Assessing the impact of climate change on water resources in Iran
Bibliographic record
Abstract
As water resources become further stressed due to increasing levels of societal demand, understanding the effect of climate change on various components of the water cycle is of strategic importance in management of this essential resource. In this study, we used a hydrologic model of Iran to study the impact of future climate on the country's water resources. The hydrologic model was created using the Soil and Water Assessment Tool (SWAT) model and calibrated for the period from 1980 to 2002 using daily river discharges and annual wheat yield data at a subbasin level. Future climate scenarios for periods of 2010–2040 and 2070–2100 were generated from the Canadian Global Coupled Model (CGCM 3.1) for scenarios A1B, B1, and A2, which were downscaled for 37 climate stations across the country. The hydrologic model was then applied to these periods to analyze the effect of future climate on precipitation, blue water, green water, and yield of wheat across the country. For future scenarios we found that in general, wet regions of the country will receive more rainfall while dry regions will receive less. Analysis of daily rainfall intensities indicated more frequent and larger‐intensity floods in the wet regions and more prolonged droughts in the dry regions. When aggregated to provincial levels, the differences in the predictions due to the three future scenarios were smaller than the uncertainty in the hydrologic model. However, at the subbasin level the three climate scenarios produced quite different results in the dry regions of the country, although the results in the wet regions were more or less similar.
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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 | 0.001 | 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".