Study of the Hydrological Functioning of the Béjà river watershed, in the Northwest of Tunisia, using the SWAT model
Bibliographic record
Abstract
Water pollution from agricultural and human activities has become a hot issue that needs to be addressed. Its qualitative and quantitative evaluation has been well demonstrated by the SWAT model. The application of this model requires prior study of the hydrological catchment of interest and the calibration of a great number of intrinsic factors particular to the area under study. This model was mainly tested on several watersheds in the Nordic countries, including Canada and France. Its adaptation to the Mediterranean context is seldom recognized. In the case of Tunisia, the absence of a long series of continuous measurement data and hydrodynamic soil impede further application of the SWAT model. This work enabled the assessment of the performance of the hydrologic functions of the SWAT model and to adapt it to suit the context of a watershed characterized by the subhumid heavy soils of northern Tunisia, and to better understand the hydrological functioning of this basin. The need to initialize the model at least one month in advance of the desired time period was revealed. In addition, a calibration approach of the various parameters has been proposed by considering in particular the rainfall distribution during the period. In the calibration approach, the sensitivity analysis model showed the importance of some hydrodynamic parameters including the hydraulic conductivity at saturation, bulk density and cation exchange capacity, as well as the interactions of various phenomena related to the hydrological balance of the water at the final outlet, namely runoff, percolation and evapotranspiration .
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 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".