Meteorologically Related Factors Leading to the 2008, 2018, and 2019 Major Spring Floods in the Transboundary Saint John River (Wolastoq) Basin
Bibliographic record
Abstract
Abstract The flood-prone Saint John River (SJR, Wolastoq), which lies within a drainage basin of 55 110 km2, flows a length of 673 km from its source in northern Maine, United States, to its mouth in southern New Brunswick, Canada. Major industries in the basin include forestry, agriculture, and hydroelectric power. During the 1991–2020 reference period, the SJR basin (SJRB) experienced major spring flood events in 2008, 2018, and 2019. As part of the Saint John River Experiment on Cold Season Storms, the objective of this research is to characterize and contrast these three major spring flood events. Given that the floods all occurred during spring, the hypothesis being tested is that rapid snowmelt alone is the dominant driver of flooding in the SJRB. There were commonalities and differences regarding the contributing factors of the three flood years. When averaged across the upper basin, they showed consistency in terms of positive winter and spring total precipitation anomalies, positive snow water equivalent anomalies, and steep increases in April cumulative runoff. Rain-on-snow events were a prominent feature of all three flood years. However, differences between flood years were also evident, including inconsistencies with respect to ice jams and high tides. Certain factors were present in only one or two of the three flood years, including positive total precipitation anomalies in spring, positive heavy liquid precipitation anomalies in spring, positive heavy solid precipitation anomalies in winter, and positive temperature anomalies in spring. The dominant factor contributing to peak water levels was rapid snowmelt.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".