Controls of contemporary (2001–2018) springtime waterflow dynamics in a Large, snowmelt-dominated basin in northeastern North America
Bibliographic record
Abstract
The objective of this study was to characterise the primary forcing variables and system feedback responsible for daily waterflow dynamics within a large, international river system (Canada and USA) during 17 melt seasons from 2001 to 2018. An analysis based on extreme gradient boosting showed that daily waterflow in four subcatchments of the upper Saint John River (SJR, Wolastoq) basin during the 17 melt seasons was to a large measure controlled by the area’s seasonal warming associated with the springtime increase in regional incident global radiation and northeasterly advection of sensible and latent heat from southerly locations. Historically, seasonal surges in air temperature and cumulative snow degree-days were shown to contribute to roughly 60% of the control on subcatchment discharge by influencing the production and timing of snowmelt. Peak accumulation of snow on the ground provided the second most important control of discharge, accounting for about 15.6% of the overall control at a daily timescale. Cumulative short- and long-term forest cover losses in the four subcatchments provided some control, but at varying levels (i.e., 4.8–14.2%) dependent on the extent of total forest cover loss and other subcatchment traits. Convergent cross mapping confirmed the unidirectional, causal relationship between annual forest cover loss and daily discharge rates at the outlet of three of the four subcatchments. The strength of the annual-forest-cover-removal-to-daily-discharge signal within the four subcatchments varied, with the subcatchment with the least annual forest cover loss (<1%, over the 17 years), predictably displaying the weakest signal (p = 0.282). Forest cover removal was shown to increase springtime discharge for all subcatchments, albeit at different rates. This work provides a more comprehensive, mechanistic interpretation of daily snowmelt control of stream/river flow dynamics in northeastern North America.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".