Climate change simulation and impacts on extreme events of rainfall and storm water in the Zayandeh Rud Catchment
Bibliographic record
Abstract
Nowadays, one of the most significant problems is that to recognize how the severity of heavy precipitation and floods may alter in future time in comparison with the current period. The purpose of this research is to understand the impact of future climate change on storm water and probability of maximum flood for future time period. Zayandeh rud river basin in Iran is selected as a case study. Forecast of future climatic parameters based on temperature and precipitation of the upcoming period (2006-2040) is completed with using the HadCM3 model and based on RCP 2.6, 4.5, and 8.5 emission patterns. Also, climate change model is downscaled statistically with applying LARS-WG. In the next step, the probable of maximum precipitation is measured through synoptic method and then, in order to model maximum storm water under the climate change effects, the HEC-HMS for simulating rainfall-runoff model is used. Also, the Snowmelt Runoff Model (SRM) is applied to model snow melting. The results of this research indicate the maximum of probable precipitation in the basin for the period of 2006-2040 under the scenario RCP 2.6, can rise by 5% and by the scenarios of RCP 4.5 and RCP 8.5 can decrease by 5% and 10%, respectively in comparison with the current period 1970-2005.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".