Modeling the impacts of climate change on streamflow of the Nicolet River as affected by snowmelt using ArcSWAT
Bibliographic record
Abstract
In the Nicolet River watershed of Southern Quebec, Canada, runoff resulting from snowmelt is responsible for the spring peak flow, which may result in flooding when temperature rises rapidly in a short time. In this study, streamflow modeling for the Nicolet River watershed was conducted, then the impacts of climate change on the hydrology of this basin were studied by comparing the streamflow characteristics of historical and projected future climate data under a wide range of climate change scenarios. The Soil and Water Assessment Tool (SWAT), was calibrated and validated against the observed streamflow for the period of 1986-1990 and 1991-2000, respectively. The ArcSWAT model was shown to be a reliable tool for simulating the stream flow (PBIAS within 15%, Nash-Sutcliffe efficiency (NSE) > 0.50 and RMSE-observations standard deviation ratio (RSR) < 0.70) in both calibration and validation phases. Driven by the climate inputs, the ArcSWAT model, showed the capacity to simulate the watershed’s response to climate change impacts. While projected changes in precipitation would directly result in the change in surface runoff, concomitant change in temperature would affect evapotranspiration and snowfall, as well as snowmelt in winter. Thus, future streamflow in the Nicolet River can be predicted by ArcSWAT model from projected change in climate variables. The impacts of climate change on the streamflow of the study area was studied by comparing the streamflow characteristics under current (1986-2000) and eleven projected future climate datasets (2053-2067) which result from embedding the Regional Climate Model (RCM) into the Global Climate Model (GCM). All the climate scenarios suggest an increase in average temperature (+2.6 ℃) and precipitation (+21%), with the increase being particulraly significant in winter. On average, snowfall is projected to decrease by 6%. The snowmelt is projected to decrease in total volume, though the snowmelt pattern shows an increase in late winter and earlier spring snowmelt. There are general increases in mean annual streamflow, summer, autumn and winter streamflow, whereas spring flow is expected to decrease. When averaging all scenarios, peak flows are predicted to increase by 13%, and occur earlier. A study discriminating between the effects of temperature and precipitation on peak flows shows their increases to be attributable to increased precipitation, whereas their timing is to be mostly influenced by increases in temperature.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".