Predicting Drought Durations and Magnitudes at Weekly Time Scale: Constant Flow as a Truncation Level
Bibliographic record
Abstract
At times hydrological droughts are defined using Q90 or Q95 (90% or 95% flow being equaled or exceeded) as truncation levels regardless of their seasonal variations. Truncation at a constant level of flow compared to a variable level (i.e. mean or median level for each season) postures unique statistical problems in modeling of drought durations and magnitudes (deficit volumes). This paper develops a procedure for predicting a T-week drought duration, E(LT ) for a weekly flow sequence based on the concept of standardized hydrological index (SHI). The SHI series were modeled using the first order Markov chain model (MC-1) while being truncated at a constant flow level such as Q90 or Q95. A T-week drought magnitude (standardized) was predicted using the relationship E(MT ) = α× I×E(LT ), where α is a scaling factor to account for the difference between averaged out (week-by-week) standard deviation and the overall standard deviation of the weekly flows, I is the drought intensity whose characteristics are assumed to resemble a truncated normal distribution of weekly deficits, and E(LT ) is based on the zero order Markov chain model (MC-0) of drought lengths. This analytical approach can be construed as distribution free, since simple and first order conditional probabilities of droughts are empirically estimated from the SHI series derived from weekly flow records irrespective of their underlying probability distribution function. Predictive ability of the proposed procedure has been found to be satisfactory for E(LT ) and E(MT ) at Q90 to Q95 truncation levels.
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How this classification was reachedexpand
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".