Rainwater harvesting for restoring degraded dry agro-pastoral ecosystems: a conceptual review of opportunities and constraints in a changing climate
Bibliographic record
Abstract
Water resources in dry environments are becoming scarcer, especially under the changing climate. In response, rainwater harvesting (RWH) is being reemphasized with calls to revive the practice. Ancient knowledge on RWH — mainly the collection through runoff, storage, and use of rainwater for various purposes — is still relevant, especially for dry environments. However, many old practices and technologies may not be suitable or feasible for the present and future. Little has been done to modernize and (or) develop new practices and technologies based on ancient indigenous knowledge. Modernizing old practices or developing new ones and using them in integrated rangelands restoration packages with enabling policy environment can unlock their potential in many water-scarce regions of the world. This paper reviews the state-of-the-art of micro-catchment rainwater harvesting (MIRWH) in dry environments and discusses the opportunities available and the major obstacles faced in using it to restore degraded agro-pastoral ecosystems and support their sustainability. The review highlights the knowledge behind it, the practices developed over the years, and their relevance to today and the future. The paper indicates areas of modernization that can make it more feasible for the future of the dry environments, especially their role in mitigating and adapting to climate change. Conventional and passive approaches to restoring/rehabilitating degraded dry agro-pastoral ecosystems are either too slow to show an obvious impact or not progressing satisfactorily. One main reason is that, because of land degradation, the majority of rain falling on such ecosystems and needed for revegetation is lost with little benefit being gained. Adopting a more progressive intervention to alter the processes of degradation and move towards new system equilibrium is required. MIRWH can enable a large portion of this otherwise lost rainwater to be stored in the soil, and, if used in an integrated packages including suitable plant species and sound grazing management, it may support meaningful vegetation growth and help system restoration. The Badia Benchmark project, implemented by ICARDA in Jordan and Syria, has demonstrated the potential for adoption at large scale in similar environments. This case study illustrates the potential and the constraints of this practice.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".