Autoregressive Noise, Deserialization, and Trend Detection and Quantification in Annual River Discharge Time Series
Bibliographic record
Abstract
The evaluation of long-term trends in yearly discharge records, such as annual peak daily flow or total annual runoff, is important to a variety of issues including water resource planning, flood hazard studies, and the assessment of historical data for evidence of anthropogenic climate change effects. Prewhitening or deserialization procedures have recently been developed and applied to adjust statistical tests of monotonic trend, and the nonparametric Mann-Kendall test in particular, for sensitivity to serial dependence. Deserialization attributes much or all of the observed serial correlation in a time series to an autoregressive process; however, deterministic processes can also lead to a large lag-1 serial correlation coefficient, and the physical basis for autoregressive noise may be weaker for annual rather than more finely-discretized (e.g., daily) streamflow records. In this paper, the potential consequences of using such procedures are investigated through a suite of Monte Carlo simulations. We find that prewhitening can substantially and inappropriately reduce the power of trend significance tests and increase slope estimate errors. The choice of whether deserialization is applied is to some degree left to the judgement and conservatism of the individual practitioner. We suggest that such procedures not be applied to a given annual hydrologic time series unless there is a strong site-specific physical basis for the assumption of AR(1) noise and that if deserialization is performed, very recently-developed multi-stage techniques appear preferable. We also present a number of useful ancillary results regarding trend identification in streamflow-derived data.
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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 | 0.008 | 0.032 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".