Expert views on strategies to increase water resilience: evidence from a global survey
Bibliographic record
Abstract
Scholars and policy-makers are advocating for increasing the resilience of water systems, both social and biophysical, to climate change impacts, and global environmental change more broadly. But what is "water resilience," and what does it imply for water resources management and water governance? Generally, water resilience may include ecological aspects of water quality or flood mitigation, engineered infrastructure to ensure safe and reliable water supply and to mitigate floods, and the socially inclusive and equitable governance of these systems. Following this, our goal was twofold: (1) explore and draw out a comprehensive set of water resource management strategies across sectors that are likely to contribute to increased resilience, and (2) investigate whether disciplinary divides are indeed a barrier toward convergence around key water resilience actions. To address these two gaps, we drew on a survey of experts in resilience and various aspects of water management and governance (n = 420), and aimed to synthesize their views on the specific strategies that can help enhance water resilience. Specifically, we surveyed experts across various water domains from ecosystem management to drought and flood management. Overall, we found that while debates about how to theorize or operationalize resilience in relation to different systems-social or biophysical-may be unresolved, there is considerable convergence among various experts about which actions are likely to make water systems more resilient to increasing risks and uncertainties. The most widely agreed upon strategies for building water resilience revolve around improved ecosystem health, integration across scales, and adaptation to change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".