Dynamics of Water Table Depths in Forested Wetlands Under Temperate Climate Conditions Using Multi-Frequential Periodogram Analysis and Wavelet Analysis
Bibliographic record
Abstract
The temporal variation of water-table depths (WTD) of wetlands has been acknowledged as their hydrological signature; it integrates the different inputs and outputs of their water budget and provides insights into their seasonal hydrological services at the watershed scale. In this study, we monitored the WTD fluctuations of six forested wetlands in the St. Charles River watershed, Quebec, Canada, from 2019 to 2022. A factor analysis of mixed data and a hierarchical classification of the principal components were performed to first characterize the wetlands of the study area and to guide the selection and in-situ monitoring of a few. Those selected were instrumented with wells equipped with a water level data logger, recording hourly total pressure and temperature. We applied a multi-frequential periodogram analysis to the observed time series to extract periodicities. The time series were also transformed using the Morlet wavelet, and WTD interactions with precipitations and lateral stream flows was explored using cross-wavelet analysis. Our results show that hydroconnectivity represents a good criterion for characterizing the diversity of wetlands at the watershed scale, as reflected in the distinct WTD patterns of isolated and riparian wetlands. Using wetland scale observed data, we also demonstrate the cascading effect between precipitation, wetland WTD increase, and lateral inflow to the river network, which illustrates the damping effect of these ecosystems on the transfer from precipitation to stream flows at local scale. These new insights provide guidance for improving the simulation of wetlands in hydrological models.
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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 | 0.001 | 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".