Assessing future drought conditions under a changing climate: a case study of the Lake Urmia basin in Iran
Bibliographic record
Abstract
Abstract This study was conducted to assess the impacts of climate change on drought over the Lake Urmia basin, Iran. Drought events for 2011–2040, 2041–2070, and 2071–2100 were analyzed based on the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) and were compared with the adopted baseline period (1976–2005). The SPI and SPEI were calculated using the precipitation and temperatures obtained from the second-generation Canadian Earth System Model (CanESM2) under Representative Concentration Pathway (RCP) 2.6 and RCP 8.5 as optimistic and pessimistic scenarios respectively. The results of SPI analyses revealed that under RCP 2.6 the frequency of droughts is almost constant while under RCP 8.5 drought frequency increased especially in the period 2071–2100. The calculated SEPI under both scenarios and during all future periods predict that the frequency and duration of droughts will increase. Generally, the difference between the SPI and SPEI is related to the input to each index. SPI is solely based on precipitation while the SPEI accounts for both precipitation and potential evapotranspiration (PET). Under global warming and changing climate, the significant role of PET was highlighted. It was concluded that the SPEI outperformed the SPI for drought studies under a changing climate.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".