Hydrologic impacts of climate change in the Upper Clackamas River Basin, Oregon, USA
Bibliographic record
Abstract
The Pacific Northwest of the USA is dependent on seasonal snowmelt for water resources that support its economy and aquatic ecosystems. Increased temperatures resulting from higher concentrations of atmospheric greenhouse gases may cause disruptions to these resources because of reductions in the annual snowpack and the earlier occurrence of seasonal snowmelt. We applied a Geographic Information System (GIS)-based distributed hydrologic model at a monthly scale to assess the effects of future climate change on runoff from the Upper Clackamas River Basin (UCB; located near Portland, Oregon, USA). Once validated using historic flow data, the model was run for 2 future time periods (2010–2039 and 2070–2099) using climate change simulations from 2 global circulation modelling groups (HadCM2 from the Hadley Centre for Climate Prediction and Research, and CGCM1 from the Canadian Centre for Climate Modelling and Analysis) as inputs. The model runs projected that mean peak snowpack in the study area will drop dramatically (36 to 49% by 2010–2039, and 83 to 88% by 2070–2099), resulting in earlier runoff and diminished spring and summer flows. Increases in mean winter runoff by 2070–2099 vary from moderate (13.7%) to large (46.4%), depending on the changes to precipitation projected by the general circulation models (GCMs). These results are similar to those of other studies in areas dependent on snowpack for seasonal runoff, but the reductions to snowpack are more severe in this study than in similar studies of the entire Columbia River Basin, presumably because the elevations of much of the Upper Clackamas Basin are near the current mid-winter snow line.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".