Investigating the hydrological implications of atmospheric fluctuations and wildfire disturbances in a mountainous terrain
Bibliographic record
Abstract
This research investigates the hydrological effects of climate change on the Amu Darya river basin with respect to uncertainties, runoff fluctuations, and altered flow regimes. Applying the Soil and Water Assessment Tool (SWAT), a semi-distributed hydrological model, we explored climate-driven changes by simulating hydrographs and analysing water balance. Global Climate Models for several Representative Concentration Pathway (RCP) scenarios were used to simulate future climate conditions, which were incorporated into SWAT for impact analysis. Model calibration and validation were done using SWAT Calibration and Uncertainty Programme (SUFI-2 algorithm) with observed stream flow data. Findings show substantial changes in water availability, seasonal discharge patterns, and extreme flow events, underpinned by increasing temperatures and changed precipitation regimes. Higher uncertainty in climate predictions has a profound impact on hydrological responses, emphasising the importance of probabilistic methods in water resource planning. Findings highlight the importance of adaptive water management strategies, such as integrated watershed management and transboundary coordination, to counteract the impacts of climate change. This research shows the capability of SWAT in describing hydrological responses and uncertainties, and it is of great value to policymakers and stakeholders in sustainable water resource planning for the Amu Darya river basin.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".