Clustering for hydroclimatic extremes: a retrospective synthesis of the literature
Bibliographic record
Abstract
Earth system processes have complex physics and are dynamically interlinked, making modelling and predictions difficult. In particular, current challenges for hydroclimatic systems are in understanding nonstationarity and heterogeneity driven by climatic and human influences. Hence, studying the spatial and temporal occurrences and dependencies of hydroclimatic extremes is becoming increasingly important for water resources management and hydrological services. Research efforts that address these issues for extreme hydrological events are large and diverse in their approaches and methodologies. In this respect, multivariate statistical methods such as clustering are approaches commonly used to reveal mechanisms affecting floods and droughts in relation to their trends and magnitude as well as their variability in time and space. Clustering is a convenient tool to analyze large hydrometeorological datasets because of its unsupervised nature. However, there are no structured insights for hydrologists to reflect on the principles and findings of data clustering for hydroclimatic extremes. This contribution sheds light on the why’s and how’s of clustering methods for floods and droughts based on a systematic review of the literature. Our aim is to provide a synthesis of hydrological themes and methodological concepts found in papers that investigate floods and droughts through data clustering. These insights are valuable for guiding future applications of clustering methods while enabling wider discussions on the knowledge gaps for modelling extremes in hydroclimatic systems.
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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 | 0.011 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.032 | 0.034 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".