Russian nesting dolls effect – Using wavelet analysis to reveal non‐stationary and nested stationary signals in water yield from catchments on a northern forested landscape
Bibliographic record
Abstract
Abstract Determining catchment responses to climate signals gives insight into the potential effects of climate change. This study tested the hypothesis that a 28‐year time series of water yields from four headwater catchments in the Turkey Lakes Watershed (TLW), Ontario contains signals of non‐stationary climate change and naturally occurring stationary climate oscillations and that the effects of these signals are greater in catchments with lower rates of change in water loading and lower water storage capacity (small wetlands). Non‐stationary trends explained 0%, 18%, 44%, and 52% of the variance in the water yields of the four catchments. Wavelet analysis using Morlet wavelets identified stationary responses at multiple temporal scales, increasing the amount of variance explained to 56%, 63%, 76%, and 81% when combining stationary and non‐stationary models. The catchment with low water loading and low water storage was most sensitive to non‐stationary and stationary signals, suggesting that these catchments act as sentinels to detect climatic signals. Wavelet coherence analysis revealed correlations between global climate oscillation indices and water yield. The Atlantic Multidecadal Oscillation (AMO) index was strongly correlated with both temperature and precipitation ( R 2 = 0.46, P < 0.001 and R 2 of 0.34, P < 0.001, respectively). Temperature in the TLW increased by 0.067 °C per year from 1981 to 2008, but approximately 0.037 °C of this increase can be explained by the AMO index. While it is likely that anthropogenic climate change impacts water yields, it is important to account for multiple nested climate oscillations to avoid exaggerating its effects. Copyright © 2012 John Wiley & Sons, Ltd.
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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) | 0.000 | 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) | 0.000 | 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".