Bivariate Drought Frequency Analysis in The Medjerda River Basin, Tunisia
Bibliographic record
Abstract
The climatology provides, for a given location or region, the time series of drought strength, the number, the mean duration, and the maximum duration of droughts of a given intensity.Similarly to most hydrological phenomena, droughts are characterized by a number of features such as their severity, duration and magnitude.Multivariate drought characterization has not been carried out in the various regions of the African continent despite the disastrous environmental, economic and social impacts of droughts.In the present paper, drought characteristics are modeled jointly in a multivariate frequency analysis (FA) framework for a data set from the Medjerda River, the principal watercourse in Tunisia.To identify drought events, the adopted threshold levels are estimated using the Flow Duration Curve (FDC) method.A sensitivity analysis to the threshold level is conducted.Results indicate that the drought features are significantly dependent and should be considered simultaneously for effective and rational modeling.Frank copula is shown to be the most appropriate copula model to represent drought features for the considered data set.The joint probabilities and bivariate return periods, based on the developed two dimensional copula models, are estimated in order to evaluate the contribution and advantages of bivariate modeling of droughts.These results are of practical relevance to hydrologists and water resources managers in Tunisia for applications in drought risk analysis and drought management, and in general for the optimal planning and management of water resources 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".