A Probabilistic Risk Assessment of the National Economic Impacts of Regulatory Drought Management on Irrigated Agriculture
Bibliographic record
Abstract
Abstract Drought frequency and intensity is expected to increase in many regions worldwide, and water shortages could become more extreme, even in humid temperate climates. To protect the environment and secure water supplies, water abstractions for irrigation can be mandatorily reduced by environmental regulators. Such abstraction restrictions can result in economic impacts on irrigated agriculture. This study provides a novel approach for the probabilistic risk assessment of potential future economic losses in irrigated agriculture arising from the interaction of climate change and regulatory drought management, with an application to England and Wales. Hydrometeorological variability is considered within a synthetic data set of daily rainfall and river flows for a baseline period (1977–2004) and for projections for near future (2022–2049) and far future (2072–2099). The probability, magnitude, and timing of abstraction restrictions are derived by applying rainfall and river flow triggers in 129 catchments. The risk of economic losses at the catchment level is then obtained from the occurrences of abstraction restrictions combined with spatially distributed crop‐specific economic losses. Results show that restrictions will become more severe, more frequent, and longer in the future. The highest economic risks are projected where drought‐sensitive crops with a high financial value are concentrated in catchments with increasingly uncertain water supply. This research highlights the significant economic losses associated with mandatory drought restrictions experienced by the agricultural sector and supports the need for environmental regulators and irrigators to collaboratively manage scarce water resources to balance environmental and economic considerations.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".