A climate-informed statistical framework to indirectly estimate trends in future seasonal high flows in snow-dominated watersheds using short-term climate variability indices
Bibliographic record
Abstract
The intensification of the hydrological cycle under climate change has brought changes in the temporal variability of flood-generating mechanisms and extreme hydrological events. To better anticipate these changes, modelling approaches integrating climate models, emissions scenarios, and hydrological models have been widely employed. However, their application remains challenging because of inherent uncertainties, in particular from hydrological models. This study aims to use a climate-informed statistical framework to indirectly estimate the temporal variability of seasonal high flows indices (HFI) using a set of short-term climate variability indices (SCI) characterizing likely causative mechanisms over different aggregated look-back periods. An ensemble of climate models, two future scenarios, and 31 SCIs were used to estimate future HFIs trends from 1997 to 2100 using as a proof of concept two snow-dominated watersheds in Southern Quebec, Canada. A statistical framework was used including linear and monotonic partial correlations along with significant trend tests. The results indicated that future temporal variability of HFIs could be anticipated using highly correlated SCIs as proxies. At least 50% of the HFI temporal variability was explained by a single SCI, such as cumulative total precipitation or climatic demands over 1 to 2 weeks, or drought indices like the Effective Drought Index (EDI) over 180 days. Furthermore, significant trends in highly correlated SCIs were consistent with significant trends observed in HFIs. These findings offer valuable insights for future analysis of HFI temporal variability, particularly in more comprehensive water management analyses aimed at informing regional mitigation and adaptation strategies. • A method was implemented to estimate future trends in seasonal high flows (HFI). • The method helps to bypass challenges of the hydrological model in hydroclimatic studies. • One climate variability index can explain at least 50% of HFI temporal variability. • Total precipitation and climatic demand indices are highly correlated with HFI. • The Effective Drought Index can assess HFI temporal variability in a changing climate.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".