Contrasting Salinity Patterns and Spatiotemporal Groundwater Dynamics in Complex Endorheic Aquifer Systems: Insights from Chemical and Isotopic Tracers
Bibliographic record
Abstract
Understanding geochemical dynamics and salinity patterns in aquifer systems of endorheic basins is crucial for water resource management in arid and semi-arid climates. These environments, often characterized by intense agriculture and limited water availability, face significant challenges due to water scarcity and elevated groundwater salinity. This study investigates the geochemical processes shaping salinity patterns in interconnected shallow and deep aquifers within a structurally complex endorheic basin. A comprehensive dataset of groundwater samples from 213 wells across two aquifer systems in Bahira, central Morocco, was analysed for major ions and stable isotopes. Known for agriculture, Bahira faces notable issues of water scarcity and high groundwater salinity. The results highlight contrasting salinity levels, with the shallow aquifer exhibiting extreme salinity (EC up to 60,000 µS/cm) due to enhanced evaporation and soil leaching, whereas the deep aquifer maintains relatively lower EC values (500 to 3,000 μS/cm). Spatial analysis reveals a west-to-east salinity gradient driven by recharge variability and hydrogeological connectivity. Geochemical data underline the critical role of water-rock interactions, gypsum dissolution, and ion exchange in controlling salinity. Stable isotope analyses corroborate these findings, demonstrating evaporative enrichment and distinguishing between local recharge sources for the shallow aquifer and regional contributions from high-altitude precipitation in the deep aquifer. These insights enhance understanding of the hydrogeochemical dynamics in endorheic basins, emphasizing the interplay of climatic, geological, and anthropogenic factors in shaping groundwater quality. The findings offer broader implications for sustainable water management in similar arid environments worldwide.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".