An Assessment of the Impacts of Climate Change on the Upper Clackamas River Basin with a Distributed Hydrologic Model
Bibliographic record
Abstract
The Pacific Northwest is dependent on seasonal snowmelt for water resources that support a significant portion of its economy. Increased temperatures resulting from higher concentrations of atmospheric greenhouse gases may cause disruptions to these resources because of reductions in the annual snowpack and variations of the timing of snowmelt. This study reconstructs and applies a 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 (located near Portland, Oregon). Historic flow data and snow measurements are used to calibrate and test the performance of the hydro logic model for a contemporary period (1971-2000), and the model is run for two future scenarios (2010-2039 and 2070-2099) using IS92 climate change scenarios from two global climate circulation models (Hadley and Canadian Centre for Climate) as inputs. The results forecast 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 to mean winter runoff by the 2070-2099 period vary from moderate (13.7%) to large (46.4%), depending on the changes to precipitation forecasted by the global climate circulation models. 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 similar studies for the entire Columbia 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".