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Record W4417515932 · doi:10.1016/j.gsd.2025.101566

Deep and fossil aquifers in Morocco: A multidisciplinary assessment of groundwater dynamics and salinization

2025· article· en· W4417515932 on OpenAlexaff
Ayoub Ayaou, Yassine Ait Brahim, Mohammed Hssaisoune, Meryem Miftah, Ahmed El-Azhari, Lhoussaine Bouchaou

Bibliographic record

VenueGroundwater for Sustainable Development · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geophysical Studies Worldwide
Canadian institutionsUniversité du Québec à Montréal
FundersFondation OCP
KeywordsGroundwater rechargeAquiferDepression-focused rechargeGroundwaterOverexploitationHydrology (agriculture)PopulationKarst

Abstract

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In Morocco, groundwater serves as a strategic water resource for drinking, domestic, agriculture, and industrial purposes due to the scarcity of surface water and high vulnerability to climate change. This dependency stems from the arid to semi-arid climate conditions where ecosystems depend on groundwater. Successive droughts, population growth, and economic development, particularly in agriculture, lead to the overexploitation of groundwater, especially shallow aquifers during the three last decades. This situation necessitates an investigation of these deep aquifers to manage this essential resource effectively. Hence, the aim of this study is to provide insights into deep aquifers, including their geometry, hydrodynamic, hydrochemical characteristics, recharge origins, geothermal potential, and residence times to determine which aquifers take longer to recharge and which should remain unexploited. The investigated deep aquifers exhibit a wide range of salinity, with electrical conductivity values ranging from 231 to 49040 μs/cm, whereas pH values range from 5.64 to 10.76. The high salinity is primarily attributed to Cl - , SO 4 2- , Na + and HCO 3 - with average concentrations of 829, 475, 463, and 316 mg/L, respectively. The recharge of the majority studied deep aquifers, particularly the Cretaceous ones (Souss, Essaouira, Ouarzazate, Tadla, Haouz, and Errachidia), is mainly from the High Atlas Mountains, except for the deep aquifers of Sahara where recharge is carried out during a specific context. The deep Jurassic aquifers, including the South Rifain Corridor, High and Middle Moulouya, High Plateaus, Horst Chain, Taourirt-Oujda Corridor, Beni-Bouyahi, and Bni-Znassen aquifers, are recharged from the Middle Atlas and North-Eastern Moroccan Mountains. Most investigated deep aquifers containing old waters are considered fossil or less renewable, with residence times exceeding 20 kiloannum before present (ka BP). The deep Sahara aquifers are the most significant groundwater potential reserves due to their lithologic composition, and their extensive area of approximately 90 000 km 2 , but with very low groundwater replenishment rates dating back over 46.5 ka BP according to radiocarbon dating. Because of their variable mineralisation related to their variable geological contexts and human activities, all the investigated systems show a high vulnerability mainly within their outcrops, which are mainly karstic or fissured areas. These deep aquifers require more stringent management and protection due to arid climate and very low precipitation through all the investigated regions. The results provide indicators about the functioning of the Moroccan deep aquifers and could guide decision-makers and policies for highlighting the need for careful management and conservation. • The deep Liassic and Turonian aquifers are the most important reservoirs in Morocco. • Many deep Moroccan aquifers are primarily recharged from the Atlas Mountains. • Most deep aquifers are fossil aquifers, with residence times exceeding 20,000 years. • Several deep aquifers are predominantly characterized by a Ca-Mg-HCO 3 and Na-Cl chemical composition.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.186

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.232
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations3
Published2025
Admission routes1
Has abstractyes

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