Modelling Climate Change Impacts on Spring Runoff for the Rocky Mountains of Montana and Alberta I: Model Development, Calibration and Historical Analysis
Bibliographic record
Abstract
Water supply from mountain snowmelt is a key resource on the Great Plains. Hydrologists recognize that water yields could be significantly reduced in a warmer climate, with negative impacts upon regional water supplies. Weather data availability is usually sparse in alpine watersheds. Consequently, distributed alpine snow hydrology models are generally limited to small instrumented watersheds. Such models are unable to simulate the timing and magnitude of spring streamflow at a sufficient spatial scale for watershed management. In this study, the Simulated Grid microclimate model (SIMGRID) was refined and applied to the simulation of snow water equivalent (SWE) and spring streamflow volume in the headwaters of the St. Mary basin of northern Montana. Relationships between winter precipitation and elevation were derived from snow survey data. The SWE mass balance algorithm was enhanced to include the effect of rain-on-snow conditions, and to differentiate between snowmelt and rainfall runoff. Multiple regression analysis was used to relate predicted SWE and rainfall runoff to observed stream discharge (QS) at Babb, MT, for the 1961–1990 period. The refined SIMGRID model was then applied to the 1991–2004 period, and accurately simulated spring discharge (linear regression, r2 = 0.67). The refined SIMGRID model is capable of simulating spring runoff in poorly-instrumented complex terrain, at a scale of relevance to water resource managers. This paper presents the results of Part I of a two-part study, which assesses the impacts of climate change on spring runoff for the study watershed.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".