Connecting the dots: teleconnection of global floods and their association with climate variability
Bibliographic record
Abstract
A fundamental question in global hydrology is how global floods behaved in the past and are expected to behave in the future. Previous site-specific analyses might offer locally relevant insights, but little is known about how floods are connected in space and time as well as their synchronous responses to climate variability at the global scale. Here we carry out empirical analyses based on a comprehensive dataset of annual maximum flood peak series from 4407 stream gaging stations. We establish the link between any two stream gages if their annual maximum flood peak discharges are significantly correlated and the dates of their occurrences are sufficiently close (using event synchronization and complex network). Our results identify notable remote links of annual flood peak series over western Canada/US (e.g., upper Missouri River basin), northern Europe (e.g., Kemijoki River basin), southern China (e.g., middle Yangtze River basin), and northern South America (e.g., Amazon River basin). Annual flood peak series are linked to their local neighbors (within a distance of 4500 km) over eastern United States, central Europe, and eastern Australia. Remote links highlight the spatial dependence of riverine floods at the global scale. These links are dictated by the oscillation of dominant climate modes over the Pacific Ocean (e.g., El Niño Southern Oscillation, Pacific Decadal Oscillation) and their resultant anomalous atmospheric circulation patterns. Local flood clusters are more responsive to region-specific atmospheric forcings. The complex flood network plays an important role in regulating the dynamic behaviors of flood hazards. Our results offer new insights into global flood hydrology and their connections with large-scale climate forcings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".