Uncertainties in runoff projection and hydrological drought assessment over Gharesu basin under CMIP5 RCP scenarios
Bibliographic record
Abstract
Abstract Hydrological drought plays an important role in planning and managing water resources systems to meet increasing water demands due to population growth. In this study, the effects of climate change on the hydrological drought characteristics of the Gharasu basin, as one of the major sub-basins of the Karkheh river basin, are investigated. This river basin has experienced severe droughts, and floods, in recent years. The uncertainties in projected drought conditions are characterized based on a suite of 34 general circulation models (GCMs). Based on hydrological simulations over the historical period, 12 GCMs are selected to estimate projected runoff values and the corresponding streamflow drought index (SDI) in the future period. The ‘run theory’ is applied to evaluate the drought characteristics under Representative Concentration Pathways (RCPs) 4.5 and 8.5 emission scenarios. Results show that uncertainties of drought projection under RCP8.5 are higher than under RCP4.5, where among different drought characteristics, the maximum uncertainty is detected for drought severity and maximum drought duration. Moreover, the uncertainty of drought projection in wet periods is greater than that in dry periods.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".