Modeling the impact of climate change on the hydrology of Andasa watershed
Bibliographic record
Abstract
Abstract This paper was aimed to study the impact of climate change on the hydrology of Andasa watershed for the period 2013–2099. The soil and water assessment tool (SWAT) was calibrated and validated, and thereby used to study the impact of climate change on the water balance. The future climate change scenarios were developed using future climate outputs from the Hadley Center Climate Model version 3 (HadCM3) A2 (high) and B2 (low) emission scenarios and Canadian Earth System Model version 2 (CanESM2) Representative concentration pathways (RCP) 4.5 and 8.5 scenarios. The large-scale maximum/minimum temperature and rainfall data were downscaled to fine-scale resolution using the Statistical Downscaling Model (SDSM). The mean monthly temperature projection of the four scenarios indicated an increase by a range of 0.4–8.5 °C while the mean monthly rainfall showed both a decrease of up to 97% and an increase of up to 109%. The long-term mean of all the scenarios indicated an increasing temperature and decreasing rainfall trends. Simulations showed that climate change may cause substantial impacts in the hydrology of the watershed by increasing the potential evapotranspiration (PET) by 4.4–17.3% and decreasing streamflow and soil water by 48.8–95.6% and 12.7–76.8%, respectively. The findings suggested that climate change may cause moisture-constrained environments in the watershed, which may impact agricultural activities in the watershed. Appropriate agricultural water management interventions should be implemented to mitigate and adapt to the plausible impacts of climate change by conserving soil moisture and reducing evapotranspiration.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".