Linking Land Use Change and Hydrological Responses: The Role of Agriculture in the Decline of Urmia Lake
Bibliographic record
Abstract
The water level and surface area of Urmia Lake, located in the northwest of Iran, has decreased dramatically, presenting significant challenges for hydrological modeling due to complex interactions between surface and groundwater. In this study, the impact of agricultural activities on streamflow within one of the largest sub-basins of Urmia Lake is assessed using the Soil and Water Assessment Tool (SWAT) for hydrological assessments. To have accurate assessments, land use change detections were considered by a novel method, which merges the Normalized Difference Vegetation Index (NDVI) with the Digital Elevation Model (DEM) to create a two-band NDVI-DEM image, effectively differentiating between agricultural and rangeland fields. Our findings reveal that agricultural development and irrigation, escalating between 1977 and 2015, resulted in increased annual evapotranspiration (ET) (ranging from 295 mm to 308 mm) and a decrease in yearly streamflow, from 317 million cubic meters to 300 million cubic meters. Overall, our study highlights the significant role that agricultural development and irrigation may play in contributing to the shrinking of Lake Urmia, underscoring the need for improved regional water management strategies to address these challenges, though further analysis across additional basins would be necessary for broader conclusions.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".