at different elevations will respond to climate change is a challenging question of great importance to assessing changing water resources. Here, three North American Cordilleran snow-dominated basins—Wolf Creek, Yukon; Marmot Creek, Alberta; and Reynolds Mountain East, Idaho—each with good meteorological and hydrological records, were modeled using the physically based, spatially distributed Cold Regions Hydrological Model. Model performance was verified using field observations and found adequate for diagnostic analysis. To diagnose the effects of future climate, the monthly temperature and precipitation changes projected for the future by 11 regional climate models for the mid-twenty-first century were added to the observed meteorological time series. The modeled future was warmer and wetter, increasing the rainfall fraction of precipitation and shifting all three basins toward rainfall–runoff hydrology. This shift was largest at lower elevations and in the relatively warmer Reynolds Mountain East. In the warmer future, there was decreased blowing snow transport, snow interception and sublimation, peak snow accumulation, and melt rates, and increased evapotranspiration and the duration of the snow-free season. Annual runoff in these basins did not change despite precipitation increases, warming, and an increased prominence of rainfall over snowfall. Reduced snow sublimation offset reduced snowfall amounts, and increased evapotranspiration offset increased rainfall amounts. The hydrological uncertainty due to variation among climate models was greater than the predicted hydrological changes. While the results of this study can be used to assess the vulnerability and resiliency of water resources that are dependent on mountain snow, stakeholders and water managers must make decisions under considerable uncertainty, which this paper illustrates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 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".