Assessing the effect of climate and land use changes on the hydrologic regimes in the upstream of Tajan river basin using SWAT model
Bibliographic record
Abstract
Abstract Climate change is the most important challenge in achieving sustainable development. Semi-arid and arid areas (such as Iran) are particularly susceptible to the effects of climate change on water supply. In this research, the effect of climate change and upstream land use is investigated on Tajan, a river in the north of Iran. The data regarding the climate were produced via second-generation Canadian Earth System Model (CanESM2) and adopted as the input to SWAT hydrologic model under RCP2.6 and RCP8.5 for the period of 2016–2066. The results showed that the peak streamflow will increase by 4% and 5.7% and the average annual discharges will decrease by 16% and 16.5% from 2016 to 2066 for RCP2.6 and RCP8.5 scenarios, respectively. Besides, the effect of different land use change scenarios on streamflow was investigated under four diverse scenarios selected to represent a comprehensive range of possible land use map of the basin. Land use change scenarios led to 8.5–15.8% increase in the average annual streamflow, highlighting the fact that it is less effective than climate change on streamflow. It could be concluded that downstream water users in the basin should adopt strategies to cope with water-stressed condition under the changing climate.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".