Global advances in managed aquifer recharge: a systematic synthesis
Bibliographic record
Abstract
The rising water crisis requires effective and innovative techniques to safeguard the dwindling groundwater resources. Managed aquifer recharge (MAR) is a groundwater management approach to minimize groundwater depletion and ensure water security across a range of climatic regions. It is implemented globally to maximize aquifer storage, reduce saltwater intrusion, prevent land subsidence, reduce flooding hazards, and enhance agricultural production, while ensuring better water quality and quantity. This synthesis followed the PRISMA 2020 guidelines to systematically select relevant publications from scientific databases between 1980 and 2023. Systematic review and bibliometric synthesis indicate that river water (surface water runoff to streams and rivers), stormwater, and reclaimed wastewater were the main water sources for MAR. Tracers, water balance, and numerical modeling methods were commonly used approaches for evaluating MAR systems. Global studies on MAR reveal a significant relationship with the aridity index, indicating that highly arid areas experience negligible recharge due to the limited availability of excess water for recharge. These regions are prone to groundwater depletion but hold potential for MAR using sources such as treated wastewater, stormwater, and desalinated water. MAR projects have been established worldwide for agricultural and domestic needs at different regional scales. MAR not only increases groundwater storage but also has global benefits, including water security, environmental enhancement, economic benefits, technological advancement, climate change mitigation, and community engagement. The challenges of MAR include financial, technical, and hydrogeological issues, social acceptance, ecological concerns, and climate change. Our systematic synthesis highlights the advantages of MAR in addressing the pressing issue of water scarcity for agriculture and domestic use. This underscores the importance of an integrated, well-informed, and region-specific strategy and policies to fully harness MAR's capabilities in guiding the world toward a sustainable future.
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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 | 0.016 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.036 | 0.029 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".