Implications of Land Use and Climate Change on Water Balance Components in the Sigi Catchment, Tanzania
Bibliographic record
Abstract
Whereas normal water flow in a catchment is necessary for all forms of life, the hydrologic systems in the Sigi catchment is susceptible to land use and climate changes. Over the past three decades, water balance of the Sigi Catchment has indicated changes with unknown forms and magnitudes. To uncover the dynamics, this study used SWAT model to simulate water balance to a separate and combined impact of land-use and climate change. SWAT simulation showed good performance with NSE=0.58 and R2=0.67 for calibration, and NSE=0.56 and R2=0.64 for validation periods. Land use change scenarios indicated increase in surface runoff by 16.1mm, while base flow and water yield decreased by 23.1mm and 7.2mm, respectively. Climate change scenarios indicated an increase in surface runoff by 29.9mm, while base flow and water yield decreased by 36.1mm and 14.2mm, respectively. The combined land use and climate change scenarios indicated increase of surface runoff by 19.0mm, and decrease in base flow and water yield by 29.7mm and 10.7mm, respectively. From the study, it is clearly that the impacts of climate change on water balance components of the Sigi catchment are larger than land use change. Owing to the dilemma facing water resources in this era of climate change, long-term planning that balance households’ livelihood options and water resources management option is needed.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".