Predicting Future Flood Frequency Under Climate Change Using Copula Function
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 The joint behaviour of flood variables under climate change is of high importance for the economics of projects and risk reduction. This study investigates the implications of climate change using Gumbel–Hougaard copula function for future bivariate of flood peak and volume variables, in Azarshahr chay watershed. Canadian Earth system model (CanESM2) under three Representative Concentration Pathways (RCPs) along with statistical downscaling method (SDSM) and soil and water assessment tool (SWAT) were adopted to assess both baseline (1976–2005) and future (2030–2059) periods. Bivariate analysis of Copula improved the accuracy of model with an average NSE of 0.97 for all scenarios. Joint return period for severe floods has declined in the future, especially in RCP8.5. For a constant discharge and volume, joint return periods at the base period, RCP2.6, RCP4.5 and RCP8.5 were 24, 10, 13 and 9 years, respectively. Multivariate analysis may also provide useful information for flood risk assessment.
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 | 0.000 | 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 | 0.001 | 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) | 0.001 | 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