Spatiotemporal variability of streamflow under current and projected climate scenarios of Andit Tid watershed, central highland of Ethiopia
Bibliographic record
Abstract
This study examined the impact of climate change on streamflow in the Andit Tid watershed using climate models of dynamically downscaled Ethiopia’s CORDEX. The Arc SWAT and ArcGIS 10.5 software assessed the spatial and temporal distribution of streamflow, incorporating geospatial data like land use maps, digital elevation models, soil maps, and climate data. The SWAT model was calibrated and validated using SWAT-CUP with the SUFI-2 algorithm. The Canadian Centre for Climate Modeling and Analysis, Canada (CCCma (RCA4) model was selected for future projections after validation. From 1991 to 2021, the average streamflow rate was 0.0374 m 3 /s (247 mm), with R 2 values of 0.83 for calibration and 0.72 for validation. Hotspots with active gullies and slopes over 20% were identified mainly in cultivated lands. Future projections indicated a comparable streamflow rate to current conditions at 0.0322 m 3 /s (212.6 mm). A decline in streamflow is projected: 7.2% and 30.2% decreases in the near and far future under RCP 4.5, and 32.3% decreases and 5% increases under RCP 8.5 scenarios. These variations were attributed to differences in catchment characteristics and climate variability. Further research is needed to validate these findings by incorporating additional biophysical variables. This study provides insights into hydrological planning and management in the Andit Tid watershed and similar regions facing climate variability.
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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 | 0.001 | 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 | 0.000 | 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) | 0.000 | 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".