Aridification impacts and adaptation strategies in the world’s drylands: a review
Bibliographic record
Abstract
Global aridity is increasing due to anthropogenic warming, which raises atmospheric evaporative demand faster than precipitation. As a result, more land and populations are exposed to arid conditions, a trend projected to intensify in the coming decades. Aridity is a long-term climatological condition that affects large regions worldwide. Unlike short-term phenomena such as droughts, aridity leads to gradual but increasingly detrimental impacts on biophysical and socio-economic systems. Over time it can transform landscapes into desert-like environments, hindering the development of life and compromising the well-being of societies. The impacts of aridity are complex and diverse, and it is especially challenging to assess its effects on socioeconomic systems. Yet no global review has systematically examined the impacts of aridity, the factors driving societal vulnerability, and the adaptation measures being implemented. This paper provides the first comprehensive synthesis of these dimensions, thereby filling a critical gap in the literature. Our review shows that while references to biophysical impacts are increasing, explicit studies on socio-economic consequences—such as those on food production, poverty, health, and migration—remain scarce. These are often inferred indirectly from related concepts like drought or desertification. By systematically integrating this dispersed evidence, our article highlights both natural and socio-economic impacts, identifies key knowledge gaps, and outlines priority areas for future research. Such knowledge is essential for improving mitigation and adaptation strategies that strengthen resilience in increasingly arid regions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".