Propagation Characteristics of Hydrological Drought Based on Variable and Fixed Threshold Methods in Snowmelt and Rainfall Driven Catchments
Bibliographic record
Abstract
Based on long-term (>30 years) monthly streamflow data from two catchments with different hydrological features, i.e., snowmelt-driven in Harp Lake, south-central, Canada and rainfall-driven in Dongjiang river, south China, the differences in the hydrological drought (HD) propagation characteristics identified by fixed (FDT) and variable drought thresholds (VDT) were explored. The results showed that (i) despite both FDT and VDT methods being able to describe HD propagation patterns well (i.e., slow intensification but quick recovery), the onset time, peak intensity time, and termination time of HD within a year were significantly different between the two methods, due to the different drought conceptual backgrounds of the methods. (ii) The HD months identified by VDT were close to evenly distributed in each month of the year, while the HD months identified by FDT were mainly concentrated in the dry season. (iii) The onset, peak intensity, and termination time of HD identified by FDT were in good agreement with the dryness/wetness attributes of the two study basins and can be recommended in the study case. (iv) More methods for monitoring and predicting HD, and for revealing the driving mechanisms for HD propagation, are needed.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".