Analyses of the impact of climate change on water resources components, drought and wheat yield in semiarid regions: Karkheh River Basin in Iran
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Bibliographic record
Abstract
Abstract Water resources availability in the semiarid regions of Iran has experienced severe reduction because of increasing water use and lengthening of dry periods. To better manage this resource, we investigated the impact of climate change on water resources and wheat yield in the Karkheh River Basin (KRB) in the semiarid region of Iran. Future climate scenarios for 2020–2040 were generated from the Canadian Global Coupled Model for scenarios A1B, B1 and A2. We constructed a hydrological model of KRB using the Soil and Water Assessment Tool to project water resources availability. Blue and green water components were modeled with uncertainty ranges for both historic and future data. The Sequential Uncertainty Fitting Version 2 was used with parallel processing option to calibrate the model based on river discharge and wheat yield. Furthermore, a newly developed program called critical continuous day calculator was used to determine the frequency and length of critical periods for precipitation, maximum temperature and soil moisture. We found that in the northern part of KRB, freshwater availability will increase from 1716 to 2670 m 3 /capita/year despite an increase of 28% in the population in 2025 in the B1 scenario. In the southern part, where much of the agricultural lands are located, the freshwater availability will on the average decrease by 44%. The long‐term average irrigated wheat yield, however, will increase in the south by 1.2%–21% in different subbasins; but for rain‐fed wheat, this variation is from −4% to 38%. The results of critical continuous day calculator showed an increase of up to 25% in both frequency and length of dry periods in south Karkheh, whereas increasing flood events could be expected in the northern and western parts of the region. In general, there is variability in the impact of climate change in the region where some areas will experience net negative whereas other areas will experience a net positive impact. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it