A framework to assess future water-resource under climate change in northern Morocco using hydro-climatic modelling and water-withdrawal scenarios
Bibliographic record
Abstract
The Bas-Loukkos catchment, a Mediterranean catchment in northern Morocco exposed to growing water withdrawal caused mainly by agricultural development. For adaptation to climate change, water managers have to consider the high and various uncertainties. To assess impacts of climate change on projected water resources, this study aimed to develop a smart analysis framework to provide scientific information by exploring the complexity of many projections combined with hydrological models. Uncertainties were quantified using 13 pair-wise combinations of 5 regional climate models forced by 4 global climate models under two emissions scenarios (RCP4.5 and RCP8.5), data with and without bias correction (using empirical quantile mapping), and two sets of GR2M hydrological model parameters corresponding to different precipitation conditions. The Budyko hypothesis was used to analyse combined effects of climate change on water resources according to water-withdrawal scenarios. Climate and hydrological projections have been analyzed over three periods: short-term [2020–2040], medium-term [2041–2060] and long-term [2081–2100]. Results from all simulations indicate that, in the long term (2081–2100), precipitation and discharge will decrease by ca. 21–38% and ca. 50–71%, respectively, compared to the reference period (1981–2005). Consequently, this decline in water resources will require water management strategies to adapt to the future climatic conditions and water demand.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".