Estimates of future flow, including extremes, of the Columbia River headwaters
Bibliographic record
Abstract
Streamflow projections, including extremes, for the 2050s for the Columbia River headwaters above Donald are obtained by downscaling four regional climate models of the North American Regional Climate Change Assessment Program (NARCCAP) suite and subsequent driving of a hydrologic model. We employ the entire model chain from global and regional climate models, station‐based statistical downscaling, and a fully distributed, physically based hydrologic model and verify the results against observed streamflow. The performance is model dependent but is generally encouraging enough to justify the application of the climate scenarios. A general warming of about 2°C is projected and, on average, slightly drier conditions, especially in late summer. We find evidence that the projected changes are elevation dependent and relatively small scale, with decreasing signals with higher elevations. All models project a shift of the hydrograph toward a more rain‐fed regime, with peak flows occurring in June instead of July. Annual peak flow is projected to not increase, and August low flow decreases in all four models. With nonshrinking (static) glaciers, relatively high melting rates are simulated for August and September that partly compensate for the shifted hydrograph; this enhanced glacier melt is also detected in simulated historic Columbia headwater flow. The static approximation is supported by a heuristic seasonal sensitivity analysis that suggests a moderate average areal glacier recession of about 10% for the midcentury. We discuss the need for a dynamic glacier component for a refined assessment of future drought risk.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".