Future Surface Water Resources Sensitivity to Climate Changes Impacts
Bibliographic record
Abstract
Abstract — Due to water resources limitation in most parts of Iran, it is essential to give especial attention on evaluating and managing water resources. Climate changes would significantly affect water resources in future. In this study clime change impacts on water resources has been evaluated. "Karun " as the most watery river in Iran with an annual discharge of 4927.4 MCM at the site of Karun4 dam, is selected as case study. For this purpose 28 scenarios for precipitation and temperature, by using 11 models of AOGCM (Atmosphere-Ocean Global Circulation Model) models from CCCSN (Canadian Climate Change Scenario Network) are established and downloaded for next 90 years. Scenarios are downscaled for being usable for the study region. Evapotranspiration scenarios are generated by models which are provided for the case study region. The precipitation and temperature scenarios are used as input data by the mentioned models to generate future evapotranspiration scenarios. Multivariable empirical regression models based on 30 years monthly historical recorded data are generated to predict future monthly discharge scenarios. All of the models are tested with historical data. The precipitation, temperature, evapotranspiration and discharge scenarios are taken into account to estimate future surface water resources. The study shows that there would be a reduction of 17.20 % (38.63 mm/year) in precipitation and 31.51 % (58 m3/s) reduction in annual discharge by the end of 2100. Also annual temperature would have a raise about 22.65 % (3.82 ° C). River runoff would have 27.8 % reduction and would cause more than 25 % reduction in water surface resources.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".