Trends and multi-decadal variability of annual maximum precipitation for Seoul, South Korea
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Flood risk management is an important and difficult problem for the densely populated and rapidly urbanised city of Seoul, South Korea. This study characterises long-term trends and variability in the city's annual maximum daily precipitation (AMP) over multiple decades. Smoothing the time series reveals that recent decades have witnessed a steep upward trend in AMP. Continuous wavelet analysis shows that the AMP series has statistically significant power in the 32–60-year periodicity band between 1880 and 1960 (one full cycle is clearly visible in the smoothed series). This feature has an even wider scope in the annual total precipitation series, suggesting that a real oscillation exists. Four climate indices were investigated as possible explanatory variables for the AMP series using cross-wavelet analysis, but no significant coherence between the signals was found. Finally, mean AMP forecasts based on three interpretations of the past linear trend are provided for flood risk management. Keywords: climate variabilityflood frequencymulti-decadaltrendprecipitationwavelet Acknowledgments This research was also supported by a grant (05 InfrastructureD03-1) from the Construction Infrastructure Technology Program funded by the Ministry of Construction & Transportation of Korean government.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it