MODELING THE IMPACT OF CLIMATE CHANGE IN A MEDITERRANEAN CATCHMENT (MERGUELLIL, TUNISIA)
Bibliographic record
Abstract
During the last decades, the Mediterranean region is suffering more and more from droughts. It has been recognized as one of the most vulnerable regions in the world to climate change. Understanding the impact of climate change on various components of water cycle is an important challenge for long-term sustainable management of water resources. In this paper, the integrated hydrological model “Soil and Water Assessment Tool” (SWAT 2005) was used to study the impact of future climate on water resources of a Mediterranean catchment. Future climate scenarios for periods of 2010-2039 and 2070-2099 were generated from the Canadian Global Coupled model (CGCM 3.1) for scenarios A1B, B1, and A2. These CGCMs data were then statistically downscaled to generate future possible local meteorological data of precipitation and temperature in the study area. SWAT model was run first under current climate (1986-2005) and then for the future climate period to analyze the potential impact of climate change on flow, evapotranspiration, and soil moisture across this catchment. Finally, Richter et al.’s Indicators of Hydrologic Alteration (IHA) were used to analyze the flow regime alterations under changing climate. The main results indicate that this catchment would suffer a combination of increased temperature and reduced rainfall that will reduce water resources in this area. Consequently, summer droughts would be intensified. Different spatial responses to climate change were observed in the catchment for near future simulations. Higher altitude regions would experience an increase of the total water yield, while a reduction is foreseen for lower parts. For far future, a noticeable decrease would affect water resources in all part of the catchment.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".