Spring flood reconstruction from continuous and discrete tree ring series
Bibliographic record
Abstract
This study proposes a method to reconstruct past spring flood discharge from continuous and discrete tree ring chronologies, since both have their respective strengths and weaknesses in northern environments. Ring width or density series provide uninterrupted records that are indirectly linked to regional discharge through a concomitant effect of climate on tree growth and streamflow. Conversely, discrete event chronologies constitute conspicuous records of past high water levels since they are constructed from trees that are directly damaged by the flood. However, the uncertainty of discrete series increases toward the past, and their relationships with spring discharge are often nonlinear. To take advantage of these two sources of information, we introduce a new transfer model technique on the basis of generalized additive model (GAM) theory. The incorporation of discrete predictors and the evaluation of the robustness of the nonlinear relationships are assessed using a jackknife procedure. We exemplify our approach in a reconstruction of May water supplies to the Caniapiscau hydroelectric reservoir in northern Quebec, Canada. We used earlywood density measurements as continuous variables and ice‐scar dates around Lake Montausier in the James Bay area as a discrete variable. Strong calibration (0.57 < 0.61 < 0.75) and validation (0.27 < 0.44 < 0.58) R 2 statistics were obtained, thus highlighting the usefulness of the model. Our reconstruction suggests that, since ∼1965, spring floods have become more intense and variable in comparison with the last 150 years. We argue that a similar procedure can be used in each case where discrete and continuous tree ring proxies are used together to reconstruct past spring floods.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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 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".