Assessing the Potential Impacts of Four Climate Change Scenarios on the Discharge of the Simiyu River, Tanzania Using the SWAT Model
Bibliographic record
Abstract
The Soil and Water Assessment Tool (SWAT) was used to explore the potential impact of four climate change scenarios on discharge from the Simiyu River in Tanzania, located in the Lake Victoria watershed in Africa. The SWAT model used in this study was calibrated and verified by comparing model output with historic stream flow data for 1973-1976 as well as 1970-1971. SWAT was operated at daily and monthly time steps during both calibration and verification. For the daily-time step verification, the model had a Nash Sutcliffe coefficient of efficiency (NSE) of 0.52 and a correlation coefficient (R2) of 0.72. For the monthly time-step verification, the recorded NSE and R2 values were 0.66 and 0.70. In developing climate change scenarios within the general patterns defined by the Intergovernmental Panel on Climate Change, predicted increases in CO2 concentrations were implemented within the constraints of the model’s parameterisation by raising, in a seasonally-specific manner, the values of two proxy parameters: daily baseline temperature and precipitation. Under all scenarios, Simiyu River discharge increased (24-45%), showing the highest increase in the rainy season (March to May), with the greatest increase occurring during the rainy season (March to May). Discharge was influenced to a much greater degree by increases in precipitation rather than by temperature. The increase in river flow predicted by the model suggests that the potential increase in heavy flood damage during the rainy season will increase, which could, in turn, have significant adverse effects on infrastructure, human health, and the environment in the watershed. The SWAT predictions provide an important insight into the magnitude of stream flow changes that might occur in the Simiyu River in Tanzania as a result of future climatic change.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".