Flexible water allocations and rotational delivery combined adapt irrigation systems to drought
Bibliographic record
Abstract
Self-governing irrigation systems are integral to global food security and face serious problems under climate change.This is particularly true in areas expected to become more arid such as the Southwestern United States, where restrictive water rights are strictly enforced.Adaptations to these dual climatic and legal challenges include user-selected rules.In particular, during water shortage self-governing irrigation systems often change water allocations between members and rotate water delivery.However, it is unclear how these rules interact with each other as configurations and with contextual factors, such as the degree of water scarcity.It is also unclear how these rules influence outcomes between irrigators closer to the water source and those farther from it.How might different configurations of rules interact with water availability to produce outcomes along an irrigation system's canal network?This study addresses this question by exploiting a natural experiment in water distribution and allocation rules during shortage among a stratified sample of 60 snowmelt dependent irrigation systems in the San Luis Valley of Colorado during a four-year drought period from 2011-2014.A key finding is that the combination of rotational delivery and flexible water allocations produces the most equal crop growth between irrigators at the head and tail of the irrigation system at all levels of water availability.The marginal productivity of water at the head and tail end of irrigation systems at all levels of water availability is also equalized under this configuration.These results suggest a greater likelihood of ongoing collective action, important for climate change adaptation.However, rotation with flexible allocations is outperformed by other configurations depending on context.These findings highlight the configurational relationships between rules, further illustrate interactions between rules and physical context, and caution against panaceas in water resource management and climate change adaptation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".