Perceptions of climate change adaptation barriers in environmental water management
Bibliographic record
Abstract
Climate change is irreversibly changing the water cycle, yet existing environmental flow assessment methods often fail to recognize the non-stationarity of hydro-climatic systems. Failure to do so will lead to the inability of environmental water management to achieve its ecological targets. Australia has undergone major reform over the past 12 years to recover water from consumptive use for environmental benefit and this paper examines how government agencies responsible for planning and delivery of environmental water establish ecological objectives, and whether there are any barriers to including climate adaptations. We used semi-structured interviews and an online survey of environmental water staff throughout Australia, focusing on southeast Australia, to gather information on methods and perceptions regarding these key issues. The results show water managers perceive current ecological objectives as unachievable and are frustrated by using outdated, government-recommended flow assessment methods. There are many general and industry-specific barriers to climate adaptation that are not insurmountable, yet the current lack of legislative and policy guidance provides little assistance on the best way to respond. We conclude that environmental water planning needs to more formally incorporate climate change considerations along with modelling approaches that can evaluate outcomes under a range of possible future hydro-climatic scenarios to ensure proactive decision making can occur. As the industry currently exists in Australia, it is ill prepared for the challenge of meeting legislated ecological targets under future climates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".